diff --git a/ai/.gitignore b/ai/.gitignore new file mode 100644 index 0000000..0ebbe3c --- /dev/null +++ b/ai/.gitignore @@ -0,0 +1,53 @@ +# Python +__pycache__/ +*.py[cod] +*$py.class +*.so +.Python +env/ +venv/ +ENV/ +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +*.egg-info/ +.installed.cfg +*.egg + +# Models and Data +models/ +*.onnx +*.h5 +*.pb +*.pkl +*.csv +*.npy + +# TensorFlow +*.ckpt +checkpoints/ +logs/ + +# IDE +.vscode/ +.idea/ +*.swp +*.swo +*~ + +# OS +.DS_Store +Thumbs.db + +# Jupyter +.ipynb_checkpoints/ +*.ipynb diff --git a/ai/ONNX_EA.mq5 b/ai/ONNX_EA.mq5 new file mode 100644 index 0000000..a557eb1 --- /dev/null +++ b/ai/ONNX_EA.mq5 @@ -0,0 +1,406 @@ +//+------------------------------------------------------------------+ +//| ONNX_EA.mq5 | +//| Copyright 2025, MetaQuotes Ltd. | +//| https://www.mql5.com | +//+------------------------------------------------------------------+ +#property copyright "Copyright 2025, MetaQuotes Ltd." +#property link "https://www.mql5.com" +#property version "1.00" +#property description "Expert Advisor using ONNX model for price prediction" +#property description "Based on: https://www.mql5.com/en/docs/onnx/onnx_prepare" + +#include + +//--- Input parameters +input group "ONNX Model Settings" +input string InpModelPath = "models\\XAUUSD_H1_model.onnx"; // ONNX Model Path +input int InpLookback = 60; // Lookback Period (bars) +input bool InpUsePrediction = true; // Use Model Prediction + +input group "Trading Settings" +input double InpLotSize = 0.01; // Lot Size +input int InpMagicNumber = 123456; // Magic Number +input int InpSlippage = 3; // Slippage (points) +input int InpStopLoss = 50; // Stop Loss (pips) +input int InpTakeProfit = 100; // Take Profit (pips) + +input group "Prediction Settings" +input double InpPredictionThreshold = 0.0001; // Min Prediction Change (0.01%) +input bool InpUseConfidence = true; // Use Confidence Filter +input double InpMinConfidence = 0.6; // Minimum Confidence + +//--- Global variables +CTrade trade; +long onnx_handle = INVALID_HANDLE; +datetime last_bar_time = 0; +double last_prediction = 0.0; +double last_confidence = 0.0; + +//+------------------------------------------------------------------+ +//| Expert initialization function | +//+------------------------------------------------------------------+ +int OnInit() +{ + // Set trade parameters + trade.SetExpertMagicNumber(InpMagicNumber); + trade.SetDeviationInPoints(InpSlippage); + trade.SetTypeFilling(ORDER_FILLING_FOK); + + // Load ONNX model + string model_path = InpModelPath; + + // Convert relative path to full path + if(StringFind(model_path, "\\") == 0 || StringFind(model_path, "/") == 0) + { + // Already absolute path + } + else + { + // Relative path - prepend terminal data folder + model_path = TerminalInfoString(TERMINAL_DATA_PATH) + "\\MQL5\\Files\\" + model_path; + } + + // Replace forward slashes with backslashes for Windows + StringReplace(model_path, "/", "\\"); + + Print("Loading ONNX model from: ", model_path); + + onnx_handle = OnnxCreate(model_path, ONNX_DEFAULT); + + if(onnx_handle == INVALID_HANDLE) + { + Print("ERROR: Failed to load ONNX model. Error: ", GetLastError()); + Print("Make sure the model file exists at: ", model_path); + return(INIT_FAILED); + } + + // Get model info + int input_count = OnnxGetInputCount(onnx_handle); + int output_count = OnnxGetOutputCount(onnx_handle); + + Print("ONNX Model loaded successfully"); + Print(" Inputs: ", input_count); + Print(" Outputs: ", output_count); + + if(input_count > 0) + { + string input_name = OnnxGetInputName(onnx_handle, 0); + Print(" Input name: ", input_name); + } + + if(output_count > 0) + { + string output_name = OnnxGetOutputName(onnx_handle, 0); + Print(" Output name: ", output_name); + } + + return(INIT_SUCCEEDED); +} + +//+------------------------------------------------------------------+ +//| Expert deinitialization function | +//+------------------------------------------------------------------+ +void OnDeinit(const int reason) +{ + // Release ONNX model + if(onnx_handle != INVALID_HANDLE) + { + OnnxRelease(onnx_handle); + Print("ONNX model released"); + } +} + +//+------------------------------------------------------------------+ +//| Expert tick function | +//+------------------------------------------------------------------+ +void OnTick() +{ + // Check if new bar + datetime current_bar_time = iTime(_Symbol, PERIOD_CURRENT, 0); + if(current_bar_time == last_bar_time) + { + return; // Still the same bar + } + last_bar_time = current_bar_time; + + // Check if we should use prediction + if(!InpUsePrediction) + { + return; + } + + // Prepare input data + float input_data[]; + if(!PrepareInputData(input_data)) + { + Print("ERROR: Failed to prepare input data"); + return; + } + + // Run ONNX model + float output_data[]; + if(!RunONNXModel(input_data, output_data)) + { + Print("ERROR: Failed to run ONNX model"); + return; + } + + // Get prediction + if(ArraySize(output_data) == 0) + { + Print("ERROR: Empty output from ONNX model"); + return; + } + + double predicted_price = output_data[0]; + double current_price = SymbolInfoDouble(_Symbol, SYMBOL_BID); + + // Calculate prediction change + double price_change = predicted_price - current_price; + double price_change_pct = (price_change / current_price) * 100.0; + + // Calculate confidence (simple heuristic based on prediction magnitude) + double confidence = MathAbs(price_change_pct) / 1.0; // Normalize + if(confidence > 1.0) confidence = 1.0; + + last_prediction = predicted_price; + last_confidence = confidence; + + // Log prediction + Print("Prediction: Current=", current_price, + " Predicted=", predicted_price, + " Change=", price_change_pct, "%", + " Confidence=", confidence); + + // Check if we should trade + if(!InpUseConfidence || confidence >= InpMinConfidence) + { + // Check if prediction is significant + if(MathAbs(price_change_pct) >= InpPredictionThreshold) + { + // Check existing position + if(PositionSelect(_Symbol)) + { + // Manage existing position + ManagePosition(predicted_price, price_change_pct); + } + else + { + // Open new position based on prediction + if(price_change_pct > InpPredictionThreshold) + { + OpenBuyPosition(); + } + else if(price_change_pct < -InpPredictionThreshold) + { + OpenSellPosition(); + } + } + } + } +} + +//+------------------------------------------------------------------+ +//| Prepare input data for ONNX model | +//+------------------------------------------------------------------+ +bool PrepareInputData(float &input_array[]) +{ + // We need to prepare data similar to training + // This is a simplified version - you may need to adjust based on your model + + int lookback = InpLookback; + int features = 12; // Adjust based on your model (OHLC + volume + indicators) + + ArrayResize(input_array, lookback * features); + ArrayInitialize(input_array, 0.0); + + // Get historical data + double open[], high[], low[], close[], volume[]; + ArraySetAsSeries(open, true); + ArraySetAsSeries(high, true); + ArraySetAsSeries(low, true); + ArraySetAsSeries(close, true); + ArraySetAsSeries(volume, true); + + if(CopyOpen(_Symbol, PERIOD_CURRENT, 0, lookback + 50, open) < lookback) + return false; + if(CopyHigh(_Symbol, PERIOD_CURRENT, 0, lookback + 50, high) < lookback) + return false; + if(CopyLow(_Symbol, PERIOD_CURRENT, 0, lookback + 50, low) < lookback) + return false; + if(CopyClose(_Symbol, PERIOD_CURRENT, 0, lookback + 50, close) < lookback) + return false; + if(CopyTickVolume(_Symbol, PERIOD_CURRENT, 0, lookback + 50, volume) < lookback) + return false; + + // Calculate indicators (simplified - you may need to match training exactly) + double rsi[], ema20[], ema50[], atr[]; + ArraySetAsSeries(rsi, true); + ArraySetAsSeries(ema20, true); + ArraySetAsSeries(ema50, true); + ArraySetAsSeries(atr, true); + + // Calculate RSI + int rsi_handle = iRSI(_Symbol, PERIOD_CURRENT, 14, PRICE_CLOSE); + if(rsi_handle == INVALID_HANDLE) return false; + if(CopyBuffer(rsi_handle, 0, 0, lookback + 50, rsi) < lookback) + { + IndicatorRelease(rsi_handle); + return false; + } + IndicatorRelease(rsi_handle); + + // Calculate EMAs + int ema20_handle = iMA(_Symbol, PERIOD_CURRENT, 20, 0, MODE_EMA, PRICE_CLOSE); + int ema50_handle = iMA(_Symbol, PERIOD_CURRENT, 50, 0, MODE_EMA, PRICE_CLOSE); + if(ema20_handle == INVALID_HANDLE || ema50_handle == INVALID_HANDLE) return false; + + if(CopyBuffer(ema20_handle, 0, 0, lookback + 50, ema20) < lookback || + CopyBuffer(ema50_handle, 0, 0, lookback + 50, ema50) < lookback) + { + IndicatorRelease(ema20_handle); + IndicatorRelease(ema50_handle); + return false; + } + IndicatorRelease(ema20_handle); + IndicatorRelease(ema50_handle); + + // Calculate ATR + int atr_handle = iATR(_Symbol, PERIOD_CURRENT, 14); + if(atr_handle == INVALID_HANDLE) return false; + if(CopyBuffer(atr_handle, 0, 0, lookback + 50, atr) < lookback) + { + IndicatorRelease(atr_handle); + return false; + } + IndicatorRelease(atr_handle); + + // Normalize and prepare data (simplified normalization) + // IMPORTANT: This uses simplified normalization. For best results, you should: + // 1. Save the scaler during training (train_onnx_model.py does this automatically) + // 2. Implement the same normalization logic in MQL5, OR + // 3. Pre-normalize data in Python and pass to MQL5 via files/global variables + // The current implementation may not match training exactly, which can affect accuracy + int idx = 0; + for(int i = 0; i < lookback; i++) + { + // Normalize features (simplified - use proper scaler in production) + input_array[idx++] = (float)((open[i] - close[lookback-1]) / close[lookback-1]); + input_array[idx++] = (float)((high[i] - close[lookback-1]) / close[lookback-1]); + input_array[idx++] = (float)((low[i] - close[lookback-1]) / close[lookback-1]); + input_array[idx++] = (float)((close[i] - close[lookback-1]) / close[lookback-1]); + input_array[idx++] = (float)(volume[i] / 1000000.0); // Normalize volume + input_array[idx++] = (float)(rsi[i] / 100.0); // Normalize RSI + input_array[idx++] = (float)((ema20[i] - close[lookback-1]) / close[lookback-1]); + input_array[idx++] = (float)((ema50[i] - close[lookback-1]) / close[lookback-1]); + input_array[idx++] = (float)(atr[i] / close[lookback-1]); + input_array[idx++] = (float)((close[i] - close[i+1]) / close[i+1]); // Price change + input_array[idx++] = (float)(high[i] / low[i]); // High/low ratio + input_array[idx++] = (float)(volume[i] / (volume[i] + volume[i+1] + volume[i+2]) / 3.0); // Volume ratio + } + + // Reshape for model: (1, lookback, features) + // ONNX expects shape [1, lookback, features] + float reshaped[]; + ArrayResize(reshaped, 1 * lookback * features); + ArrayCopy(reshaped, input_array); + + ArrayCopy(input_array, reshaped); + + return true; +} + +//+------------------------------------------------------------------+ +//| Run ONNX model | +//+------------------------------------------------------------------+ +bool RunONNXModel(float &input_data[], float &output_data[]) +{ + if(onnx_handle == INVALID_HANDLE) + return false; + + // Set input shape + long input_shape[] = {1, InpLookback, 12}; // Adjust based on your model + if(!OnnxSetInputShape(onnx_handle, 0, input_shape)) + { + Print("ERROR: Failed to set input shape. Error: ", GetLastError()); + return false; + } + + // Run model + if(!OnnxRun(onnx_handle, ONNX_NO_CONVERSION, input_data, output_data)) + { + Print("ERROR: Failed to run ONNX model. Error: ", GetLastError()); + return false; + } + + return true; +} + +//+------------------------------------------------------------------+ +//| Open buy position | +//+------------------------------------------------------------------+ +void OpenBuyPosition() +{ + double price = SymbolInfoDouble(_Symbol, SYMBOL_ASK); + double sl = InpStopLoss > 0 ? price - InpStopLoss * _Point * 10 : 0; + double tp = InpTakeProfit > 0 ? price + InpTakeProfit * _Point * 10 : 0; + + if(trade.Buy(InpLotSize, _Symbol, price, sl, tp, "ONNX Buy Signal")) + { + Print("Buy order opened. Ticket: ", trade.ResultOrder()); + } + else + { + Print("Failed to open buy order. Error: ", trade.ResultRetcodeDescription()); + } +} + +//+------------------------------------------------------------------+ +//| Open sell position | +//+------------------------------------------------------------------+ +void OpenSellPosition() +{ + double price = SymbolInfoDouble(_Symbol, SYMBOL_BID); + double sl = InpStopLoss > 0 ? price + InpStopLoss * _Point * 10 : 0; + double tp = InpTakeProfit > 0 ? price - InpTakeProfit * _Point * 10 : 0; + + if(trade.Sell(InpLotSize, _Symbol, price, sl, tp, "ONNX Sell Signal")) + { + Print("Sell order opened. Ticket: ", trade.ResultOrder()); + } + else + { + Print("Failed to open sell order. Error: ", trade.ResultRetcodeDescription()); + } +} + +//+------------------------------------------------------------------+ +//| Manage existing position | +//+------------------------------------------------------------------+ +void ManagePosition(double predicted_price, double price_change_pct) +{ + if(!PositionSelect(_Symbol)) + return; + + long position_type = PositionGetInteger(POSITION_TYPE); + double position_open_price = PositionGetDouble(POSITION_PRICE_OPEN); + double current_profit = PositionGetDouble(POSITION_PROFIT); + + // Simple management: close if prediction reverses + if(position_type == POSITION_TYPE_BUY && price_change_pct < -InpPredictionThreshold) + { + // Prediction turned bearish, close long + if(trade.PositionClose(_Symbol)) + { + Print("Closed long position due to bearish prediction"); + } + } + else if(position_type == POSITION_TYPE_SELL && price_change_pct > InpPredictionThreshold) + { + // Prediction turned bullish, close short + if(trade.PositionClose(_Symbol)) + { + Print("Closed short position due to bullish prediction"); + } + } +} diff --git a/ai/QUICKSTART.md b/ai/QUICKSTART.md new file mode 100644 index 0000000..1be98d0 --- /dev/null +++ b/ai/QUICKSTART.md @@ -0,0 +1,197 @@ +# Quick Start Guide - ONNX with MetaTrader 5 + +Get started with ONNX machine learning models in MT5 in 5 minutes! + +## Prerequisites + +1. **MetaTrader 5** installed and running +2. **Python 3.8+** installed +3. **MT5 account** (demo or live) with access to historical data + +## Step 1: Install Dependencies + +```bash +cd ai +pip install -r requirements.txt +``` + +This installs: +- TensorFlow/Keras for model training +- ONNX runtime for model inference +- MetaTrader5 Python module +- Other required libraries + +## Step 2: Verify MT5 Connection + +Make sure MT5 is running and you're logged in. The scripts will automatically connect to MT5. + +## Step 3: Train Your First Model + +Train a price prediction model for Gold (XAUUSD): + +```bash +python train_onnx_model.py --symbol XAUUSD --timeframe H1 --epochs 30 +``` + +This will: +- Download 2 years of historical data +- Train an LSTM neural network +- Save the model as `models/XAUUSD_H1_model.onnx` + +**Expected time**: 5-15 minutes depending on your hardware. + +## Step 4: Test the Model + +Make a prediction with your trained model: + +```bash +python predict_with_onnx.py --model models/XAUUSD_H1_model.onnx --symbol XAUUSD +``` + +You should see output like: +``` +Current XAUUSD price: 2650.12345 +Making 1 prediction(s)... + +Predicted next price: 2652.54321 +Expected change: 2.41976 (0.09%) +``` + +## Step 5: Use in MetaTrader 5 + +### Option A: Copy Model to MT5 + +1. Copy your ONNX model to MT5's Files folder: + ``` + \MQL5\Files\models\XAUUSD_H1_model.onnx + ``` + + Default locations: + - Windows: `C:\Users\\AppData\Roaming\MetaQuotes\Terminal\\MQL5\Files\` + - Or find it: MT5 → File → Open Data Folder → MQL5 → Files + +2. Open `ONNX_EA.mq5` in MetaEditor + +3. Compile (F7) + +4. Attach to chart: + - Model path: `models\XAUUSD_H1_model.onnx` + - Lookback: `60` (must match training) + - Set your trading parameters + +### Option B: Run from MetaEditor + +If you have Python integration enabled in MetaEditor: + +1. Open `train_onnx_model.py` in MetaEditor +2. Press F7 (Compile) to run +3. The model will be saved to the project folder + +## Common Commands + +### Train for Different Symbols + +```bash +# EUR/USD on 15-minute charts +python train_onnx_model.py --symbol EURUSD --timeframe M15 + +# Bitcoin on 4-hour charts +python train_onnx_model.py --symbol BTCUSD --timeframe H4 +``` + +### Custom Training Parameters + +```bash +python train_onnx_model.py \ + --symbol XAUUSD \ + --timeframe H1 \ + --lookback 100 \ + --epochs 100 \ + --batch-size 64 +``` + +### Multiple Predictions + +```bash +python predict_with_onnx.py \ + --model models/XAUUSD_H1_model.onnx \ + --symbol XAUUSD \ + --predictions 5 +``` + +## Troubleshooting + +### "MT5 initialization failed" +- ✅ Make sure MT5 is running +- ✅ Log into your account in MT5 +- ✅ Check that the symbol exists (e.g., XAUUSD, not GOLD) + +### "No data available" +- ✅ Ensure you have historical data downloaded in MT5 +- ✅ Check the date range (script uses last 2 years) +- ✅ Verify symbol name is correct + +### "Failed to load ONNX model" in EA +- ✅ Check the file path is correct +- ✅ Ensure model is in MT5's Files folder +- ✅ Verify the model file exists and is not corrupted + +### Model predictions seem wrong +- ✅ Ensure `InpLookback` in EA matches training `--lookback` +- ✅ Check that you're using the same symbol/timeframe +- ✅ Verify feature normalization matches training + +## Next Steps + +1. **Experiment with different models**: + - Try different lookback periods + - Adjust network architecture + - Add more features + +2. **Optimize trading parameters**: + - Test different prediction thresholds + - Tune stop loss/take profit + - Adjust confidence levels + +3. **Backtest thoroughly**: + - Use MT5 Strategy Tester + - Test on different time periods + - Analyze performance metrics + +4. **Monitor and improve**: + - Track prediction accuracy + - Retrain models periodically + - Adjust based on market conditions + +## Example Workflow + +```bash +# 1. Train model +python train_onnx_model.py --symbol XAUUSD --timeframe H1 --epochs 50 + +# 2. Test predictions +python predict_with_onnx.py --model models/XAUUSD_H1_model.onnx --symbol XAUUSD + +# 3. Copy model to MT5 Files folder +# (Manual step) + +# 4. Compile and attach ONNX_EA.mq5 to chart + +# 5. Monitor and adjust parameters +``` + +## Tips + +- 🎯 Start with longer timeframes (H1, H4) for more stable predictions +- 📊 Use multiple models for different market conditions +- 🔄 Retrain models periodically (weekly/monthly) +- ⚠️ Always test on demo account first +- 📈 Monitor model performance and adjust parameters + +## Need Help? + +- Check the main [README.md](README.md) for detailed documentation +- Review the MQL5 ONNX documentation: https://www.mql5.com/en/docs/onnx/onnx_prepare +- Examine the code comments for implementation details + +Happy trading! 🚀 diff --git a/ai/README.md b/ai/README.md new file mode 100644 index 0000000..f5939fe --- /dev/null +++ b/ai/README.md @@ -0,0 +1,310 @@ +# ONNX Models with MetaTrader 5 + +A complete framework for training and using ONNX machine learning models in MetaTrader 5 for algorithmic trading. + +Based on the [MQL5 ONNX documentation](https://www.mql5.com/en/docs/onnx/onnx_prepare). + +## Overview + +This framework allows you to: +1. **Train neural network models** in Python using MetaTrader 5 historical data +2. **Export models to ONNX format** for use in MQL5 +3. **Use ONNX models in Expert Advisors** for real-time trading predictions +4. **Test predictions** using Python scripts + +## Features + +- 🧠 **LSTM Neural Networks** for price prediction +- 📊 **Technical Indicators** as features (RSI, EMA, ATR, etc.) +- 🔄 **ONNX Export** for MQL5 integration +- 📈 **Real-time Prediction** in Expert Advisors +- 🎯 **Flexible Configuration** for different symbols and timeframes + +## Installation + +### 1. Install Python Dependencies + +```bash +cd ai +pip install -r requirements.txt +``` + +### 2. Install MetaTrader 5 + +- Download and install [MetaTrader 5](https://www.metatrader5.com/en/download) +- Create a demo or live account +- Enable Python integration in MT5 settings: + - Tools → Options → Expert Advisors + - Check "Allow DLL imports" + - Check "Integration with Python" (if available) + +### 3. Configure MetaEditor (Optional) + +If you want to run Python scripts from MetaEditor: +- MetaEditor → Tools → Options → Compiler +- Set Python executable path +- Or click "Install" to download Python + +## Quick Start + +### Step 1: Train an ONNX Model + +Train a model for price prediction: + +```bash +python train_onnx_model.py --symbol XAUUSD --timeframe H1 --lookback 60 --epochs 50 +``` + +This will: +- Fetch 2 years of historical data from MT5 +- Prepare features (OHLCV + technical indicators) +- Train an LSTM neural network +- Export the model to `models/XAUUSD_H1_model.onnx` + +### Step 2: Test the Model + +Make predictions using the trained model: + +```bash +python predict_with_onnx.py --model models/XAUUSD_H1_model.onnx --symbol XAUUSD --timeframe H1 +``` + +### Step 3: Use in Expert Advisor + +1. Copy the ONNX model to MT5's Files folder: + ``` + \MQL5\Files\models\XAUUSD_H1_model.onnx + ``` + +2. Compile `ONNX_EA.mq5` in MetaEditor + +3. Attach the EA to a chart with: + - Model path: `models\XAUUSD_H1_model.onnx` + - Your trading parameters + +## Detailed Usage + +### Training Models + +#### Basic Training + +```bash +python train_onnx_model.py \ + --symbol XAUUSD \ + --timeframe H1 \ + --lookback 60 \ + --epochs 50 \ + --batch-size 32 +``` + +#### Advanced Options + +```bash +python train_onnx_model.py \ + --symbol EURUSD \ + --timeframe M15 \ + --lookback 100 \ + --epochs 100 \ + --batch-size 64 \ + --output custom_models +``` + +**Parameters:** +- `--symbol`: Trading symbol (XAUUSD, EURUSD, BTCUSD, etc.) +- `--timeframe`: M1, M5, M15, M30, H1, H4, D1 +- `--lookback`: Number of bars to use for prediction (default: 60) +- `--epochs`: Training epochs (default: 50) +- `--batch-size`: Batch size (default: 32) +- `--output`: Output directory (default: models) + +### Making Predictions + +#### Single Prediction + +```bash +python predict_with_onnx.py \ + --model models/XAUUSD_H1_model.onnx \ + --symbol XAUUSD \ + --timeframe H1 +``` + +#### Multiple Predictions + +```bash +python predict_with_onnx.py \ + --model models/XAUUSD_H1_model.onnx \ + --symbol XAUUSD \ + --timeframe H1 \ + --predictions 5 +``` + +### Expert Advisor Configuration + +The `ONNX_EA.mq5` Expert Advisor includes: + +**ONNX Model Settings:** +- `InpModelPath`: Path to ONNX model file +- `InpLookback`: Lookback period (must match training) +- `InpUsePrediction`: Enable/disable model predictions + +**Trading Settings:** +- `InpLotSize`: Position size +- `InpMagicNumber`: Magic number for trades +- `InpStopLoss`: Stop loss in pips +- `InpTakeProfit`: Take profit in pips + +**Prediction Settings:** +- `InpPredictionThreshold`: Minimum prediction change to trade (0.01% = 0.0001) +- `InpUseConfidence`: Enable confidence filtering +- `InpMinConfidence`: Minimum confidence level (0.0-1.0) + +## Model Architecture + +The default model uses: +- **Input**: 60 bars × 12 features +- **Architecture**: + - LSTM(128) → Dropout(0.2) + - LSTM(64) → Dropout(0.2) + - LSTM(32) → Dropout(0.2) + - Dense(16, ReLU) + - Dense(1) - Price prediction +- **Features**: + - OHLC prices + - Tick volume + - RSI (14) + - EMA(20), EMA(50) + - ATR(14) + - Price changes + - High/Low ratio + - Volume ratios + +## Customization + +### Modify Features + +Edit `train_onnx_model.py` to add/remove features: + +```python +def prepare_features(self, df: pd.DataFrame) -> pd.DataFrame: + feature_df = df[['open', 'high', 'low', 'close', 'tick_volume']].copy() + + # Add your custom indicators + feature_df['custom_indicator'] = your_calculation(df) + + return feature_df +``` + +### Change Model Architecture + +Modify `build_model()` in `train_onnx_model.py`: + +```python +def build_model(self, input_shape: tuple) -> keras.Model: + model = keras.Sequential([ + layers.LSTM(256, return_sequences=True, input_shape=input_shape), + # Add your layers here + layers.Dense(1) + ]) + return model +``` + +### Adjust Expert Advisor Logic + +Edit `ONNX_EA.mq5` to customize trading logic: +- Entry conditions +- Exit conditions +- Position management +- Risk management + +## File Structure + +``` +ai/ +├── requirements.txt # Python dependencies +├── train_onnx_model.py # Model training script +├── predict_with_onnx.py # Prediction testing script +├── ONNX_EA.mq5 # MQL5 Expert Advisor +├── README.md # This file +└── models/ # Trained ONNX models (created after training) +``` + +## Troubleshooting + +### MT5 Connection Issues + +**Error**: "MT5 initialization failed" +- Ensure MetaTrader 5 is installed and running +- Log into a demo or live account +- Check that the symbol exists in MT5 + +### Model Loading Issues + +**Error**: "Failed to load ONNX model" +- Verify the model file path is correct +- Ensure the model file is in MT5's Files folder +- Check that the model was exported correctly + +### Prediction Issues + +**Error**: "Failed to prepare input data" +- Ensure enough historical data is available +- Check that lookback period matches training +- Verify indicators can be calculated + +### Shape Mismatch Errors + +If you get shape mismatch errors: +1. Check that `InpLookback` in EA matches training `--lookback` +2. Verify feature count matches (default: 12 features) +3. Ensure input normalization matches training + +## Best Practices + +1. **Data Quality**: Use high-quality historical data +2. **Feature Engineering**: Experiment with different indicators +3. **Model Validation**: Always validate on out-of-sample data +4. **Risk Management**: Use stop loss and position sizing +5. **Backtesting**: Test thoroughly before live trading +6. **Monitoring**: Monitor model performance regularly + +## Example Workflow + +1. **Train Model**: + ```bash + python train_onnx_model.py --symbol XAUUSD --timeframe H1 + ``` + +2. **Test Predictions**: + ```bash + python predict_with_onnx.py --model models/XAUUSD_H1_model.onnx --symbol XAUUSD + ``` + +3. **Backtest in MT5**: + - Use Strategy Tester with `ONNX_EA.mq5` + - Test on historical data + - Analyze results + +4. **Optimize Parameters**: + - Adjust prediction threshold + - Tune confidence levels + - Optimize stop loss/take profit + +5. **Deploy**: + - Start with small position sizes + - Monitor performance + - Adjust as needed + +## References + +- [MQL5 ONNX Documentation](https://www.mql5.com/en/docs/onnx/onnx_prepare) +- [ONNX Model Zoo](https://github.com/onnx/models) +- [MetaTrader 5 Python Module](https://pypi.org/project/MetaTrader5/) +- [TensorFlow to ONNX](https://github.com/onnx/tensorflow-onnx) + +## Disclaimer + +Trading involves substantial risk of loss. This framework is provided for educational purposes only. Always test thoroughly on a demo account before using with real money. Past performance does not guarantee future results. + +## License + +This framework is provided for educational and research purposes. diff --git a/ai/example_workflow.py b/ai/example_workflow.py new file mode 100644 index 0000000..a217b20 --- /dev/null +++ b/ai/example_workflow.py @@ -0,0 +1,159 @@ +""" +Complete Example Workflow for ONNX + MT5 + +This script demonstrates the complete workflow: +1. Train an ONNX model +2. Test predictions +3. Show how to use in MT5 + +Run this to see the full process in action. +""" + +import os +import sys +from datetime import datetime +import MetaTrader5 as mt5 + +# Import our modules +from train_onnx_model import ONNXModelTrainer +from predict_with_onnx import ONNXPredictor + + +def main(): + """Complete workflow example.""" + print("="*60) + print("ONNX + MetaTrader 5 - Complete Workflow Example") + print("="*60) + + # Configuration + symbol = 'XAUUSD' + timeframe_str = 'H1' + lookback = 60 + epochs = 20 # Reduced for quick demo + + # Convert timeframe + timeframe_map = { + 'M1': mt5.TIMEFRAME_M1, + 'M5': mt5.TIMEFRAME_M5, + 'M15': mt5.TIMEFRAME_M15, + 'M30': mt5.TIMEFRAME_M30, + 'H1': mt5.TIMEFRAME_H1, + 'H4': mt5.TIMEFRAME_H4, + 'D1': mt5.TIMEFRAME_D1 + } + timeframe = timeframe_map[timeframe_str] + + # Create output directory + models_dir = 'models' + os.makedirs(models_dir, exist_ok=True) + + # Step 1: Train Model + print("\n" + "="*60) + print("STEP 1: Training ONNX Model") + print("="*60) + + trainer = ONNXModelTrainer( + symbol=symbol, + timeframe=timeframe, + lookback=lookback + ) + + try: + print(f"\nTraining model for {symbol} on {timeframe_str} timeframe...") + print(f"Lookback: {lookback} bars") + print(f"Epochs: {epochs}") + print("\nThis may take several minutes...\n") + + trainer.train(epochs=epochs, batch_size=32, verbose=1) + + # Export model + model_name = f"{symbol}_{timeframe_str}_model.onnx" + model_path = os.path.join(models_dir, model_name) + + print(f"\nExporting model to ONNX format...") + trainer.export_to_onnx(model_path) + + print(f"\n✓ Model saved to: {model_path}") + + except Exception as e: + print(f"\n✗ Training failed: {e}") + import traceback + traceback.print_exc() + trainer.cleanup() + return + + finally: + trainer.cleanup() + + # Step 2: Test Predictions + print("\n" + "="*60) + print("STEP 2: Testing Predictions") + print("="*60) + + predictor = ONNXPredictor(model_path) + + try: + # Get current price + symbol_info = mt5.symbol_info(symbol) + if symbol_info: + current_price = symbol_info.bid + print(f"\nCurrent {symbol} price: {current_price:.5f}") + else: + print(f"\nWarning: Could not get current price for {symbol}") + current_price = 0 + + # Make predictions + print(f"\nMaking predictions...") + predictions = predictor.predict_batch(symbol, timeframe, n_predictions=3) + + print("\nPredictions:") + for i, pred in enumerate(predictions, 1): + if current_price > 0: + change = pred - current_price + change_pct = (change / current_price) * 100 + print(f" {i}. {pred:.5f} (change: {change:+.5f}, {change_pct:+.2f}%)") + else: + print(f" {i}. {pred:.5f}") + + except Exception as e: + print(f"\n✗ Prediction failed: {e}") + import traceback + traceback.print_exc() + finally: + predictor.cleanup() + + # Step 3: Instructions for MT5 + print("\n" + "="*60) + print("STEP 3: Using in MetaTrader 5") + print("="*60) + + print(f"\nTo use this model in MetaTrader 5:") + print(f"\n1. Copy the model file to MT5's Files folder:") + print(f" {model_path}") + print(f" → \\MQL5\\Files\\models\\{os.path.basename(model_path)}") + print(f"\n2. Open ONNX_EA.mq5 in MetaEditor") + print(f"\n3. Set EA parameters:") + print(f" - Model Path: models\\{os.path.basename(model_path)}") + print(f" - Lookback: {lookback}") + print(f" - Your trading parameters") + print(f"\n4. Compile and attach to chart") + print(f"\n5. Monitor performance") + + print("\n" + "="*60) + print("Workflow completed!") + print("="*60 + "\n") + + +if __name__ == '__main__': + # Check MT5 connection first + if not mt5.initialize(): + print("ERROR: Failed to initialize MT5") + print("Make sure MetaTrader 5 is running and you're logged in.") + sys.exit(1) + + try: + main() + except KeyboardInterrupt: + print("\n\nInterrupted by user") + finally: + mt5.shutdown() diff --git a/ai/predict_with_onnx.py b/ai/predict_with_onnx.py new file mode 100644 index 0000000..b590766 --- /dev/null +++ b/ai/predict_with_onnx.py @@ -0,0 +1,261 @@ +""" +ONNX Model Prediction Script + +This script loads a trained ONNX model and makes predictions using MT5 data. +Can be run from MetaEditor or directly in Python. + +Usage: + python predict_with_onnx.py --model models/XAUUSD_H1_model.onnx --symbol XAUUSD +""" + +import argparse +import numpy as np +import pandas as pd +import MetaTrader5 as mt5 +import onnxruntime as ort +from datetime import datetime +from sklearn.preprocessing import MinMaxScaler +import pickle +import os + + +class ONNXPredictor: + """ + Predictor class for using ONNX models with MT5 data. + """ + + def __init__(self, model_path: str, scaler_path: str = None): + """ + Initialize the predictor. + + Args: + model_path: Path to ONNX model file + scaler_path: Path to saved scaler (optional, will create if not provided) + """ + self.model_path = model_path + self.scaler_path = scaler_path + + # Load ONNX model + if not os.path.exists(model_path): + raise FileNotFoundError(f"ONNX model not found: {model_path}") + + self.session = ort.InferenceSession(model_path) + + # Get input/output info + self.input_name = self.session.get_inputs()[0].name + self.output_name = self.session.get_outputs()[0].name + self.input_shape = self.session.get_inputs()[0].shape + + print(f"Loaded ONNX model: {model_path}") + print(f"Input shape: {self.input_shape}") + print(f"Input name: {self.input_name}") + print(f"Output name: {self.output_name}") + + # Load or create scaler + if scaler_path and os.path.exists(scaler_path): + with open(scaler_path, 'rb') as f: + self.scaler = pickle.load(f) + print(f"Loaded scaler from: {scaler_path}") + else: + self.scaler = MinMaxScaler() + print("Using default scaler (will need to fit)") + + # Initialize MT5 + if not mt5.initialize(): + raise RuntimeError(f"MT5 initialization failed: {mt5.last_error()}") + + def prepare_features(self, df: pd.DataFrame) -> pd.DataFrame: + """ + Prepare features from raw OHLCV data (same as training). + + Args: + df: Raw OHLCV data + + Returns: + DataFrame with features + """ + features = ['open', 'high', 'low', 'close', 'tick_volume'] + feature_df = df[features].copy() + + # Add technical indicators + feature_df['rsi'] = self._calculate_rsi(df['close'], period=14) + feature_df['ema_20'] = df['close'].ewm(span=20).mean() + feature_df['ema_50'] = df['close'].ewm(span=50).mean() + feature_df['atr'] = self._calculate_atr(df, period=14) + feature_df['price_change'] = df['close'].pct_change() + feature_df['high_low_ratio'] = df['high'] / df['low'] + feature_df['volume_ma'] = df['tick_volume'].rolling(window=20).mean() + feature_df['volume_ratio'] = df['tick_volume'] / feature_df['volume_ma'] + + feature_df = feature_df.dropna() + return feature_df + + def _calculate_rsi(self, prices: pd.Series, period: int = 14) -> pd.Series: + """Calculate RSI indicator.""" + delta = prices.diff() + gain = (delta.where(delta > 0, 0)).rolling(window=period).mean() + loss = (-delta.where(delta < 0, 0)).rolling(window=period).mean() + rs = gain / loss + rsi = 100 - (100 / (1 + rs)) + return rsi + + def _calculate_atr(self, df: pd.DataFrame, period: int = 14) -> pd.Series: + """Calculate ATR indicator.""" + high_low = df['high'] - df['low'] + high_close = np.abs(df['high'] - df['close'].shift()) + low_close = np.abs(df['low'] - df['close'].shift()) + tr = pd.concat([high_low, high_close, low_close], axis=1).max(axis=1) + atr = tr.rolling(window=period).mean() + return atr + + def get_latest_data(self, symbol: str, timeframe: int, lookback: int) -> np.ndarray: + """ + Get latest data from MT5 and prepare for prediction. + + Args: + symbol: Trading symbol + timeframe: MT5 timeframe + lookback: Number of bars needed + + Returns: + Prepared feature array ready for model input + """ + # Fetch data + rates = mt5.copy_rates_from_pos(symbol, timeframe, 0, lookback + 50) + + if rates is None or len(rates) < lookback: + raise ValueError(f"Insufficient data for {symbol}") + + df = pd.DataFrame(rates) + df['time'] = pd.to_datetime(df['time'], unit='s') + + # Prepare features + feature_df = self.prepare_features(df) + + # Get last lookback bars + feature_data = feature_df.values[-lookback:] + + # Scale features + if hasattr(self.scaler, 'scale_'): + feature_data_scaled = self.scaler.transform(feature_data) + else: + # Fit scaler if not already fitted + print("Warning: Scaler not fitted, fitting on current data...") + feature_data_scaled = self.scaler.fit_transform(feature_data) + + # Reshape for model input: (1, lookback, features) + feature_data_scaled = feature_data_scaled.reshape(1, lookback, -1) + + return feature_data_scaled.astype(np.float32) + + def predict(self, symbol: str, timeframe: int, lookback: int = None) -> float: + """ + Make a prediction for the next price. + + Args: + symbol: Trading symbol + timeframe: MT5 timeframe + lookback: Number of bars to use (default: from model input shape) + + Returns: + Predicted price + """ + if lookback is None: + lookback = self.input_shape[1] if self.input_shape[1] else 60 + + # Get and prepare data + input_data = self.get_latest_data(symbol, timeframe, lookback) + + # Make prediction + outputs = self.session.run([self.output_name], {self.input_name: input_data}) + prediction = outputs[0][0][0] + + return float(prediction) + + def predict_batch(self, symbol: str, timeframe: int, n_predictions: int = 5) -> list: + """ + Make multiple predictions. + + Args: + symbol: Trading symbol + timeframe: MT5 timeframe + n_predictions: Number of predictions to make + + Returns: + List of predictions + """ + predictions = [] + for _ in range(n_predictions): + pred = self.predict(symbol, timeframe) + predictions.append(pred) + return predictions + + def cleanup(self): + """Clean up MT5 connection.""" + mt5.shutdown() + + +def main(): + """Main function.""" + parser = argparse.ArgumentParser(description='Make predictions using ONNX model') + parser.add_argument('--model', type=str, required=True, + help='Path to ONNX model file') + parser.add_argument('--symbol', type=str, default='XAUUSD', + help='Trading symbol') + parser.add_argument('--timeframe', type=str, default='H1', + choices=['M1', 'M5', 'M15', 'M30', 'H1', 'H4', 'D1'], + help='Timeframe') + parser.add_argument('--scaler', type=str, default=None, + help='Path to saved scaler (optional)') + parser.add_argument('--predictions', type=int, default=1, + help='Number of predictions to make') + + args = parser.parse_args() + + # Convert timeframe + timeframe_map = { + 'M1': mt5.TIMEFRAME_M1, + 'M5': mt5.TIMEFRAME_M5, + 'M15': mt5.TIMEFRAME_M15, + 'M30': mt5.TIMEFRAME_M30, + 'H1': mt5.TIMEFRAME_H1, + 'H4': mt5.TIMEFRAME_H4, + 'D1': mt5.TIMEFRAME_D1 + } + timeframe = timeframe_map[args.timeframe] + + # Create predictor + predictor = ONNXPredictor(args.model, args.scaler) + + try: + # Get current price + symbol_info = mt5.symbol_info(args.symbol) + current_price = symbol_info.bid if symbol_info else 0 + + print(f"\nCurrent {args.symbol} price: {current_price:.5f}") + print(f"Making {args.predictions} prediction(s)...\n") + + # Make predictions + if args.predictions == 1: + prediction = predictor.predict(args.symbol, timeframe) + print(f"Predicted next price: {prediction:.5f}") + print(f"Expected change: {(prediction - current_price):.5f} " + f"({((prediction - current_price) / current_price * 100):.2f}%)") + else: + predictions = predictor.predict_batch(args.symbol, timeframe, args.predictions) + print("Predictions:") + for i, pred in enumerate(predictions, 1): + change = pred - current_price + change_pct = (change / current_price * 100) if current_price > 0 else 0 + print(f" {i}. {pred:.5f} (change: {change:+.5f}, {change_pct:+.2f}%)") + + except Exception as e: + print(f"\nError: {e}") + import traceback + traceback.print_exc() + finally: + predictor.cleanup() + + +if __name__ == '__main__': + main() diff --git a/ai/requirements.txt b/ai/requirements.txt new file mode 100644 index 0000000..a33e8c6 --- /dev/null +++ b/ai/requirements.txt @@ -0,0 +1,17 @@ +# ONNX and Machine Learning +onnx>=1.12.0 +onnxruntime>=1.12.0 +tensorflow>=2.10.0 +tf2onnx>=1.13.0 + +# Data Processing +pandas>=1.3.0 +numpy>=1.21.0 +scikit-learn>=1.0.0 + +# MetaTrader 5 Integration +MetaTrader5>=5.0.45 + +# Visualization and Utilities +matplotlib>=3.4.0 +tqdm>=4.64.0 diff --git a/ai/train_onnx_model.py b/ai/train_onnx_model.py new file mode 100644 index 0000000..c514fe0 --- /dev/null +++ b/ai/train_onnx_model.py @@ -0,0 +1,364 @@ +""" +ONNX Model Training Script for MetaTrader 5 + +This script trains a neural network model for price prediction and exports it to ONNX format. +Based on MQL5 ONNX documentation: https://www.mql5.com/en/docs/onnx/onnx_prepare + +Usage: + python train_onnx_model.py --symbol XAUUSD --timeframe H1 --lookback 60 --epochs 50 +""" + +import argparse +import os +import sys +from datetime import datetime, timedelta +import numpy as np +import pandas as pd +import MetaTrader5 as mt5 +import tensorflow as tf +from tensorflow import keras +from tensorflow.keras import layers +from sklearn.preprocessing import MinMaxScaler +from sklearn.model_selection import train_test_split +import tf2onnx +import onnx +from tqdm import tqdm + + +class ONNXModelTrainer: + """ + Trainer class for creating ONNX models from MT5 data. + """ + + def __init__(self, symbol: str, timeframe: int, lookback: int = 60, + prediction_horizon: int = 1, features: list = None): + """ + Initialize the trainer. + + Args: + symbol: Trading symbol (e.g., 'XAUUSD', 'EURUSD') + timeframe: MT5 timeframe constant + lookback: Number of bars to look back for prediction + prediction_horizon: Number of bars ahead to predict + features: List of features to use (default: OHLC + volume) + """ + self.symbol = symbol + self.timeframe = timeframe + self.lookback = lookback + self.prediction_horizon = prediction_horizon + self.features = features or ['open', 'high', 'low', 'close', 'tick_volume'] + + self.scaler = MinMaxScaler() + self.model = None + + # Initialize MT5 + if not mt5.initialize(): + raise RuntimeError(f"MT5 initialization failed: {mt5.last_error()}") + + def fetch_data(self, start_date: datetime, end_date: datetime) -> pd.DataFrame: + """ + Fetch historical data from MT5. + + Args: + start_date: Start date for data + end_date: End date for data + + Returns: + DataFrame with OHLCV data + """ + print(f"Fetching data for {self.symbol} from {start_date} to {end_date}...") + + rates = mt5.copy_rates_range(self.symbol, self.timeframe, start_date, end_date) + + if rates is None or len(rates) == 0: + raise ValueError(f"No data available for {self.symbol} in the specified date range") + + df = pd.DataFrame(rates) + df['time'] = pd.to_datetime(df['time'], unit='s') + df.set_index('time', inplace=True) + + print(f"Fetched {len(df)} bars") + return df + + def prepare_features(self, df: pd.DataFrame) -> pd.DataFrame: + """ + Prepare features for training. + + Args: + df: Raw OHLCV data + + Returns: + DataFrame with features + """ + feature_df = df[self.features].copy() + + # Add technical indicators as features + feature_df['rsi'] = self._calculate_rsi(df['close'], period=14) + feature_df['ema_20'] = df['close'].ewm(span=20).mean() + feature_df['ema_50'] = df['close'].ewm(span=50).mean() + feature_df['atr'] = self._calculate_atr(df, period=14) + + # Price changes + feature_df['price_change'] = df['close'].pct_change() + feature_df['high_low_ratio'] = df['high'] / df['low'] + + # Volume features + feature_df['volume_ma'] = df['tick_volume'].rolling(window=20).mean() + feature_df['volume_ratio'] = df['tick_volume'] / feature_df['volume_ma'] + + # Drop NaN values + feature_df = feature_df.dropna() + + return feature_df + + def _calculate_rsi(self, prices: pd.Series, period: int = 14) -> pd.Series: + """Calculate RSI indicator.""" + delta = prices.diff() + gain = (delta.where(delta > 0, 0)).rolling(window=period).mean() + loss = (-delta.where(delta < 0, 0)).rolling(window=period).mean() + rs = gain / loss + rsi = 100 - (100 / (1 + rs)) + return rsi + + def _calculate_atr(self, df: pd.DataFrame, period: int = 14) -> pd.Series: + """Calculate ATR indicator.""" + high_low = df['high'] - df['low'] + high_close = np.abs(df['high'] - df['close'].shift()) + low_close = np.abs(df['low'] - df['close'].shift()) + tr = pd.concat([high_low, high_close, low_close], axis=1).max(axis=1) + atr = tr.rolling(window=period).mean() + return atr + + def create_sequences(self, data: np.ndarray, target: np.ndarray) -> tuple: + """ + Create sequences for LSTM/RNN training. + + Args: + data: Feature data + target: Target values + + Returns: + Tuple of (X, y) sequences + """ + X, y = [], [] + + for i in range(self.lookback, len(data) - self.prediction_horizon + 1): + X.append(data[i - self.lookback:i]) + y.append(target[i + self.prediction_horizon - 1]) + + return np.array(X), np.array(y) + + def build_model(self, input_shape: tuple) -> keras.Model: + """ + Build the neural network model. + + Args: + input_shape: Shape of input data (lookback, features) + + Returns: + Compiled Keras model + """ + model = keras.Sequential([ + layers.LSTM(128, return_sequences=True, input_shape=input_shape), + layers.Dropout(0.2), + layers.LSTM(64, return_sequences=True), + layers.Dropout(0.2), + layers.LSTM(32), + layers.Dropout(0.2), + layers.Dense(16, activation='relu'), + layers.Dense(1) # Predict next close price + ]) + + model.compile( + optimizer=keras.optimizers.Adam(learning_rate=0.001), + loss='mse', + metrics=['mae'] + ) + + return model + + def train(self, epochs: int = 50, batch_size: int = 32, + validation_split: float = 0.2, verbose: int = 1): + """ + Train the model. + + Args: + epochs: Number of training epochs + batch_size: Batch size for training + validation_split: Fraction of data to use for validation + verbose: Verbosity level + """ + # Fetch data (last 2 years) + end_date = datetime.now() + start_date = end_date - timedelta(days=730) + + df = self.fetch_data(start_date, end_date) + feature_df = self.prepare_features(df) + + # Prepare data + feature_data = feature_df.values + target_data = df['close'].values[feature_df.index] + + # Scale features + feature_data_scaled = self.scaler.fit_transform(feature_data) + + # Create sequences + X, y = self.create_sequences(feature_data_scaled, target_data) + + # Split data + X_train, X_test, y_train, y_test = train_test_split( + X, y, test_size=validation_split, shuffle=False + ) + + print(f"\nTraining data shape: {X_train.shape}") + print(f"Validation data shape: {X_test.shape}") + + # Build model + self.model = self.build_model((X_train.shape[1], X_train.shape[2])) + + print("\nModel architecture:") + self.model.summary() + + # Train model + print("\nTraining model...") + history = self.model.fit( + X_train, y_train, + batch_size=batch_size, + epochs=epochs, + validation_data=(X_test, y_test), + verbose=verbose, + callbacks=[ + keras.callbacks.EarlyStopping( + monitor='val_loss', + patience=10, + restore_best_weights=True + ), + keras.callbacks.ReduceLROnPlateau( + monitor='val_loss', + factor=0.5, + patience=5, + min_lr=0.0001 + ) + ] + ) + + # Evaluate + train_loss = self.model.evaluate(X_train, y_train, verbose=0) + test_loss = self.model.evaluate(X_test, y_test, verbose=0) + + print(f"\nTraining Loss: {train_loss[0]:.4f}, MAE: {train_loss[1]:.4f}") + print(f"Validation Loss: {test_loss[0]:.4f}, MAE: {test_loss[1]:.4f}") + + return history + + def export_to_onnx(self, output_path: str): + """ + Export the trained model to ONNX format. + + Args: + output_path: Path to save ONNX model + """ + if self.model is None: + raise ValueError("Model must be trained before exporting") + + print(f"\nExporting model to ONNX format: {output_path}") + + # Get input shape + input_shape = (1, self.lookback, len(self.features) + 7) # +7 for added features + + # Create dummy input + dummy_input = np.random.randn(*input_shape).astype(np.float32) + + # Convert to ONNX + spec = (tf.TensorSpec((None, self.lookback, len(self.features) + 7), tf.float32, name="input"),) + output_path_onnx = tf2onnx.convert.from_keras( + self.model, + input_signature=spec, + opset=13, + output_path=output_path + ) + + print(f"✓ ONNX model saved to: {output_path}") + + # Verify ONNX model + try: + onnx_model = onnx.load(output_path) + onnx.checker.check_model(onnx_model) + print("✓ ONNX model validation passed") + except Exception as e: + print(f"⚠ ONNX model validation warning: {e}") + + def cleanup(self): + """Clean up MT5 connection.""" + mt5.shutdown() + + +def main(): + """Main function.""" + parser = argparse.ArgumentParser(description='Train ONNX model for MT5 price prediction') + parser.add_argument('--symbol', type=str, default='XAUUSD', help='Trading symbol') + parser.add_argument('--timeframe', type=str, default='H1', + choices=['M1', 'M5', 'M15', 'M30', 'H1', 'H4', 'D1'], + help='Timeframe') + parser.add_argument('--lookback', type=int, default=60, + help='Number of bars to look back') + parser.add_argument('--epochs', type=int, default=50, help='Training epochs') + parser.add_argument('--batch-size', type=int, default=32, help='Batch size') + parser.add_argument('--output', type=str, default='models', + help='Output directory for ONNX model') + + args = parser.parse_args() + + # Convert timeframe string to MT5 constant + timeframe_map = { + 'M1': mt5.TIMEFRAME_M1, + 'M5': mt5.TIMEFRAME_M5, + 'M15': mt5.TIMEFRAME_M15, + 'M30': mt5.TIMEFRAME_M30, + 'H1': mt5.TIMEFRAME_H1, + 'H4': mt5.TIMEFRAME_H4, + 'D1': mt5.TIMEFRAME_D1 + } + timeframe = timeframe_map[args.timeframe] + + # Create output directory + os.makedirs(args.output, exist_ok=True) + + # Create trainer + trainer = ONNXModelTrainer( + symbol=args.symbol, + timeframe=timeframe, + lookback=args.lookback + ) + + try: + # Train model + trainer.train(epochs=args.epochs, batch_size=args.batch_size) + + # Export to ONNX + model_name = f"{args.symbol}_{args.timeframe}_model.onnx" + output_path = os.path.join(args.output, model_name) + trainer.export_to_onnx(output_path) + + # Save scaler for consistent normalization + scaler_path = os.path.join(args.output, f"{args.symbol}_{args.timeframe}_scaler.pkl") + import pickle + with open(scaler_path, 'wb') as f: + pickle.dump(trainer.scaler, f) + print(f"✓ Scaler saved to: {scaler_path}") + print(f" Use this with predict_with_onnx.py for consistent normalization") + + print(f"\n{'='*60}") + print("Training completed successfully!") + print(f"ONNX model saved to: {output_path}") + print(f"{'='*60}\n") + + except Exception as e: + print(f"\nError: {e}") + sys.exit(1) + finally: + trainer.cleanup() + + +if __name__ == '__main__': + main() diff --git a/backtesting/MT5/.gitignore b/backtesting/MT5/.gitignore new file mode 100644 index 0000000..47c83f8 --- /dev/null +++ b/backtesting/MT5/.gitignore @@ -0,0 +1,41 @@ +# Python +__pycache__/ +*.py[cod] +*$py.class +*.so +.Python +env/ +venv/ +ENV/ +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +*.egg-info/ +.installed.cfg +*.egg + +# Backtest results +backtest_results/ +*.csv +*.png +*.jpg + +# IDE +.vscode/ +.idea/ +*.swp +*.swo +*~ + +# OS +.DS_Store +Thumbs.db diff --git a/backtesting/MT5/QUICKSTART.md b/backtesting/MT5/QUICKSTART.md new file mode 100644 index 0000000..dc18faf --- /dev/null +++ b/backtesting/MT5/QUICKSTART.md @@ -0,0 +1,165 @@ +# Quick Start Guide + +Get up and running with the MT5 Python backtesting framework in 5 minutes! + +## Step 1: Install Dependencies + +```bash +cd backtesting/MT5 +pip install -r requirements.txt +``` + +## Step 2: Test Your Setup + +Before running backtests, verify that MT5 is properly configured: + +```bash +python test_setup.py +``` + +This will: +- Test MT5 connection +- Check account access +- Verify symbol availability +- Test historical data retrieval +- Test indicator creation + +**If this fails**, make sure: +1. MetaTrader5 is installed +2. MT5 is running +3. You're logged into a demo or live account +4. You have historical data downloaded in MT5 + +## Step 3: Run Your First Backtest + +### Option A: Command Line (Easiest) + +```bash +python run_backtest.py --strategy RSIReversalStrategy --symbol XAUUSD --start 2023-01-01 --end 2024-01-01 +``` + +This will: +- Run the RSI Reversal strategy on Gold (XAUUSD) +- Backtest from Jan 1, 2023 to Jan 1, 2024 +- Generate performance reports in `backtest_results/` + +### Option B: Python Script + +```python +from datetime import datetime +import MetaTrader5 as mt5 +from backtest_engine import BacktestEngine +from example_strategies import RSIReversalStrategy +from performance_analyzer import PerformanceAnalyzer + +# Create strategy +strategy = RSIReversalStrategy( + symbol='XAUUSD', + timeframe=mt5.TIMEFRAME_H1, + initial_balance=10000.0 +) + +# Run backtest +engine = BacktestEngine( + strategy, + start_date=datetime(2023, 1, 1), + end_date=datetime(2024, 1, 1) +) + +results = engine.run() + +# View results +analyzer = PerformanceAnalyzer(results) +analyzer.generate_report('my_results') +``` + +## Step 4: Create Your Own Strategy + +1. **Copy an example strategy** from `example_strategies.py` + +2. **Modify the `on_bar()` method** with your trading logic: + +```python +def on_bar(self, bar_data): + rsi = bar_data.get('rsi') + current_price = bar_data['close'] + + # Your logic here + if rsi < 30 and self.position is None: + self.open_position('BUY', 0.1, current_price) +``` + +3. **Specify required indicators**: + +```python +def get_required_indicators(self): + return { + 'rsi': {'period': 14, 'applied_price': mt5.PRICE_CLOSE}, + 'ema': {'period': 50, 'applied_price': mt5.PRICE_CLOSE} + } +``` + +4. **Run your strategy**: + +```python +from backtest_engine import BacktestEngine +# ... (same as above) +``` + +## Common Commands + +### Different Symbols +```bash +python run_backtest.py --strategy RSIReversalStrategy --symbol EURUSD --start 2023-01-01 --end 2024-01-01 +``` + +### Different Timeframes +```bash +python run_backtest.py --strategy RSIScalpingStrategy --symbol XAUUSD --timeframe M15 --start 2023-01-01 --end 2024-01-01 +``` + +### Custom Parameters +```bash +python run_backtest.py --strategy RSIReversalStrategy --symbol XAUUSD --start 2023-01-01 --end 2024-01-01 --rsi-period 28 --rsi-overbought 64 --rsi-oversold 13 --lot-size 0.2 +``` + +## Understanding the Output + +After running a backtest, you'll get: + +1. **Console Summary**: Key metrics printed to terminal +2. **Equity Curve**: `*_equity_curve.png` - Account balance over time +3. **Drawdown Chart**: `*_drawdown.png` - Drawdown visualization +4. **Monthly Returns**: `*_monthly_returns.png` - Monthly performance +5. **Trades CSV**: `*_trades.csv` - Detailed trade log + +## Next Steps + +- **Optimize Parameters**: Try different parameter combinations +- **Test Multiple Strategies**: Compare different approaches +- **Add More Indicators**: Extend `BacktestEngine.setup_indicators()` +- **Improve Risk Management**: Customize position sizing and risk rules + +## Troubleshooting + +### "MT5 initialization failed" +- Make sure MT5 is installed and running +- Try logging into MT5 manually first +- Check that you have a demo/live account configured + +### "No data available" +- Check date range - ensure data exists +- Verify symbol name (e.g., 'XAUUSD' not 'GOLD') +- Download historical data in MT5 (Tools > History Center) + +### "Failed to create indicator" +- Ensure enough bars are available (need more bars than indicator period) +- Check indicator parameters are valid + +## Need Help? + +- Check the main [README.md](README.md) for detailed documentation +- Review `example_strategies.py` for strategy examples +- Look at `example_usage.py` for more usage examples + +Happy backtesting! 🚀 diff --git a/backtesting/MT5/README.md b/backtesting/MT5/README.md new file mode 100644 index 0000000..75b0a7f --- /dev/null +++ b/backtesting/MT5/README.md @@ -0,0 +1,279 @@ +# MetaTrader5 Python Backtesting Framework + +A comprehensive Python backtesting framework for algorithmic trading strategies using MetaTrader5 historical data. + +## Features + +- **Easy Strategy Development**: Inherit from `BaseStrategy` and implement your trading logic +- **MT5 Integration**: Uses MetaTrader5 Python library for historical data and indicators +- **Multiple Indicators**: Built-in support for RSI, EMA, SMA, ATR, MACD, and more +- **Risk Management**: Built-in position sizing, stop loss, take profit, and drawdown protection +- **Performance Analysis**: Comprehensive metrics and visualization tools +- **Example Strategies**: Ready-to-use example strategies (RSI Scalping, EMA Crossover, RSI Reversal) + +## Installation + +1. **Install MetaTrader5**: Make sure you have MetaTrader5 installed on your system. + +2. **Install Python dependencies**: +```bash +pip install -r requirements.txt +``` + +3. **Initialize MT5 Connection**: The framework will automatically connect to MT5 when you run a backtest. Make sure MT5 is installed and you have a demo or live account configured. + +## Quick Start + +### Running a Backtest + +Use the command-line interface to run a backtest: + +```bash +python run_backtest.py --strategy RSIReversalStrategy --symbol XAUUSD --start 2023-01-01 --end 2024-01-01 +``` + +### Creating Your Own Strategy + +1. Create a new Python file or add to `example_strategies.py`: + +```python +from base_strategy import BaseStrategy +import MetaTrader5 as mt5 + +class MyStrategy(BaseStrategy): + def __init__(self, symbol, timeframe, initial_balance=10000.0): + super().__init__(symbol, timeframe, initial_balance) + # Initialize your strategy parameters + self.my_param = 42 + + def get_required_indicators(self): + return { + 'rsi': {'period': 14, 'applied_price': mt5.PRICE_CLOSE}, + 'ema': {'period': 50, 'applied_price': mt5.PRICE_CLOSE} + } + + def on_bar(self, bar_data): + # Your trading logic here + rsi = bar_data.get('rsi') + ema = bar_data.get('ema') + current_price = bar_data['close'] + + # Example: Buy when RSI < 30 and price > EMA + if rsi < 30 and current_price > ema: + if self.position is None: + self.open_position('BUY', 0.1, current_price) + + def get_parameters(self): + return {'my_param': self.my_param} +``` + +2. Run your strategy: + +```python +from datetime import datetime +from backtest_engine import BacktestEngine +from performance_analyzer import PerformanceAnalyzer + +# Create strategy +strategy = MyStrategy('XAUUSD', mt5.TIMEFRAME_H1, initial_balance=10000.0) + +# Run backtest +engine = BacktestEngine( + strategy, + start_date=datetime(2023, 1, 1), + end_date=datetime(2024, 1, 1) +) + +results = engine.run() + +# Analyze results +analyzer = PerformanceAnalyzer(results) +analyzer.generate_report('my_backtest_results') +``` + +## Available Strategies + +### RSIScalpingStrategy +RSI-based scalping strategy that enters on RSI crossovers. + +**Parameters:** +- `rsi_period`: RSI period (default: 14) +- `rsi_overbought`: Overbought level (default: 70) +- `rsi_oversold`: Oversold level (default: 30) +- `rsi_target_buy`: Exit target for long positions (default: 80) +- `rsi_target_sell`: Exit target for short positions (default: 20) + +### EMAStrategy +Simple EMA crossover strategy. + +**Parameters:** +- `ema_period`: EMA period (default: 50) + +### RSIReversalStrategy +RSI reversal strategy similar to your MQL5 implementations. + +**Parameters:** +- `rsi_period`: RSI period (default: 14) +- `rsi_overbought`: Overbought level (default: 70) +- `rsi_oversold`: Oversold level (default: 30) +- `rsi_exit`: Neutral exit level (default: 50) + +## Command Line Options + +```bash +python run_backtest.py --help +``` + +**Required Arguments:** +- `--strategy`: Strategy name (RSIScalpingStrategy, EMAStrategy, RSIReversalStrategy) +- `--start`: Start date (YYYY-MM-DD) +- `--end`: End date (YYYY-MM-DD) + +**Optional Arguments:** +- `--symbol`: Trading symbol (default: XAUUSD) +- `--timeframe`: Timeframe M1, M5, M15, M30, H1, H4, D1 (default: H1) +- `--balance`: Initial balance (default: 10000) +- `--output`: Output directory (default: backtest_results) +- `--rsi-period`: RSI period (default: 14) +- `--rsi-overbought`: RSI overbought level (default: 70) +- `--rsi-oversold`: RSI oversold level (default: 30) +- `--ema-period`: EMA period (default: 50) +- `--lot-size`: Lot size (default: 0.1) +- `--stop-loss`: Stop loss in pips (default: 50) +- `--take-profit`: Take profit in pips (default: 100) + +## Example Commands + +```bash +# RSI Scalping on Gold, 1-hour timeframe +python run_backtest.py --strategy RSIScalpingStrategy --symbol XAUUSD --timeframe H1 --start 2023-01-01 --end 2024-01-01 + +# EMA Strategy on EUR/USD, 4-hour timeframe +python run_backtest.py --strategy EMAStrategy --symbol EURUSD --timeframe H4 --start 2023-01-01 --end 2024-01-01 --ema-period 100 + +# RSI Reversal with custom parameters +python run_backtest.py --strategy RSIReversalStrategy --symbol XAUUSD --start 2023-01-01 --end 2024-01-01 --rsi-period 28 --rsi-overbought 64 --rsi-oversold 13 +``` + +## Output + +The backtest generates: + +1. **Console Summary**: Performance metrics printed to console +2. **Equity Curve Chart**: Visual representation of account balance over time +3. **Drawdown Chart**: Drawdown visualization +4. **Monthly Returns Chart**: Monthly performance breakdown +5. **Trades CSV**: Detailed trade log in CSV format + +All files are saved in the specified output directory (default: `backtest_results/`). + +## Performance Metrics + +The framework calculates: + +- **Total Return**: Percentage return on initial balance +- **Win Rate**: Percentage of winning trades +- **Profit Factor**: Total profit / Total loss +- **Average Win/Loss**: Average profit per winning/losing trade +- **Maximum Drawdown**: Largest peak-to-trough decline +- **Total Trades**: Number of completed trades + +## BaseStrategy API + +### Methods to Override + +- `on_bar(bar_data)`: Called on each new bar with market data and indicators +- `get_parameters()`: Return strategy parameters for logging +- `get_required_indicators()`: Specify which indicators are needed + +### Available Methods + +- `open_position(order_type, volume, price, sl=None, tp=None, comment="")`: Open a position +- `close_position(close_price)`: Close current position +- `check_stop_loss_take_profit(current_price)`: Check SL/TP (called automatically) +- `get_performance_metrics()`: Get performance statistics + +### Bar Data Structure + +The `bar_data` dictionary passed to `on_bar()` contains: + +```python +{ + 'time': datetime, # Bar timestamp + 'open': float, # Opening price + 'high': float, # High price + 'low': float, # Low price + 'close': float, # Closing price + 'tick_volume': int, # Tick volume + 'spread': int, # Spread in points + 'rsi': float, # RSI value (if requested) + 'ema': float, # EMA value (if requested) + 'indicators': { # All requested indicators + 'rsi': float, + 'ema': float, + ... + } +} +``` + +## Supported Indicators + +- **RSI**: Relative Strength Index +- **EMA**: Exponential Moving Average +- **SMA**: Simple Moving Average +- **ATR**: Average True Range +- **MACD**: Moving Average Convergence Divergence + +To add more indicators, modify `BacktestEngine.setup_indicators()`. + +## Risk Management + +The framework includes built-in risk management: + +- **Position Sizing**: Configurable min/max lot sizes +- **Stop Loss/Take Profit**: Automatic SL/TP checking +- **Spread Filtering**: Skip trades when spread is too high +- **Drawdown Protection**: Track and limit maximum drawdown +- **Margin Management**: Prevent over-leveraging + +## Tips + +1. **Test on Demo First**: Always test strategies on demo accounts before live trading +2. **Start Small**: Begin with small position sizes and gradually increase +3. **Multiple Timeframes**: Test strategies on different timeframes +4. **Parameter Optimization**: Use the framework to optimize strategy parameters +5. **Compare Strategies**: Run multiple strategies and compare results + +## Troubleshooting + +### MT5 Connection Issues +- Ensure MetaTrader5 is installed and running +- Check that you have a demo or live account configured +- Verify symbol names match MT5 format (e.g., 'XAUUSD' not 'GOLD') + +### No Data Available +- Check date range - ensure data exists for the specified period +- Verify symbol name is correct +- Check that MT5 has historical data for the symbol/timeframe + +### Indicator Errors +- Ensure indicator parameters are valid +- Check that enough bars are available for indicator calculation +- Verify indicator handle creation succeeded + +## Contributing + +Feel free to extend this framework with: +- Additional indicators +- More sophisticated risk management +- Optimization tools +- Walk-forward analysis +- Monte Carlo simulation + +## License + +This framework is provided for educational and research purposes. + +## Disclaimer + +Trading involves substantial risk of loss. This framework is provided for educational purposes only. Always test thoroughly on a demo account before using with real money. Past performance does not guarantee future results. diff --git a/backtesting/MT5/backtest_engine.py b/backtesting/MT5/backtest_engine.py new file mode 100644 index 0000000..46477c1 --- /dev/null +++ b/backtesting/MT5/backtest_engine.py @@ -0,0 +1,257 @@ +""" +Backtesting Engine for MetaTrader5 + +This module provides the core backtesting functionality using MT5 historical data. +""" + +from datetime import datetime, timedelta +from typing import Optional, Dict, Any, List +import MetaTrader5 as mt5 +import pandas as pd +import numpy as np +from base_strategy import BaseStrategy + + +class BacktestEngine: + """ + Main backtesting engine that runs strategies on historical data. + """ + + def __init__(self, strategy: BaseStrategy, start_date: datetime, end_date: datetime): + """ + Initialize the backtesting engine. + + Args: + strategy: Strategy instance to backtest + start_date: Start date for backtesting + end_date: End date for backtesting + """ + self.strategy = strategy + self.start_date = start_date + self.end_date = end_date + + # Initialize MT5 connection + if not mt5.initialize(): + raise RuntimeError(f"MT5 initialization failed: {mt5.last_error()}") + + # Indicator handles + self.indicator_handles = {} + self.setup_indicators() + + def setup_indicators(self): + """Setup all required indicators for the strategy.""" + required_indicators = self.strategy.get_required_indicators() + + for indicator_name, params in required_indicators.items(): + handle = None + + if indicator_name.lower() == 'rsi': + handle = mt5.iRSI( + self.strategy.symbol, + self.strategy.timeframe, + params.get('period', 14), + params.get('applied_price', mt5.PRICE_CLOSE) + ) + elif indicator_name.lower() == 'ema': + handle = mt5.iMA( + self.strategy.symbol, + self.strategy.timeframe, + params.get('period', 50), + 0, # shift + mt5.MODE_EMA, + params.get('applied_price', mt5.PRICE_CLOSE) + ) + elif indicator_name.lower() == 'sma': + handle = mt5.iMA( + self.strategy.symbol, + self.strategy.timeframe, + params.get('period', 50), + 0, # shift + mt5.MODE_SMA, + params.get('applied_price', mt5.PRICE_CLOSE) + ) + elif indicator_name.lower() == 'atr': + handle = mt5.iATR( + self.strategy.symbol, + self.strategy.timeframe, + params.get('period', 14) + ) + elif indicator_name.lower() == 'macd': + handle = mt5.iMACD( + self.strategy.symbol, + self.strategy.timeframe, + params.get('fast', 12), + params.get('slow', 26), + params.get('signal', 9), + params.get('applied_price', mt5.PRICE_CLOSE) + ) + + if handle is not None and handle != mt5.INVALID_HANDLE: + self.indicator_handles[indicator_name] = handle + else: + print(f"Warning: Failed to create {indicator_name} indicator") + + def get_indicator_values(self, indicator_name: str, count: int = 1) -> Optional[np.ndarray]: + """ + Get indicator values. + + Args: + indicator_name: Name of the indicator + count: Number of values to retrieve + + Returns: + Array of indicator values or None + """ + if indicator_name not in self.indicator_handles: + return None + + handle = self.indicator_handles[indicator_name] + buffer = np.zeros(count, dtype=float) + + if indicator_name.lower() == 'macd': + # MACD returns 3 buffers + result = mt5.copy_buffer(handle, 0, 0, count) # Main line + if result is None: + return None + return np.array(result) + else: + result = mt5.copy_buffer(handle, 0, 0, count) + if result is None: + return None + return np.array(result) + + def get_bar_data(self, time: datetime) -> Optional[Dict[str, Any]]: + """ + Get bar data and indicator values for a specific time. + + Args: + time: Bar time + + Returns: + Dictionary with bar data and indicators + """ + # Get rates + rates = mt5.copy_rates_from( + self.strategy.symbol, + self.strategy.timeframe, + time, + 1 + ) + + if rates is None or len(rates) == 0: + return None + + rate = rates[0] + + # Get spread + symbol_info = mt5.symbol_info(self.strategy.symbol) + spread = symbol_info.spread if symbol_info else 0 + + # Build bar data + bar_data = { + 'time': datetime.fromtimestamp(rate['time']), + 'open': float(rate['open']), + 'high': float(rate['high']), + 'low': float(rate['low']), + 'close': float(rate['close']), + 'tick_volume': int(rate['tick_volume']), + 'spread': spread, + 'indicators': {} + } + + # Get indicator values + for indicator_name in self.indicator_handles.keys(): + values = self.get_indicator_values(indicator_name, 2) + if values is not None and len(values) >= 1: + bar_data['indicators'][indicator_name] = values[0] + # Also add to top level for convenience + bar_data[indicator_name.lower()] = values[0] + + return bar_data + + def run(self) -> Dict[str, Any]: + """ + Run the backtest. + + Returns: + Dictionary with backtest results and performance metrics + """ + print(f"Starting backtest from {self.start_date} to {self.end_date}") + print(f"Symbol: {self.strategy.symbol}, Timeframe: {self.strategy.timeframe}") + + # Get all bars in the date range + rates = mt5.copy_rates_range( + self.strategy.symbol, + self.strategy.timeframe, + self.start_date, + self.end_date + ) + + if rates is None or len(rates) == 0: + raise ValueError(f"No data available for {self.strategy.symbol} in the specified date range") + + print(f"Processing {len(rates)} bars...") + + # Process each bar + processed_bars = 0 + for i, rate in enumerate(rates): + bar_time = datetime.fromtimestamp(rate['time']) + + # Get full bar data with indicators + bar_data = self.get_bar_data(bar_time) + if bar_data is None: + continue + + # Check stop loss/take profit on current position + if self.strategy.position is not None: + self.strategy.check_stop_loss_take_profit(bar_data['close']) + + # Call strategy on_bar method + try: + self.strategy.on_bar(bar_data) + except Exception as e: + print(f"Error in strategy on_bar at {bar_time}: {e}") + continue + + # Update equity (unrealized P&L) + if self.strategy.position is not None: + if self.strategy.position['type'] == 'BUY': + unrealized_pnl = (bar_data['close'] - self.strategy.position['open_price']) * \ + self.strategy.position['volume'] * 10000 * 10 + else: + unrealized_pnl = (self.strategy.position['open_price'] - bar_data['close']) * \ + self.strategy.position['volume'] * 10000 * 10 + self.strategy.equity = self.strategy.current_balance + unrealized_pnl + else: + self.strategy.equity = self.strategy.current_balance + + processed_bars += 1 + + if processed_bars % 100 == 0: + print(f"Processed {processed_bars}/{len(rates)} bars...") + + # Close any open position at the end + if self.strategy.position is not None: + last_bar = rates[-1] + last_price = float(last_bar['close']) + self.strategy.close_position(last_price) + + print(f"Backtest completed. Processed {processed_bars} bars.") + + # Get performance metrics + metrics = self.strategy.get_performance_metrics() + + # Cleanup + self.cleanup() + + return { + 'metrics': metrics, + 'trades': self.strategy.closed_trades, + 'strategy_name': self.strategy.__class__.__name__ + } + + def cleanup(self): + """Clean up indicator handles and MT5 connection.""" + for handle in self.indicator_handles.values(): + mt5.indicator_release(handle) + mt5.shutdown() diff --git a/backtesting/MT5/base_strategy.py b/backtesting/MT5/base_strategy.py new file mode 100644 index 0000000..05cfd58 --- /dev/null +++ b/backtesting/MT5/base_strategy.py @@ -0,0 +1,262 @@ +""" +Base Strategy Class for MetaTrader5 Backtesting + +This module provides a base class that all trading strategies should inherit from. +Implement your trading logic by overriding the on_bar() method. +""" + +from abc import ABC, abstractmethod +from datetime import datetime +from typing import Optional, Dict, Any +import MetaTrader5 as mt5 + + +class BaseStrategy(ABC): + """ + Base class for all trading strategies. + + Inherit from this class and implement: + - on_bar(): Your trading logic for each bar + - get_parameters(): Return strategy parameters + """ + + def __init__(self, symbol: str, timeframe: int, initial_balance: float = 10000.0): + """ + Initialize the strategy. + + Args: + symbol: Trading symbol (e.g., 'XAUUSD', 'EURUSD') + timeframe: MT5 timeframe constant (e.g., mt5.TIMEFRAME_H1) + initial_balance: Starting account balance + """ + self.symbol = symbol + self.timeframe = timeframe + self.initial_balance = initial_balance + self.current_balance = initial_balance + self.equity = initial_balance + + # Position tracking + self.position = None # {'type': 'BUY'/'SELL', 'volume': float, 'open_price': float, 'open_time': datetime} + self.trades = [] + self.closed_trades = [] + + # Performance metrics + self.max_drawdown = 0.0 + self.peak_equity = initial_balance + self.total_profit = 0.0 + self.total_loss = 0.0 + self.winning_trades = 0 + self.losing_trades = 0 + + # Risk management + self.max_lot_size = 0.1 + self.min_lot_size = 0.01 + self.max_spread = 1000 # in points + self.max_drawdown_percent = 0.2 # 20% max drawdown + + @abstractmethod + def on_bar(self, bar_data: Dict[str, Any]) -> None: + """ + Called on each new bar. Implement your trading logic here. + + Args: + bar_data: Dictionary containing: + - 'time': datetime of the bar + - 'open': float opening price + - 'high': float high price + - 'low': float low price + - 'close': float closing price + - 'tick_volume': int tick volume + - 'spread': int spread in points + - 'rsi': Optional[float] RSI value if requested + - 'ema': Optional[float] EMA value if requested + - 'indicators': Dict with any other requested indicators + """ + pass + + @abstractmethod + def get_parameters(self) -> Dict[str, Any]: + """ + Return strategy parameters for logging/reporting. + + Returns: + Dictionary of parameter names and values + """ + pass + + def get_required_indicators(self) -> Dict[str, Dict[str, Any]]: + """ + Specify which indicators are needed by the strategy. + + Returns: + Dictionary mapping indicator names to their parameters. + Example: { + 'rsi': {'period': 14, 'applied_price': mt5.PRICE_CLOSE}, + 'ema': {'period': 50, 'applied_price': mt5.PRICE_CLOSE} + } + """ + return {} + + def open_position(self, order_type: str, volume: float, price: float, + sl: Optional[float] = None, tp: Optional[float] = None, + comment: str = "") -> bool: + """ + Open a trading position. + + Args: + order_type: 'BUY' or 'SELL' + volume: Lot size + price: Entry price + sl: Stop loss price (optional) + tp: Take profit price (optional) + comment: Trade comment + + Returns: + True if position opened successfully + """ + if self.position is not None: + return False # Position already open + + # Validate volume + volume = max(self.min_lot_size, min(volume, self.max_lot_size)) + + # Calculate margin requirement (simplified) + contract_size = 100000 # Standard lot size + margin_required = volume * contract_size * price * 0.01 # 1% margin (adjust as needed) + + if margin_required > self.equity * 0.9: # Don't use more than 90% of equity + return False + + self.position = { + 'type': order_type, + 'volume': volume, + 'open_price': price, + 'open_time': datetime.now(), + 'sl': sl, + 'tp': tp, + 'comment': comment + } + + return True + + def close_position(self, close_price: float) -> Optional[Dict[str, Any]]: + """ + Close the current position. + + Args: + close_price: Price at which to close + + Returns: + Trade result dictionary or None if no position + """ + if self.position is None: + return None + + # Calculate profit/loss + if self.position['type'] == 'BUY': + pips = (close_price - self.position['open_price']) * 10000 # For 5-digit brokers + profit = pips * self.position['volume'] * 10 # Simplified P&L calculation + else: # SELL + pips = (self.position['open_price'] - close_price) * 10000 + profit = pips * self.position['volume'] * 10 + + trade_result = { + 'type': self.position['type'], + 'volume': self.position['volume'], + 'open_price': self.position['open_price'], + 'close_price': close_price, + 'open_time': self.position['open_time'], + 'close_time': datetime.now(), + 'profit': profit, + 'pips': pips, + 'comment': self.position.get('comment', '') + } + + # Update balance and metrics + self.current_balance += profit + self.equity = self.current_balance + + if profit > 0: + self.winning_trades += 1 + self.total_profit += profit + else: + self.losing_trades += 1 + self.total_loss += abs(profit) + + # Update drawdown + if self.equity > self.peak_equity: + self.peak_equity = self.equity + + drawdown = (self.peak_equity - self.equity) / self.peak_equity + if drawdown > self.max_drawdown: + self.max_drawdown = drawdown + + self.closed_trades.append(trade_result) + self.position = None + + return trade_result + + def check_stop_loss_take_profit(self, current_price: float) -> bool: + """ + Check if stop loss or take profit should be triggered. + + Args: + current_price: Current market price + + Returns: + True if position was closed + """ + if self.position is None: + return False + + should_close = False + + if self.position['type'] == 'BUY': + if self.position.get('sl') and current_price <= self.position['sl']: + should_close = True + if self.position.get('tp') and current_price >= self.position['tp']: + should_close = True + else: # SELL + if self.position.get('sl') and current_price >= self.position['sl']: + should_close = True + if self.position.get('tp') and current_price <= self.position['tp']: + should_close = True + + if should_close: + self.close_position(current_price) + return True + + return False + + def get_performance_metrics(self) -> Dict[str, Any]: + """ + Calculate and return performance metrics. + + Returns: + Dictionary with performance statistics + """ + total_trades = len(self.closed_trades) + win_rate = (self.winning_trades / total_trades * 100) if total_trades > 0 else 0 + + avg_win = (self.total_profit / self.winning_trades) if self.winning_trades > 0 else 0 + avg_loss = (self.total_loss / self.losing_trades) if self.losing_trades > 0 else 0 + profit_factor = (self.total_profit / self.total_loss) if self.total_loss > 0 else 0 + + total_return = ((self.equity - self.initial_balance) / self.initial_balance) * 100 + + return { + 'initial_balance': self.initial_balance, + 'final_balance': self.equity, + 'total_return_pct': total_return, + 'total_trades': total_trades, + 'winning_trades': self.winning_trades, + 'losing_trades': self.losing_trades, + 'win_rate_pct': win_rate, + 'total_profit': self.total_profit, + 'total_loss': self.total_loss, + 'profit_factor': profit_factor, + 'avg_win': avg_win, + 'avg_loss': avg_loss, + 'max_drawdown_pct': self.max_drawdown * 100, + 'parameters': self.get_parameters() + } diff --git a/backtesting/MT5/example_strategies.py b/backtesting/MT5/example_strategies.py new file mode 100644 index 0000000..895ab88 --- /dev/null +++ b/backtesting/MT5/example_strategies.py @@ -0,0 +1,256 @@ +""" +Example Trading Strategies + +These are example implementations of trading strategies that you can use as templates +or modify for your own strategies. +""" + +from datetime import datetime +from typing import Dict, Any, Optional +import MetaTrader5 as mt5 +from base_strategy import BaseStrategy + + +class RSIScalpingStrategy(BaseStrategy): + """ + RSI Scalping Strategy - Example implementation + + Entry: + - Buy when RSI crosses above oversold level + - Sell when RSI crosses below overbought level + + Exit: + - RSI reaches target levels + - Stop loss and take profit + """ + + def __init__(self, symbol: str, timeframe: int, initial_balance: float = 10000.0, + rsi_period: int = 14, rsi_overbought: float = 70, rsi_oversold: float = 30, + rsi_target_buy: float = 80, rsi_target_sell: float = 20, + lot_size: float = 0.1, stop_loss_pips: int = 50, take_profit_pips: int = 100): + super().__init__(symbol, timeframe, initial_balance) + + self.rsi_period = rsi_period + self.rsi_overbought = rsi_overbought + self.rsi_oversold = rsi_oversold + self.rsi_target_buy = rsi_target_buy + self.rsi_target_sell = rsi_target_sell + self.lot_size = lot_size + self.stop_loss_pips = stop_loss_pips + self.take_profit_pips = take_profit_pips + + # Track previous RSI for crossover detection + self.prev_rsi = None + + def get_required_indicators(self) -> Dict[str, Dict[str, Any]]: + return { + 'rsi': { + 'period': self.rsi_period, + 'applied_price': mt5.PRICE_CLOSE + } + } + + def on_bar(self, bar_data: Dict[str, Any]) -> None: + rsi = bar_data.get('rsi') + if rsi is None: + return + + current_price = bar_data['close'] + spread = bar_data.get('spread', 0) + + # Check spread + if spread > self.max_spread: + return + + # Check if we have a position + if self.position is not None: + # Check exit conditions + if self.position['type'] == 'BUY': + if rsi >= self.rsi_target_buy: + self.close_position(current_price) + elif self.position['type'] == 'SELL': + if rsi <= self.rsi_target_sell: + self.close_position(current_price) + else: + # Check entry conditions + if self.prev_rsi is not None: + # Buy signal: RSI crosses above oversold + if self.prev_rsi <= self.rsi_oversold and rsi > self.rsi_oversold: + sl = current_price - (self.stop_loss_pips / 10000) + tp = current_price + (self.take_profit_pips / 10000) + self.open_position('BUY', self.lot_size, current_price, sl, tp, 'RSI Scalping Buy') + + # Sell signal: RSI crosses below overbought + elif self.prev_rsi >= self.rsi_overbought and rsi < self.rsi_overbought: + sl = current_price + (self.stop_loss_pips / 10000) + tp = current_price - (self.take_profit_pips / 10000) + self.open_position('SELL', self.lot_size, current_price, sl, tp, 'RSI Scalping Sell') + + self.prev_rsi = rsi + + def get_parameters(self) -> Dict[str, Any]: + return { + 'rsi_period': self.rsi_period, + 'rsi_overbought': self.rsi_overbought, + 'rsi_oversold': self.rsi_oversold, + 'rsi_target_buy': self.rsi_target_buy, + 'rsi_target_sell': self.rsi_target_sell, + 'lot_size': self.lot_size, + 'stop_loss_pips': self.stop_loss_pips, + 'take_profit_pips': self.take_profit_pips + } + + +class EMAStrategy(BaseStrategy): + """ + EMA Crossover Strategy + + Entry: + - Buy when price crosses above EMA + - Sell when price crosses below EMA + + Exit: + - Opposite crossover + - Stop loss and take profit + """ + + def __init__(self, symbol: str, timeframe: int, initial_balance: float = 10000.0, + ema_period: int = 50, lot_size: float = 0.1, + stop_loss_pips: int = 50, take_profit_pips: int = 100): + super().__init__(symbol, timeframe, initial_balance) + + self.ema_period = ema_period + self.lot_size = lot_size + self.stop_loss_pips = stop_loss_pips + self.take_profit_pips = take_profit_pips + + self.prev_price = None + self.prev_ema = None + + def get_required_indicators(self) -> Dict[str, Dict[str, Any]]: + return { + 'ema': { + 'period': self.ema_period, + 'applied_price': mt5.PRICE_CLOSE + } + } + + def on_bar(self, bar_data: Dict[str, Any]) -> None: + ema = bar_data.get('ema') + current_price = bar_data['close'] + + if ema is None: + return + + # Check if we have a position + if self.position is not None: + # Exit on opposite crossover + if self.position['type'] == 'BUY' and current_price < ema: + self.close_position(current_price) + elif self.position['type'] == 'SELL' and current_price > ema: + self.close_position(current_price) + else: + # Check entry conditions + if self.prev_price is not None and self.prev_ema is not None: + # Buy signal: price crosses above EMA + if self.prev_price <= self.prev_ema and current_price > ema: + sl = current_price - (self.stop_loss_pips / 10000) + tp = current_price + (self.take_profit_pips / 10000) + self.open_position('BUY', self.lot_size, current_price, sl, tp, 'EMA Crossover Buy') + + # Sell signal: price crosses below EMA + elif self.prev_price >= self.prev_ema and current_price < ema: + sl = current_price + (self.stop_loss_pips / 10000) + tp = current_price - (self.take_profit_pips / 10000) + self.open_position('SELL', self.lot_size, current_price, sl, tp, 'EMA Crossover Sell') + + self.prev_price = current_price + self.prev_ema = ema + + def get_parameters(self) -> Dict[str, Any]: + return { + 'ema_period': self.ema_period, + 'lot_size': self.lot_size, + 'stop_loss_pips': self.stop_loss_pips, + 'take_profit_pips': self.take_profit_pips + } + + +class RSIReversalStrategy(BaseStrategy): + """ + RSI Reversal Strategy - Similar to your MQL5 RSI Reversal strategies + + Entry: + - Buy when RSI is oversold and starts rising + - Sell when RSI is overbought and starts falling + + Exit: + - RSI reaches neutral level + - Stop loss and take profit + """ + + def __init__(self, symbol: str, timeframe: int, initial_balance: float = 10000.0, + rsi_period: int = 14, rsi_overbought: float = 70, rsi_oversold: float = 30, + rsi_exit: float = 50, lot_size: float = 0.1, + stop_loss_pips: int = 50, take_profit_pips: int = 100): + super().__init__(symbol, timeframe, initial_balance) + + self.rsi_period = rsi_period + self.rsi_overbought = rsi_overbought + self.rsi_oversold = rsi_oversold + self.rsi_exit = rsi_exit + self.lot_size = lot_size + self.stop_loss_pips = stop_loss_pips + self.take_profit_pips = take_profit_pips + + self.prev_rsi = None + + def get_required_indicators(self) -> Dict[str, Dict[str, Any]]: + return { + 'rsi': { + 'period': self.rsi_period, + 'applied_price': mt5.PRICE_CLOSE + } + } + + def on_bar(self, bar_data: Dict[str, Any]) -> None: + rsi = bar_data.get('rsi') + if rsi is None: + return + + current_price = bar_data['close'] + + # Check if we have a position + if self.position is not None: + # Exit when RSI reaches neutral level + if self.position['type'] == 'BUY' and rsi >= self.rsi_exit: + self.close_position(current_price) + elif self.position['type'] == 'SELL' and rsi <= self.rsi_exit: + self.close_position(current_price) + else: + # Check entry conditions + if self.prev_rsi is not None: + # Buy signal: RSI was oversold and now rising + if self.prev_rsi < self.rsi_oversold and rsi > self.prev_rsi: + sl = current_price - (self.stop_loss_pips / 10000) + tp = current_price + (self.take_profit_pips / 10000) + self.open_position('BUY', self.lot_size, current_price, sl, tp, 'RSI Reversal Buy') + + # Sell signal: RSI was overbought and now falling + elif self.prev_rsi > self.rsi_overbought and rsi < self.prev_rsi: + sl = current_price + (self.stop_loss_pips / 10000) + tp = current_price - (self.take_profit_pips / 10000) + self.open_position('SELL', self.lot_size, current_price, sl, tp, 'RSI Reversal Sell') + + self.prev_rsi = rsi + + def get_parameters(self) -> Dict[str, Any]: + return { + 'rsi_period': self.rsi_period, + 'rsi_overbought': self.rsi_overbought, + 'rsi_oversold': self.rsi_oversold, + 'rsi_exit': self.rsi_exit, + 'lot_size': self.lot_size, + 'stop_loss_pips': self.stop_loss_pips, + 'take_profit_pips': self.take_profit_pips + } diff --git a/backtesting/MT5/example_usage.py b/backtesting/MT5/example_usage.py new file mode 100644 index 0000000..6b32091 --- /dev/null +++ b/backtesting/MT5/example_usage.py @@ -0,0 +1,196 @@ +""" +Example usage of the backtesting framework + +This script demonstrates how to use the framework programmatically +without using the command-line interface. +""" + +from datetime import datetime +import MetaTrader5 as mt5 +from backtest_engine import BacktestEngine +from example_strategies import RSIReversalStrategy, RSIScalpingStrategy, EMAStrategy +from performance_analyzer import PerformanceAnalyzer + + +def example_rsi_reversal(): + """Example: RSI Reversal Strategy backtest""" + print("="*60) + print("Example 1: RSI Reversal Strategy") + print("="*60) + + # Create strategy + strategy = RSIReversalStrategy( + symbol='XAUUSD', + timeframe=mt5.TIMEFRAME_H1, + initial_balance=10000.0, + rsi_period=14, + rsi_overbought=70, + rsi_oversold=30, + rsi_exit=50, + lot_size=0.1, + stop_loss_pips=50, + take_profit_pips=100 + ) + + # Run backtest + engine = BacktestEngine( + strategy, + start_date=datetime(2023, 1, 1), + end_date=datetime(2024, 1, 1) + ) + + results = engine.run() + + # Analyze results + analyzer = PerformanceAnalyzer(results) + analyzer.generate_report('example_results/rsi_reversal') + + return results + + +def example_rsi_scalping(): + """Example: RSI Scalping Strategy backtest""" + print("\n" + "="*60) + print("Example 2: RSI Scalping Strategy") + print("="*60) + + # Create strategy + strategy = RSIScalpingStrategy( + symbol='EURUSD', + timeframe=mt5.TIMEFRAME_M15, + initial_balance=10000.0, + rsi_period=14, + rsi_overbought=71, + rsi_oversold=57, + rsi_target_buy=80, + rsi_target_sell=20, + lot_size=0.1, + stop_loss_pips=30, + take_profit_pips=50 + ) + + # Run backtest + engine = BacktestEngine( + strategy, + start_date=datetime(2023, 6, 1), + end_date=datetime(2023, 12, 31) + ) + + results = engine.run() + + # Analyze results + analyzer = PerformanceAnalyzer(results) + analyzer.generate_report('example_results/rsi_scalping') + + return results + + +def example_ema_crossover(): + """Example: EMA Crossover Strategy backtest""" + print("\n" + "="*60) + print("Example 3: EMA Crossover Strategy") + print("="*60) + + # Create strategy + strategy = EMAStrategy( + symbol='BTCUSD', + timeframe=mt5.TIMEFRAME_H4, + initial_balance=10000.0, + ema_period=50, + lot_size=0.1, + stop_loss_pips=100, + take_profit_pips=200 + ) + + # Run backtest + engine = BacktestEngine( + strategy, + start_date=datetime(2023, 1, 1), + end_date=datetime(2024, 1, 1) + ) + + results = engine.run() + + # Analyze results + analyzer = PerformanceAnalyzer(results) + analyzer.generate_report('example_results/ema_crossover') + + return results + + +def compare_strategies(): + """Compare multiple strategies""" + print("\n" + "="*60) + print("Example 4: Strategy Comparison") + print("="*60) + + strategies = [ + ('RSI Reversal', RSIReversalStrategy( + 'XAUUSD', mt5.TIMEFRAME_H1, 10000.0, + rsi_period=14, rsi_overbought=70, rsi_oversold=30 + )), + ('RSI Scalping', RSIScalpingStrategy( + 'XAUUSD', mt5.TIMEFRAME_H1, 10000.0, + rsi_period=14, rsi_overbought=71, rsi_oversold=57 + )), + ('EMA Crossover', EMAStrategy( + 'XAUUSD', mt5.TIMEFRAME_H1, 10000.0, + ema_period=50 + )) + ] + + start_date = datetime(2023, 1, 1) + end_date = datetime(2024, 1, 1) + + results_list = [] + + for name, strategy in strategies: + print(f"\nBacktesting {name}...") + engine = BacktestEngine(strategy, start_date, end_date) + results = engine.run() + results_list.append((name, results)) + + analyzer = PerformanceAnalyzer(results) + print(f"\n{name} Results:") + analyzer.print_summary() + + # Print comparison + print("\n" + "="*60) + print("STRATEGY COMPARISON") + print("="*60) + print(f"{'Strategy':<20} {'Return %':<12} {'Win Rate %':<12} {'Profit Factor':<15} {'Max DD %':<10}") + print("-"*60) + + for name, results in results_list: + metrics = results['metrics'] + print(f"{name:<20} {metrics['total_return_pct']:>10.2f}% " + f"{metrics['win_rate_pct']:>10.2f}% " + f"{metrics['profit_factor']:>13.2f} " + f"{metrics['max_drawdown_pct']:>8.2f}%") + + +if __name__ == '__main__': + # Initialize MT5 (will be done by BacktestEngine, but good to check) + if not mt5.initialize(): + print("MT5 initialization failed. Please ensure MT5 is installed and running.") + exit(1) + + print("MetaTrader5 Python Backtesting Framework - Examples") + print("="*60) + + # Run examples (comment out the ones you don't want to run) + + # Example 1: RSI Reversal + # example_rsi_reversal() + + # Example 2: RSI Scalping + # example_rsi_scalping() + + # Example 3: EMA Crossover + # example_ema_crossover() + + # Example 4: Compare strategies + compare_strategies() + + mt5.shutdown() + print("\nExamples completed!") diff --git a/backtesting/MT5/performance_analyzer.py b/backtesting/MT5/performance_analyzer.py new file mode 100644 index 0000000..2e9bdc1 --- /dev/null +++ b/backtesting/MT5/performance_analyzer.py @@ -0,0 +1,212 @@ +""" +Performance Analysis and Reporting + +This module provides tools for analyzing backtest results and generating reports. +""" + +from typing import Dict, Any, List +import pandas as pd +import matplotlib.pyplot as plt +import numpy as np +from datetime import datetime + + +class PerformanceAnalyzer: + """ + Analyzes backtest performance and generates reports. + """ + + def __init__(self, backtest_results: Dict[str, Any]): + """ + Initialize with backtest results. + + Args: + backtest_results: Results dictionary from BacktestEngine.run() + """ + self.results = backtest_results + self.metrics = backtest_results['metrics'] + self.trades = backtest_results['trades'] + self.strategy_name = backtest_results['strategy_name'] + + def print_summary(self): + """Print a summary of the backtest results.""" + print("\n" + "="*60) + print(f"BACKTEST SUMMARY: {self.strategy_name}") + print("="*60) + print(f"\nInitial Balance: ${self.metrics['initial_balance']:,.2f}") + print(f"Final Balance: ${self.metrics['final_balance']:,.2f}") + print(f"Total Return: {self.metrics['total_return_pct']:.2f}%") + print(f"\nTotal Trades: {self.metrics['total_trades']}") + print(f"Winning Trades: {self.metrics['winning_trades']}") + print(f"Losing Trades: {self.metrics['losing_trades']}") + print(f"Win Rate: {self.metrics['win_rate_pct']:.2f}%") + print(f"\nTotal Profit: ${self.metrics['total_profit']:,.2f}") + print(f"Total Loss: ${self.metrics['total_loss']:,.2f}") + print(f"Profit Factor: {self.metrics['profit_factor']:.2f}") + print(f"\nAverage Win: ${self.metrics['avg_win']:,.2f}") + print(f"Average Loss: ${self.metrics['avg_loss']:,.2f}") + print(f"Max Drawdown: {self.metrics['max_drawdown_pct']:.2f}%") + + if self.metrics.get('parameters'): + print(f"\nStrategy Parameters:") + for key, value in self.metrics['parameters'].items(): + print(f" {key}: {value}") + + print("="*60 + "\n") + + def get_trades_dataframe(self) -> pd.DataFrame: + """Convert trades list to pandas DataFrame.""" + if not self.trades: + return pd.DataFrame() + + df = pd.DataFrame(self.trades) + df['open_time'] = pd.to_datetime(df['open_time']) + df['close_time'] = pd.to_datetime(df['close_time']) + df['duration'] = df['close_time'] - df['open_time'] + + return df + + def plot_equity_curve(self, save_path: str = None): + """ + Plot equity curve over time. + + Args: + save_path: Optional path to save the plot + """ + if not self.trades: + print("No trades to plot") + return + + df = self.get_trades_dataframe() + df = df.sort_values('close_time') + + # Calculate cumulative equity + cumulative_profit = df['profit'].cumsum() + equity_curve = self.metrics['initial_balance'] + cumulative_profit + + plt.figure(figsize=(12, 6)) + plt.plot(df['close_time'], equity_curve, linewidth=2, label='Equity') + plt.axhline(y=self.metrics['initial_balance'], color='r', linestyle='--', label='Initial Balance') + plt.xlabel('Time') + plt.ylabel('Equity ($)') + plt.title(f'Equity Curve - {self.strategy_name}') + plt.legend() + plt.grid(True, alpha=0.3) + plt.tight_layout() + + if save_path: + plt.savefig(save_path, dpi=300, bbox_inches='tight') + print(f"Equity curve saved to {save_path}") + else: + plt.show() + + def plot_drawdown(self, save_path: str = None): + """ + Plot drawdown over time. + + Args: + save_path: Optional path to save the plot + """ + if not self.trades: + print("No trades to plot") + return + + df = self.get_trades_dataframe() + df = df.sort_values('close_time') + + # Calculate cumulative equity + cumulative_profit = df['profit'].cumsum() + equity_curve = self.metrics['initial_balance'] + cumulative_profit + + # Calculate running maximum + running_max = equity_curve.expanding().max() + drawdown = (equity_curve - running_max) / running_max * 100 + + plt.figure(figsize=(12, 6)) + plt.fill_between(df['close_time'], drawdown, 0, alpha=0.3, color='red', label='Drawdown') + plt.plot(df['close_time'], drawdown, linewidth=1, color='darkred') + plt.xlabel('Time') + plt.ylabel('Drawdown (%)') + plt.title(f'Drawdown Chart - {self.strategy_name}') + plt.legend() + plt.grid(True, alpha=0.3) + plt.tight_layout() + + if save_path: + plt.savefig(save_path, dpi=300, bbox_inches='tight') + print(f"Drawdown chart saved to {save_path}") + else: + plt.show() + + def plot_monthly_returns(self, save_path: str = None): + """ + Plot monthly returns. + + Args: + save_path: Optional path to save the plot + """ + if not self.trades: + print("No trades to plot") + return + + df = self.get_trades_dataframe() + df = df.sort_values('close_time') + + # Group by month + df['month'] = df['close_time'].dt.to_period('M') + monthly_returns = df.groupby('month')['profit'].sum() + monthly_returns_pct = (monthly_returns / self.metrics['initial_balance']) * 100 + + plt.figure(figsize=(12, 6)) + colors = ['green' if x > 0 else 'red' for x in monthly_returns_pct] + plt.bar(range(len(monthly_returns_pct)), monthly_returns_pct, color=colors, alpha=0.7) + plt.xlabel('Month') + plt.ylabel('Return (%)') + plt.title(f'Monthly Returns - {self.strategy_name}') + plt.xticks(range(len(monthly_returns_pct)), [str(x) for x in monthly_returns_pct.index], rotation=45) + plt.axhline(y=0, color='black', linestyle='-', linewidth=0.5) + plt.grid(True, alpha=0.3, axis='y') + plt.tight_layout() + + if save_path: + plt.savefig(save_path, dpi=300, bbox_inches='tight') + print(f"Monthly returns chart saved to {save_path}") + else: + plt.show() + + def export_trades_csv(self, filepath: str): + """ + Export trades to CSV file. + + Args: + filepath: Path to save CSV file + """ + df = self.get_trades_dataframe() + df.to_csv(filepath, index=False) + print(f"Trades exported to {filepath}") + + def generate_report(self, output_dir: str = "backtest_results"): + """ + Generate a comprehensive report with all charts and data. + + Args: + output_dir: Directory to save report files + """ + import os + os.makedirs(output_dir, exist_ok=True) + + timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") + prefix = f"{self.strategy_name}_{timestamp}" + + # Print summary + self.print_summary() + + # Generate plots + self.plot_equity_curve(os.path.join(output_dir, f"{prefix}_equity_curve.png")) + self.plot_drawdown(os.path.join(output_dir, f"{prefix}_drawdown.png")) + self.plot_monthly_returns(os.path.join(output_dir, f"{prefix}_monthly_returns.png")) + + # Export trades + self.export_trades_csv(os.path.join(output_dir, f"{prefix}_trades.csv")) + + print(f"\nReport generated in {output_dir}/") diff --git a/backtesting/MT5/requirements.txt b/backtesting/MT5/requirements.txt new file mode 100644 index 0000000..f92e836 --- /dev/null +++ b/backtesting/MT5/requirements.txt @@ -0,0 +1,7 @@ +MetaTrader5>=5.0.45 +pandas>=1.3.0 +numpy>=1.21.0 +matplotlib>=3.4.0 +scipy>=1.7.0 +ta-lib>=0.4.0 +python-dateutil>=2.8.0 diff --git a/backtesting/MT5/run_backtest.py b/backtesting/MT5/run_backtest.py new file mode 100644 index 0000000..c7eb29d --- /dev/null +++ b/backtesting/MT5/run_backtest.py @@ -0,0 +1,149 @@ +""" +Main script to run backtests + +Example usage: + python run_backtest.py --strategy RSIReversalStrategy --symbol XAUUSD --start 2023-01-01 --end 2024-01-01 +""" + +import argparse +from datetime import datetime +import MetaTrader5 as mt5 +from backtest_engine import BacktestEngine +from example_strategies import RSIScalpingStrategy, EMAStrategy, RSIReversalStrategy +from performance_analyzer import PerformanceAnalyzer +from base_strategy import BaseStrategy + + +def parse_args(): + """Parse command line arguments.""" + parser = argparse.ArgumentParser(description='Run backtest on trading strategy') + + parser.add_argument('--strategy', type=str, required=True, + choices=['RSIScalpingStrategy', 'EMAStrategy', 'RSIReversalStrategy'], + help='Strategy to backtest') + parser.add_argument('--symbol', type=str, default='XAUUSD', + help='Trading symbol (default: XAUUSD)') + parser.add_argument('--timeframe', type=str, default='H1', + choices=['M1', 'M5', 'M15', 'M30', 'H1', 'H4', 'D1'], + help='Timeframe (default: H1)') + parser.add_argument('--start', type=str, required=True, + help='Start date (YYYY-MM-DD)') + parser.add_argument('--end', type=str, required=True, + help='End date (YYYY-MM-DD)') + parser.add_argument('--balance', type=float, default=10000.0, + help='Initial balance (default: 10000)') + parser.add_argument('--output', type=str, default='backtest_results', + help='Output directory for results (default: backtest_results)') + + # Strategy-specific parameters + parser.add_argument('--rsi-period', type=int, default=14, + help='RSI period (default: 14)') + parser.add_argument('--rsi-overbought', type=float, default=70.0, + help='RSI overbought level (default: 70)') + parser.add_argument('--rsi-oversold', type=float, default=30.0, + help='RSI oversold level (default: 30)') + parser.add_argument('--ema-period', type=int, default=50, + help='EMA period (default: 50)') + parser.add_argument('--lot-size', type=float, default=0.1, + help='Lot size (default: 0.1)') + parser.add_argument('--stop-loss', type=int, default=50, + help='Stop loss in pips (default: 50)') + parser.add_argument('--take-profit', type=int, default=100, + help='Take profit in pips (default: 100)') + + return parser.parse_args() + + +def get_timeframe(timeframe_str: str) -> int: + """Convert timeframe string to MT5 constant.""" + timeframe_map = { + 'M1': mt5.TIMEFRAME_M1, + 'M5': mt5.TIMEFRAME_M5, + 'M15': mt5.TIMEFRAME_M15, + 'M30': mt5.TIMEFRAME_M30, + 'H1': mt5.TIMEFRAME_H1, + 'H4': mt5.TIMEFRAME_H4, + 'D1': mt5.TIMEFRAME_D1 + } + return timeframe_map.get(timeframe_str, mt5.TIMEFRAME_H1) + + +def create_strategy(strategy_name: str, symbol: str, timeframe: int, + initial_balance: float, args) -> BaseStrategy: + """Create strategy instance based on name.""" + if strategy_name == 'RSIScalpingStrategy': + return RSIScalpingStrategy( + symbol=symbol, + timeframe=timeframe, + initial_balance=initial_balance, + rsi_period=args.rsi_period, + rsi_overbought=args.rsi_overbought, + rsi_oversold=args.rsi_oversold, + lot_size=args.lot_size, + stop_loss_pips=args.stop_loss, + take_profit_pips=args.take_profit + ) + elif strategy_name == 'EMAStrategy': + return EMAStrategy( + symbol=symbol, + timeframe=timeframe, + initial_balance=initial_balance, + ema_period=args.ema_period, + lot_size=args.lot_size, + stop_loss_pips=args.stop_loss, + take_profit_pips=args.take_profit + ) + elif strategy_name == 'RSIReversalStrategy': + return RSIReversalStrategy( + symbol=symbol, + timeframe=timeframe, + initial_balance=initial_balance, + rsi_period=args.rsi_period, + rsi_overbought=args.rsi_overbought, + rsi_oversold=args.rsi_oversold, + lot_size=args.lot_size, + stop_loss_pips=args.stop_loss, + take_profit_pips=args.take_profit + ) + else: + raise ValueError(f"Unknown strategy: {strategy_name}") + + +def main(): + """Main function to run backtest.""" + args = parse_args() + + # Parse dates + start_date = datetime.strptime(args.start, '%Y-%m-%d') + end_date = datetime.strptime(args.end, '%Y-%m-%d') + + # Get timeframe + timeframe = get_timeframe(args.timeframe) + + # Create strategy + print(f"Creating {args.strategy} strategy...") + strategy = create_strategy( + args.strategy, + args.symbol, + timeframe, + args.balance, + args + ) + + # Create and run backtest + print("Initializing backtest engine...") + engine = BacktestEngine(strategy, start_date, end_date) + + print("Running backtest...") + results = engine.run() + + # Analyze results + print("Analyzing results...") + analyzer = PerformanceAnalyzer(results) + analyzer.generate_report(args.output) + + print("\nBacktest completed successfully!") + + +if __name__ == '__main__': + main() diff --git a/backtesting/MT5/test_setup.py b/backtesting/MT5/test_setup.py new file mode 100644 index 0000000..d268434 --- /dev/null +++ b/backtesting/MT5/test_setup.py @@ -0,0 +1,98 @@ +""" +Test script to verify MT5 connection and setup + +Run this script first to ensure everything is configured correctly. +""" + +import MetaTrader5 as mt5 +from datetime import datetime, timedelta + + +def test_mt5_connection(): + """Test MT5 connection and basic functionality""" + print("Testing MetaTrader5 Connection...") + print("="*60) + + # Initialize MT5 + if not mt5.initialize(): + print(f"ERROR: MT5 initialization failed") + print(f"Error code: {mt5.last_error()}") + print("\nTroubleshooting:") + print("1. Make sure MetaTrader5 is installed") + print("2. Make sure MT5 is running") + print("3. Try logging into MT5 manually first") + return False + + print("✓ MT5 initialized successfully") + + # Get account info + account_info = mt5.account_info() + if account_info is None: + print("WARNING: Could not get account info") + else: + print(f"✓ Account: {account_info.login}") + print(f" Server: {account_info.server}") + print(f" Balance: ${account_info.balance:.2f}") + + # Test symbol access + test_symbols = ['XAUUSD', 'EURUSD', 'BTCUSD'] + print("\nTesting symbol access...") + + for symbol in test_symbols: + symbol_info = mt5.symbol_info(symbol) + if symbol_info is None: + print(f"✗ {symbol}: Not available") + else: + print(f"✓ {symbol}: Available") + print(f" Bid: {symbol_info.bid:.5f}, Ask: {symbol_info.ask:.5f}") + print(f" Spread: {symbol_info.spread} points") + + # Test historical data + print("\nTesting historical data retrieval...") + symbol = 'XAUUSD' + timeframe = mt5.TIMEFRAME_H1 + end_date = datetime.now() + start_date = end_date - timedelta(days=7) + + rates = mt5.copy_rates_range(symbol, timeframe, start_date, end_date) + if rates is None or len(rates) == 0: + print(f"✗ Could not retrieve historical data for {symbol}") + print(" Make sure you have historical data in MT5") + else: + print(f"✓ Retrieved {len(rates)} bars for {symbol}") + print(f" Date range: {datetime.fromtimestamp(rates[0]['time'])} to {datetime.fromtimestamp(rates[-1]['time'])}") + + # Test indicator creation + print("\nTesting indicator creation...") + rsi_handle = mt5.iRSI(symbol, timeframe, 14, mt5.PRICE_CLOSE) + if rsi_handle == mt5.INVALID_HANDLE: + print("✗ Failed to create RSI indicator") + else: + print("✓ RSI indicator created successfully") + # Get RSI values + rsi_values = mt5.copy_buffer(rsi_handle, 0, 0, 10) + if rsi_values is not None: + print(f" Latest RSI values: {rsi_values[-3:]}") + mt5.indicator_release(rsi_handle) + + ema_handle = mt5.iMA(symbol, timeframe, 50, 0, mt5.MODE_EMA, mt5.PRICE_CLOSE) + if ema_handle == mt5.INVALID_HANDLE: + print("✗ Failed to create EMA indicator") + else: + print("✓ EMA indicator created successfully") + mt5.indicator_release(ema_handle) + + # Cleanup + mt5.shutdown() + + print("\n" + "="*60) + print("Setup test completed!") + print("="*60) + + return True + + +if __name__ == '__main__': + success = test_mt5_connection() + if not success: + exit(1) diff --git a/backtesting/own/README.md b/backtesting/own/README.md new file mode 100644 index 0000000..311c8dd --- /dev/null +++ b/backtesting/own/README.md @@ -0,0 +1 @@ +PLACEHOLDER \ No newline at end of file diff --git a/DarvasBoxXAUUSD/main.mq5 b/frontline/MQL5/DarvasBoxXAUUSD/main.mq5 similarity index 100% rename from DarvasBoxXAUUSD/main.mq5 rename to frontline/MQL5/DarvasBoxXAUUSD/main.mq5 diff --git a/DarvasBoxXAUUSD/test-balance.jpg b/frontline/MQL5/DarvasBoxXAUUSD/test-balance.jpg similarity index 100% rename from DarvasBoxXAUUSD/test-balance.jpg rename to frontline/MQL5/DarvasBoxXAUUSD/test-balance.jpg diff --git a/EMASlopeDistanceCocktailXAUUSD/main.mq5 b/frontline/MQL5/EMASlopeDistanceCocktailXAUUSD/main.mq5 similarity index 100% rename from EMASlopeDistanceCocktailXAUUSD/main.mq5 rename to frontline/MQL5/EMASlopeDistanceCocktailXAUUSD/main.mq5 diff --git a/EMASlopeDistanceCocktailXAUUSD/test-balance.jpg b/frontline/MQL5/EMASlopeDistanceCocktailXAUUSD/test-balance.jpg similarity index 100% rename from EMASlopeDistanceCocktailXAUUSD/test-balance.jpg rename to frontline/MQL5/EMASlopeDistanceCocktailXAUUSD/test-balance.jpg diff --git a/RSICrossOverReversalXAUUSD/main.mq5 b/frontline/MQL5/RSICrossOverReversalXAUUSD/main.mq5 similarity index 100% rename from RSICrossOverReversalXAUUSD/main.mq5 rename to frontline/MQL5/RSICrossOverReversalXAUUSD/main.mq5 diff --git a/RSICrossOverReversalXAUUSD/test-balance.jpg b/frontline/MQL5/RSICrossOverReversalXAUUSD/test-balance.jpg similarity index 100% rename from RSICrossOverReversalXAUUSD/test-balance.jpg rename to frontline/MQL5/RSICrossOverReversalXAUUSD/test-balance.jpg diff --git a/RSIFollowReverseEMACrossOverBTCUSD/main.mq5 b/frontline/MQL5/RSIFollowReverseEMACrossOverBTCUSD/main.mq5 similarity index 100% rename from RSIFollowReverseEMACrossOverBTCUSD/main.mq5 rename to frontline/MQL5/RSIFollowReverseEMACrossOverBTCUSD/main.mq5 diff --git a/RSIFollowReverseEMACrossOverBTCUSD/test-balance.jpg b/frontline/MQL5/RSIFollowReverseEMACrossOverBTCUSD/test-balance.jpg similarity index 100% rename from RSIFollowReverseEMACrossOverBTCUSD/test-balance.jpg rename to frontline/MQL5/RSIFollowReverseEMACrossOverBTCUSD/test-balance.jpg diff --git a/RSIMidPointHijackXAUUSD/main.mq5 b/frontline/MQL5/RSIMidPointHijackXAUUSD/main.mq5 similarity index 100% rename from RSIMidPointHijackXAUUSD/main.mq5 rename to frontline/MQL5/RSIMidPointHijackXAUUSD/main.mq5 diff --git a/RSIMidPointHijackXAUUSD/test-balance.jpg b/frontline/MQL5/RSIMidPointHijackXAUUSD/test-balance.jpg similarity index 100% rename from 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similarity index 100% rename from RSIScalpingXAGUSD/main.mq5 rename to frontline/MQL5/RSIScalpingXAGUSD/main.mq5 diff --git a/RSIScalpingXAGUSD/test-balance.png b/frontline/MQL5/RSIScalpingXAGUSD/test-balance.png similarity index 100% rename from RSIScalpingXAGUSD/test-balance.png rename to frontline/MQL5/RSIScalpingXAGUSD/test-balance.png diff --git a/RSIScalpingXAUUSD/main.mq5 b/frontline/MQL5/RSIScalpingXAUUSD/main.mq5 similarity index 100% rename from RSIScalpingXAUUSD/main.mq5 rename to frontline/MQL5/RSIScalpingXAUUSD/main.mq5 diff --git a/RSIScalpingXAUUSD/test-balance.png b/frontline/MQL5/RSIScalpingXAUUSD/test-balance.png similarity index 100% rename from RSIScalpingXAUUSD/test-balance.png rename to frontline/MQL5/RSIScalpingXAUUSD/test-balance.png diff --git a/SSEEMARSICocktail/README.md b/frontline/tradingview/SSEEMARSICocktail/README.md similarity index 100% rename from SSEEMARSICocktail/README.md rename to frontline/tradingview/SSEEMARSICocktail/README.md diff --git a/SSEEMARSICocktail/main.pine b/frontline/tradingview/SSEEMARSICocktail/main.pine similarity index 100% rename from SSEEMARSICocktail/main.pine rename to frontline/tradingview/SSEEMARSICocktail/main.pine diff --git a/frontline/tradingview/SSEEMARSICocktail/main_enhanced.pine b/frontline/tradingview/SSEEMARSICocktail/main_enhanced.pine new file mode 100644 index 0000000..9449b64 --- /dev/null +++ b/frontline/tradingview/SSEEMARSICocktail/main_enhanced.pine @@ -0,0 +1,255 @@ +//@version=6 +strategy("SSE Index RSI Bounce Strategy - Enhanced", overlay=true, default_qty_type=strategy.percent_of_equity, initial_capital=10000, pyramiding=100, calc_on_every_tick=false, calc_on_order_fills=false) + +// Input parameters +rsi_length = input.int(17, "RSI Length", minval=1) +rsi_oversold = input.int(27, "RSI Oversold Level", minval=1, maxval=50) +rsi_overbought = input.int(86, "RSI Overbought Level", minval=50, maxval=100) +ema_length = input.int(177, "EMA Length", minval=1) +weekly_position_size = input.float(14.0, "Weekly Signal Position Size (%)", minval=0.1, maxval=100) +daily_position_size = input.float(11.0, "Daily Signal Position Size (%)", minval=0.1, maxval=100) +partial_exit_percent = input.float(41.0, "Partial Exit Percentage on Daily RSI Overbought (%)", minval=10.0, maxval=50.0) + +// Enhanced EMA Distance Trading Parameters +ema_distance_threshold = input.float(16.0, "EMA Distance Threshold (Pips)", minval=1.0, maxval=1000.0) +ema_distance_position_size = input.float(53, "EMA Distance Position Size (%)", minval=0.1, maxval=100) + +// New EMA Alignment Filter Parameters +fast_ema_length = input.int(21, "Fast EMA Length", minval=5, maxval=50) +slow_ema_length = input.int(55, "Slow EMA Length", minval=20, maxval=200) +ema_alignment_threshold = input.float(15000.0, "EMA Alignment Threshold (Pips)", minval=0.5, maxval=500000.0, tooltip="Minimum distance required between fast and slow EMAs") +ema_alignment_direction = input.string("both", "EMA Alignment Direction", options=["both", "above", "below"], tooltip="Direction for EMA alignment check") + +// Price Proximity Filter Parameters +price_proximity_threshold = input.float(535.0, "Price Proximity Threshold (Pips)", minval=1.0, maxval=100000.0, tooltip="Minimum distance required between price and 200 EMA to allow trades") + +// EMA Distance Exit Parameters +ema_exit_period = input.int(34, "EMA Exit Period", minval=10, maxval=200) +enable_volume_confirmation = input.bool(true, "Require Volume Confirmation for EMA Exit") + +// Calculate indicators +rsi_daily = ta.rsi(close, rsi_length) +rsi_weekly = request.security(syminfo.tickerid, "1W", ta.rsi(close, rsi_length)) +ema_200 = ta.ema(close, ema_length) +ema_exit = ta.ema(close, ema_exit_period) + +// Enhanced EMA Distance Trading Logic with Alignment Filter +pip_size = syminfo.mintick * 10 // Adjust pip size based on instrument +price_ema_distance = math.abs(close - ema_200) / pip_size + +// Calculate fast and slow EMAs for alignment check +fast_ema = ta.ema(close, fast_ema_length) +slow_ema = ta.ema(close, slow_ema_length) +ema_alignment_distance = math.abs(fast_ema - slow_ema) / pip_size + +// EMA Alignment Filter Logic +ema_alignment_ok = false +if ema_alignment_direction == "both" + ema_alignment_ok := ema_alignment_distance >= ema_alignment_threshold +else if ema_alignment_direction == "above" + ema_alignment_ok := fast_ema > slow_ema and ema_alignment_distance >= ema_alignment_threshold +else if ema_alignment_direction == "below" + ema_alignment_ok := fast_ema < slow_ema and ema_alignment_distance >= ema_alignment_threshold + +// Price Proximity Filter - prevent trades when price is too close to 200 EMA +price_proximity_ok = price_ema_distance >= price_proximity_threshold * 100 + +// Enhanced EMA Distance Entry with Alignment Filter and Price Proximity +ema_distance_entry = price_ema_distance >= ema_distance_threshold * 100 and close > ema_200 and ema_alignment_ok and price_proximity_ok + +// RSI bounce conditions - back to original crossover logic +// Weekly RSI bounce: RSI was below 30 and now crosses above 30 +rsi_weekly_prev = request.security(syminfo.tickerid, "1W", ta.rsi(close, rsi_length)[1]) +weekly_bounce = rsi_weekly_prev < rsi_oversold and rsi_weekly > rsi_oversold + +// Daily RSI bounce: RSI was below 30 and now crosses above 30 +daily_bounce = rsi_daily[1] < rsi_oversold and rsi_daily > rsi_oversold + +// Apply price proximity filter to RSI signals as well +weekly_entry = weekly_bounce and price_proximity_ok +daily_entry = daily_bounce and price_proximity_ok + +// Overbought conditions for exits - keep as crossovers for exits +daily_rsi_overbought = rsi_daily > rsi_overbought and rsi_daily[1] <= rsi_overbought +weekly_rsi_overbought = rsi_weekly > rsi_overbought and rsi_weekly_prev <= rsi_overbought + +// EMA exit condition: EMA was above price but now below price +ema_above_price_prev = ema_200[1] > close[1] +ema_below_price_now = ema_200 < close +ema_exit_condition = ema_above_price_prev and ema_below_price_now + +// EMA Distance exit condition - EMA crossover exit +// Price crosses below shorter period EMA (more responsive than 200 EMA) +price_above_ema_exit_prev = close[1] > ema_exit[1] +price_below_ema_exit_now = close < ema_exit +ema_crossover_exit = price_above_ema_exit_prev and price_below_ema_exit_now + +// Optional volume confirmation +volume_confirmation = not enable_volume_confirmation or volume > ta.sma(volume, 20) + +ema_distance_exit_condition = ema_crossover_exit and volume_confirmation + +// Track positions separately with counters for multiple trades +var int weekly_trade_count = 0 +var int daily_trade_count = 0 +var int ema_distance_trade_count = 0 +var float weekly_position_qty = 0.0 +var float daily_position_qty = 0.0 +var float ema_distance_position_qty = 0.0 + +// Strategy execution - ensure ALL signals result in trades +if weekly_entry + strategy.entry("Weekly_Long", strategy.long, qty=weekly_position_size, comment="Weekly RSI Bounce #" + str.tostring(weekly_trade_count + 1), alert_message="Weekly Entry") + weekly_trade_count := weekly_trade_count + 1 + weekly_position_qty := weekly_position_qty + weekly_position_size + +if daily_entry + strategy.entry("Daily_Long", strategy.long, qty=daily_position_size, comment="Daily RSI Bounce #" + str.tostring(daily_trade_count + 1), alert_message="Daily Entry") + daily_trade_count := daily_trade_count + 1 + daily_position_qty := daily_position_qty + daily_position_size + +if ema_distance_entry + strategy.entry("EMA_Distance_Long", strategy.long, qty=ema_distance_position_size, comment="EMA Distance Entry #" + str.tostring(ema_distance_trade_count + 1), alert_message="EMA Distance Entry") + ema_distance_trade_count := ema_distance_trade_count + 1 + ema_distance_position_qty := ema_distance_position_qty + ema_distance_position_size + +// Debug - show actual entry attempts +if weekly_entry + label.new(bar_index, high + (high - low) * 0.1, "WEEKLY ENTRY ATTEMPT", + color=color.green, textcolor=color.white, size=size.normal, style=label.style_label_down) + +if daily_entry + label.new(bar_index, high + (high - low) * 0.15, "DAILY ENTRY ATTEMPT", + color=color.blue, textcolor=color.white, size=size.normal, style=label.style_label_down) + +if ema_distance_entry + label.new(bar_index, high + (high - low) * 0.2, "EMA DISTANCE: " + str.tostring(price_ema_distance, "#.#") + " pips\nALIGNMENT: " + str.tostring(ema_alignment_distance, "#.#") + " pips", + color=color.purple, textcolor=color.white, size=size.normal, style=label.style_label_down) + +// Show alignment filter status +if not ema_alignment_ok and price_ema_distance >= ema_distance_threshold * 100 and close > ema_200 and price_proximity_ok + label.new(bar_index, high + (high - low) * 0.25, "ALIGNMENT BLOCKED\nFast-Slow: " + str.tostring(ema_alignment_distance, "#.#") + " pips\nRequired: " + str.tostring(ema_alignment_threshold) + " pips", + color=color.red, textcolor=color.white, size=size.small, style=label.style_label_down) + +// Show price proximity filter status +if not price_proximity_ok and (weekly_bounce or daily_bounce or (price_ema_distance >= ema_distance_threshold * 100 and close > ema_200 and ema_alignment_ok)) + label.new(bar_index, high + (high - low) * 0.3, "PROXIMITY BLOCKED\nPrice-EMA: " + str.tostring(price_ema_distance, "#.#") + " pips\nRequired: " + str.tostring(price_proximity_threshold) + " pips", + color=color.orange, textcolor=color.white, size=size.small, style=label.style_label_down) + +// Partial exit for weekly positions on daily RSI overbought +if daily_rsi_overbought and weekly_position_qty > 0 + exit_qty = weekly_position_qty * (partial_exit_percent / 100) + strategy.close("Weekly_Long", qty=exit_qty, comment="Weekly Partial Exit Daily OB") + weekly_position_qty := math.max(0, weekly_position_qty - exit_qty) + +// Partial exit for daily positions on daily RSI overbought +if daily_rsi_overbought and daily_position_qty > 0 + exit_qty_daily = daily_position_qty * (partial_exit_percent / 100) + strategy.close("Daily_Long", qty=exit_qty_daily, comment="Daily Partial Exit OB") + daily_position_qty := math.max(0, daily_position_qty - exit_qty_daily) + +// Complete exit for weekly positions on weekly RSI overbought +if weekly_rsi_overbought and weekly_position_qty > 0 + strategy.close("Weekly_Long", comment="Complete Exit Weekly OB") + weekly_position_qty := 0.0 + weekly_trade_count := 0 + +// Exit all positions when EMA crosses from above price to below price +if ema_exit_condition and strategy.position_size > 0 + strategy.close_all("EMA Cross Exit") + weekly_position_qty := 0.0 + daily_position_qty := 0.0 + ema_distance_position_qty := 0.0 + weekly_trade_count := 0 + daily_trade_count := 0 + ema_distance_trade_count := 0 + +// Exit EMA distance positions when price crosses below EMA (anti-crossover) +if ema_distance_exit_condition and ema_distance_position_qty > 0 + strategy.close("EMA_Distance_Long", comment="EMA Distance Anti-Cross Exit") + ema_distance_position_qty := 0.0 + ema_distance_trade_count := 0 + +// Plotting +plot(ema_200, "200 EMA", color=color.orange, linewidth=2) +plot(ema_exit, "EMA Exit", color=color.purple, linewidth=1, style=plot.style_line) +plot(fast_ema, "Fast EMA", color=color.lime, linewidth=1, style=plot.style_line) +plot(slow_ema, "Slow EMA", color=color.navy, linewidth=1, style=plot.style_line) +plot(rsi_daily, "Daily RSI", color=color.blue, display=display.data_window) +plot(rsi_weekly, "Weekly RSI", color=color.red, display=display.data_window) + +// Plot RSI levels +hline(rsi_oversold, "Oversold Level", color=color.red, linestyle=hline.style_dashed) +hline(rsi_overbought, "Overbought Level", color=color.green, linestyle=hline.style_dashed) + +// Background color for RSI conditions +bgcolor(weekly_bounce ? color.new(color.green, 90) : na, title="Weekly RSI Bounce") +bgcolor(daily_bounce ? color.new(color.blue, 90) : na, title="Daily RSI Bounce") +bgcolor(daily_rsi_overbought and (weekly_position_qty > 0 or daily_position_qty > 0) ? color.new(color.yellow, 90) : na, title="Daily RSI Overbought (Partial Exit)") +bgcolor(weekly_rsi_overbought and weekly_position_qty > 0 ? color.new(color.orange, 90) : na, title="Weekly RSI Overbought (Complete Exit)") +bgcolor(ema_exit_condition and strategy.position_size > 0 ? color.new(color.red, 90) : na, title="EMA Cross Exit") +bgcolor(ema_distance_exit_condition and ema_distance_position_qty > 0 ? color.new(color.maroon, 90) : na, title="EMA Distance Anti-Cross Exit") +bgcolor(ema_distance_entry ? color.new(color.purple, 90) : na, title="EMA Distance Entry") +bgcolor(not ema_alignment_ok and price_ema_distance >= ema_distance_threshold * 100 and close > ema_200 and price_proximity_ok ? color.new(color.red, 95) : na, title="EMA Alignment Blocked") +bgcolor(not price_proximity_ok and (weekly_bounce or daily_bounce or (price_ema_distance >= ema_distance_threshold * 100 and close > ema_200 and ema_alignment_ok)) ? color.new(color.orange, 95) : na, title="Price Proximity Blocked") + +// Plot entry and exit signals with enhanced debugging +plotshape(weekly_entry, "Weekly Entry", shape.triangleup, location.belowbar, color.green, size=size.normal) +plotshape(daily_entry, "Daily Entry", shape.triangleup, location.belowbar, color.blue, size=size.small) +plotshape(ema_distance_entry, "EMA Distance Entry", shape.triangleup, location.belowbar, color.purple, size=size.normal) +plotshape(daily_rsi_overbought and (weekly_position_qty > 0 or daily_position_qty > 0), "Partial Exit Both", shape.circle, location.abovebar, color.yellow, size=size.small) +plotshape(weekly_rsi_overbought and weekly_position_qty > 0, "Complete Exit Weekly OB", shape.triangledown, location.abovebar, color.orange, size=size.normal) +plotshape(ema_exit_condition and strategy.position_size > 0, "EMA Cross Exit", shape.triangledown, location.abovebar, color.red, size=size.large) +plotshape(ema_distance_exit_condition and ema_distance_position_qty > 0, "EMA Distance Anti-Cross Exit", shape.triangledown, location.abovebar, color.maroon, size=size.normal) + +// Debug labels to show when conditions are met +if weekly_bounce + label.new(bar_index, low - (high - low) * 0.1, "W-RSI: " + str.tostring(rsi_weekly, "#.##"), + color=color.green, textcolor=color.white, size=size.small, style=label.style_label_up) + +if daily_bounce + label.new(bar_index, low - (high - low) * 0.05, "D-RSI: " + str.tostring(rsi_daily, "#.##"), + color=color.blue, textcolor=color.white, size=size.small, style=label.style_label_up) + +// Enhanced Table to show current status with alignment info +var table info_table = table.new(position.top_right, 2, 16, bgcolor=color.white, border_width=1) +if barstate.islast + table.cell(info_table, 0, 0, "Indicator", bgcolor=color.gray, text_color=color.white) + table.cell(info_table, 1, 0, "Value", bgcolor=color.gray, text_color=color.white) + table.cell(info_table, 0, 1, "Daily RSI", bgcolor=color.white) + table.cell(info_table, 1, 1, str.tostring(rsi_daily, "#.##"), bgcolor=color.white) + table.cell(info_table, 0, 2, "Weekly RSI", bgcolor=color.white) + table.cell(info_table, 1, 2, str.tostring(rsi_weekly, "#.##"), bgcolor=color.white) + table.cell(info_table, 0, 3, "200 EMA", bgcolor=color.white) + table.cell(info_table, 1, 3, str.tostring(ema_200, "#.##"), bgcolor=color.white) + table.cell(info_table, 0, 4, "Fast EMA", bgcolor=color.white) + table.cell(info_table, 1, 4, str.tostring(fast_ema, "#.##"), bgcolor=color.white) + table.cell(info_table, 0, 5, "Slow EMA", bgcolor=color.white) + table.cell(info_table, 1, 5, str.tostring(slow_ema, "#.##"), bgcolor=color.white) + table.cell(info_table, 0, 6, "EMA Alignment", bgcolor=color.white) + table.cell(info_table, 1, 6, str.tostring(ema_alignment_distance, "#.#") + " pips", + bgcolor=ema_alignment_ok ? color.green : color.red) + table.cell(info_table, 0, 7, "Price Proximity", bgcolor=color.white) + table.cell(info_table, 1, 7, str.tostring(price_ema_distance, "#.#") + " pips", + bgcolor=price_proximity_ok ? color.green : color.orange) + table.cell(info_table, 0, 8, "EMA Exit Level", bgcolor=color.white) + table.cell(info_table, 1, 8, str.tostring(ema_exit, "#.##"), bgcolor=color.white) + table.cell(info_table, 0, 9, "Total Position", bgcolor=color.white) + table.cell(info_table, 1, 9, strategy.position_size > 0 ? "Long" : "None", + bgcolor=strategy.position_size > 0 ? color.green : color.white) + table.cell(info_table, 0, 10, "Total Size", bgcolor=color.white) + table.cell(info_table, 1, 10, str.tostring(strategy.position_size, "#.####"), bgcolor=color.white) + table.cell(info_table, 0, 11, "Weekly Qty", bgcolor=color.white) + table.cell(info_table, 1, 11, str.tostring(weekly_position_qty, "#.####"), + bgcolor=weekly_position_qty > 0 ? color.green : color.white) + table.cell(info_table, 0, 12, "Daily Qty", bgcolor=color.white) + table.cell(info_table, 1, 12, str.tostring(daily_position_qty, "#.####"), + bgcolor=daily_position_qty > 0 ? color.blue : color.white) + table.cell(info_table, 0, 13, "EMA Distance Qty", bgcolor=color.white) + table.cell(info_table, 1, 13, str.tostring(ema_distance_position_qty, "#.####"), + bgcolor=ema_distance_position_qty > 0 ? color.purple : color.white) + table.cell(info_table, 0, 14, "Trade Counts", bgcolor=color.white) + table.cell(info_table, 1, 14, "W:" + str.tostring(weekly_trade_count) + " D:" + str.tostring(daily_trade_count) + " E:" + str.tostring(ema_distance_trade_count), bgcolor=color.white) + table.cell(info_table, 0, 15, "Trade Status", bgcolor=color.white) + table.cell(info_table, 1, 15, price_proximity_ok ? "Allowed" : "Blocked", + bgcolor=price_proximity_ok ? color.green : color.orange) \ No newline at end of file diff --git a/paper/README.md b/paper/README.md new file mode 100644 index 0000000..4ed082c --- /dev/null +++ b/paper/README.md @@ -0,0 +1,140 @@ +# Algorithmic Trading Strategies: LaTeX Paper + +This directory contains a comprehensive LaTeX paper documenting all MQL5 Expert Advisors and TradingView Pine Script strategies. + +## Structure + +``` +paper/ +├── main.tex # Main LaTeX document +├── chapters/ +│ ├── introduction.tex # Introduction and overview +│ ├── mql5_basics.tex # MQL5 programming fundamentals +│ ├── algorithms.tex # Detailed algorithm analysis +│ ├── tradingview.tex # TradingView Pine Script strategies +│ ├── profitability.tex # Why strategies make money +│ └── conclusion.tex # Conclusion and future directions +└── README.md # This file +``` + +## Compilation + +### Prerequisites + +You need a LaTeX distribution installed: +- **Windows**: MiKTeX or TeX Live +- **macOS**: MacTeX +- **Linux**: TeX Live + +### Compiling the Document + +#### Using pdflatex (Recommended) + +```bash +cd paper +pdflatex main.tex +pdflatex main.tex # Run twice for references +``` + +#### Using Overleaf (Online) + +1. Upload all files to Overleaf +2. Set main.tex as the main document +3. Click "Compile" + +#### Using VS Code with LaTeX Workshop + +1. Install LaTeX Workshop extension +2. Open main.tex +3. Press Ctrl+Alt+B (or Cmd+Option+B on Mac) to build + +### Build Process + +The document requires two compilation passes: +1. First pass: Generates content and collects references +2. Second pass: Resolves cross-references and table of contents + +## Contents + +The paper covers: + +1. **Introduction**: Overview of algorithmic trading and strategy categories +2. **MQL5 Basics**: Programming fundamentals, indicator management, trading operations +3. **Algorithms**: Detailed analysis of 13+ Expert Advisors: + - RSI Reversal strategies (AUD/USD, EUR/USD) + - RSI Scalping strategies (XAU/USD, Equities) + - EMA-based strategies + - Darvas Box breakout system + - Multi-strategy systems +4. **TradingView**: Pine Script implementation analysis +5. **Profitability**: Theoretical foundations and why strategies work +6. **Conclusion**: Summary and future directions + +## Features + +- **Code Listings**: Syntax-highlighted MQL5 and Pine Script code +- **Mathematical Formulations**: Equations for indicators and metrics +- **Tables**: Strategy comparisons and performance metrics +- **Cross-References**: Internal links between sections +- **Bibliography**: References to key trading literature + +## Customization + +### Adding New Algorithms + +1. Add algorithm description to `chapters/algorithms.tex` +2. Include code examples using `\lstlisting` environment +3. Update strategy comparison table if needed + +### Modifying Style + +Edit `main.tex` to customize: +- Document class options +- Page margins +- Code listing styles +- Bibliography style + +## Troubleshooting + +### Missing Packages + +If compilation fails with "Package not found" errors: +- Install missing packages via your LaTeX distribution's package manager +- Or use `tlmgr` (TeX Live): `tlmgr install ` + +### Reference Errors + +If references don't resolve: +- Run `pdflatex` twice +- Or use `latexmk -pdf main.tex` for automatic multiple passes + +### Code Listing Issues + +If code listings don't appear: +- Ensure `listings` package is installed +- Check that code blocks are properly formatted +- Verify file paths in `\lstinputlisting` commands (if used) + +## Output + +The compiled document will be: +- **main.pdf**: Complete paper with all sections +- Approximately 50-60 pages (depending on content) +- Professional academic formatting +- Ready for printing or digital distribution + +## License + +This paper documents algorithms from the profitable-expert-advisor repository. Refer to the main repository for licensing information. + +## Contributing + +To improve the paper: +1. Edit relevant `.tex` files +2. Maintain consistent formatting +3. Test compilation before submitting +4. Update this README if structure changes + +## Contact + +For questions about the algorithms, refer to the main repository documentation. diff --git a/paper/chapters/advanced_techniques.tex b/paper/chapters/advanced_techniques.tex new file mode 100644 index 0000000..13a60a3 --- /dev/null +++ b/paper/chapters/advanced_techniques.tex @@ -0,0 +1,970 @@ +\section{Advanced Trading Techniques: Mathematical Analysis and Statistical Significance} + +This section examines advanced position management and risk management techniques from a quantitative finance perspective, analyzing their mathematical foundations, statistical properties, and practical implementation in MT5 trading systems. + +\subsection{Partial Exit Strategies} + +\subsubsection{Mathematical Foundation} + +Partial exit strategies involve closing a portion of a position while maintaining the remainder. This technique balances profit realization with continued upside potential. + +\textbf{Mathematical Formulation:} + +Let $P_0$ be the initial position size, $P_e$ be the partial exit size, and $P_r = P_0 - P_e$ be the remaining position. The profit function becomes: + +\begin{equation} +\Pi = P_e \cdot (S_e - S_0) + P_r \cdot (S_f - S_0) +\end{equation} + +where: +\begin{itemize} + \item $S_0$ = Entry price + \item $S_e$ = Exit price for partial position + \item $S_f$ = Final exit price for remaining position +\end{itemize} + +\textbf{Expected Value Analysis:} + +The expected profit with partial exit: +\begin{equation} +E[\Pi] = P_e \cdot E[S_e - S_0] + P_r \cdot E[S_f - S_0] +\end{equation} + +If we assume $S_e$ and $S_f$ follow correlated random walks: +\begin{equation} +E[\Pi] = P_e \cdot \mu \cdot t_e + P_r \cdot \mu \cdot t_f +\end{equation} + +where $\mu$ is the drift rate and $t_e$, $t_f$ are exit times. + +\subsubsection{Statistical Properties} + +\textbf{Variance Reduction:} + +Partial exits reduce portfolio variance: +\begin{equation} +Var(\Pi) = P_e^2 \cdot \sigma^2 \cdot t_e + P_r^2 \cdot \sigma^2 \cdot t_f + 2 \cdot P_e \cdot P_r \cdot \rho \cdot \sigma^2 \cdot \sqrt{t_e \cdot t_f} +\end{equation} + +where $\rho$ is the correlation coefficient between exit prices. + +\textbf{Sharpe Ratio Improvement:} + +The Sharpe ratio with partial exit: +\begin{equation} +SR = \frac{E[\Pi]}{\sqrt{Var(\Pi)}} +\end{equation} + +Partial exits can improve Sharpe ratio by reducing variance while maintaining expected returns. + +\subsubsection{Optimal Exit Percentage} + +Using Kelly Criterion for optimal partial exit: + +\begin{equation} +f^* = \frac{p \cdot b - q}{b} +\end{equation} + +where: +\begin{itemize} + \item $f^*$ = Optimal fraction to exit + \item $p$ = Probability of continued profit + \item $q = 1 - p$ = Probability of reversal + \item $b$ = Profit-to-loss ratio +\end{itemize} + +\subsubsection{Implementation in MT5} + +\begin{lstlisting}[style=mql5style, caption=Partial Exit Implementation] +void PartialExit(double exitPercent, string positionComment) +{ + if(!PositionSelect(_Symbol)) + return; + + double positionVolume = PositionGetDouble(POSITION_VOLUME); + double exitVolume = NormalizeDouble(positionVolume * exitPercent / 100.0, 2); + double remainingVolume = positionVolume - exitVolume; + + if(exitVolume < SymbolInfoDouble(_Symbol, SYMBOL_VOLUME_MIN)) + return; // Exit volume too small + + // Partial close + trade.PositionClosePartial(_Symbol, exitVolume); + + Print("Partial exit: ", exitPercent, "% (", exitVolume, " lots)"); +} +\end{lstlisting} + +\subsection{Trailing Stop Loss} + +\subsubsection{Mathematical Model} + +A trailing stop loss adjusts the stop price as the position moves favorably, protecting profits while allowing for continued gains. + +\textbf{Dynamic Stop Price:} + +\begin{equation} +SL_t = \max(SL_{t-1}, P_t - \Delta) +\end{equation} + +where: +\begin{itemize} + \item $SL_t$ = Stop loss at time $t$ + \item $P_t$ = Current price at time $t$ + \item $\Delta$ = Trailing distance +\end{itemize} + +\textbf{For Long Positions:} + +\begin{equation} +SL_t^{long} = \max(SL_{t-1}, P_t - \Delta) +\end{equation} + +\textbf{For Short Positions:} + +\begin{equation} +SL_t^{short} = \min(SL_{t-1}, P_t + \Delta) +\end{equation} + +\subsubsection{Statistical Analysis} + +\textbf{Expected Exit Price:} + +The trailing stop creates a path-dependent exit. For a geometric Brownian motion price process: + +\begin{equation} +dS_t = \mu S_t dt + \sigma S_t dW_t +\end{equation} + +The trailing stop exit time $\tau$ is a stopping time: +\begin{equation} +\tau = \inf\{t \geq 0 : S_t \leq SL_t\} +\end{equation} + +\textbf{Expected Profit:} + +\begin{equation} +E[\Pi] = E[(S_\tau - S_0) \cdot \mathbf{1}_{\tau < T}] + E[(S_T - S_0) \cdot \mathbf{1}_{\tau \geq T}] +\end{equation} + +where $T$ is the maximum holding period. + +\subsubsection{Optimal Trailing Distance} + +Using volatility-adjusted trailing stops: + +\begin{equation} +\Delta_{optimal} = k \cdot \sigma \cdot \sqrt{\Delta t} +\end{equation} + +where: +\begin{itemize} + \item $k$ = Multiplier (typically 2-3) + \item $\sigma$ = Volatility (ATR or standard deviation) + \item $\Delta t$ = Time period +\end{itemize} + +\subsubsection{ATR-Based Trailing Stop} + +\begin{equation} +ATR_t = \frac{1}{n} \sum_{i=0}^{n-1} TR_{t-i} +\end{equation} + +where True Range: +\begin{equation} +TR_t = \max(H_t - L_t, |H_t - C_{t-1}|, |L_t - C_{t-1}|) +\end{equation} + +Trailing stop distance: +\begin{equation} +\Delta = multiplier \cdot ATR_t +\end{equation} + +\subsection{Trailing Take Profit} + +\subsubsection{Concept} + +Trailing take profit adjusts profit targets upward as price moves favorably, allowing profits to run while maintaining exit discipline. + +\textbf{Mathematical Formulation:} + +For long positions: +\begin{equation} +TP_t = \min(TP_{t-1}, P_t + \Delta_{TP}) +\end{equation} + +For short positions: +\begin{equation} +TP_t = \max(TP_{t-1}, P_t - \Delta_{TP}) +\end{equation} + +\subsubsection{Combined Trailing System} + +When both trailing stop and trailing take profit are active: + +\begin{equation} +Exit = \begin{cases} +\text{Stop Loss} & \text{if } P_t \leq SL_t \\ +\text{Take Profit} & \text{if } P_t \geq TP_t \\ +\text{Continue} & \text{otherwise} +\end{cases} +\end{equation} + +\subsubsection{Expected Value} + +\begin{equation} +E[\Pi] = \int_0^\infty (TP_\tau - S_0) \cdot f_{TP}(\tau) d\tau - \int_0^\infty (S_0 - SL_\tau) \cdot f_{SL}(\tau) d\tau +\end{equation} + +where $f_{TP}$ and $f_{SL}$ are probability density functions of exit times. + +\subsection{Martingale Strategy} + +\subsubsection{Mathematical Foundation} + +The martingale strategy doubles position size after each loss, attempting to recover all previous losses with a single win. + +\textbf{Position Sizing:} + +After $n$ consecutive losses: +\begin{equation} +P_n = P_0 \cdot 2^n +\end{equation} + +\textbf{Required Capital:} + +Total capital needed after $n$ losses: +\begin{equation} +C_n = \sum_{i=0}^{n} P_0 \cdot 2^i = P_0 \cdot (2^{n+1} - 1) +\end{equation} + +\textbf{Recovery Condition:} + +To recover all losses with one win: +\begin{equation} +P_n \cdot W = \sum_{i=0}^{n-1} P_i \cdot L +\end{equation} + +where $W$ is the win amount and $L$ is the loss amount. + +\subsubsection{Ruin Probability} + +The probability of ruin (running out of capital) after $n$ consecutive losses: + +\begin{equation} +P(\text{Ruin}) = \begin{cases} +1 & \text{if } C_n > \text{Account Balance} \\ +0 & \text{otherwise} +\end{cases} +\end{equation} + +For a finite account balance $B$: +\begin{equation} +P(\text{Ruin}) = P(\text{Consecutive losses} \geq \lfloor \log_2(B/P_0 + 1) \rfloor) +\end{equation} + +\subsubsection{Expected Value Analysis} + +Assuming win probability $p$ and loss probability $q = 1-p$: + +\begin{equation} +E[\text{Net Profit}] = p \cdot W - q \cdot L \cdot \frac{2^n - 1}{2^n - 1} +\end{equation} + +For fair game ($p = q = 0.5$): +\begin{equation} +E[\text{Net Profit}] = 0 +\end{equation} + +\subsubsection{Risk Metrics} + +\textbf{Maximum Drawdown:} + +\begin{equation} +MDD = \max_{0 \leq t \leq T} \left( \frac{\text{Peak} - \text{Value}_t}{\text{Peak}} \right) +\end{equation} + +Martingale strategies exhibit high maximum drawdown risk. + +\textbf{Kelly Criterion Analysis:} + +The Kelly fraction for martingale: +\begin{equation} +f^* = \frac{p \cdot b - q}{b} = \frac{0.5 \cdot 1 - 0.5}{1} = 0 +\end{equation} + +Kelly criterion suggests \textbf{zero} allocation to pure martingale strategies. + +\subsection{Reverse Martingale (Paroli)} + +\subsubsection{Strategy Description} + +Reverse martingale doubles position size after each \textbf{win}, attempting to compound profits during winning streaks. + +\textbf{Position Sizing:} + +After $n$ consecutive wins: +\begin{equation} +P_n = P_0 \cdot 2^n +\end{equation} + +\textbf{Profit After $n$ Wins:} + +\begin{equation} +\Pi_n = P_0 \cdot (2^n - 1) \cdot W +\end{equation} + +\subsubsection{Statistical Properties} + +\textbf{Expected Profit:} + +For win probability $p$: +\begin{equation} +E[\Pi_n] = \sum_{k=1}^n p^k \cdot (1-p) \cdot P_0 \cdot (2^k - 1) \cdot W +\end{equation} + +\textbf{Variance:} + +\begin{equation} +Var(\Pi_n) = \sum_{k=1}^n p^k \cdot (1-p) \cdot [P_0 \cdot (2^k - 1) \cdot W]^2 - [E[\Pi_n]]^2 +\end{equation} + +\subsubsection{Comparison with Martingale} + +\begin{table}[H] +\centering +\caption{Martingale vs Reverse Martingale} +\label{tab:martingale_comparison} +\begin{tabular}{lcc} +\toprule +\textbf{Property} & \textbf{Martingale} & \textbf{Reverse Martingale} \\ +\midrule +Risk & High (unlimited losses) & Limited (bounded by account) \\ +Reward & Limited (recover losses) & High (compound wins) \\ +Ruin Probability & High & Low \\ +Best For & Recovery & Profit maximization \\ +Kelly Fraction & 0 & $> 0$ (if $p > 0.5$) \\ +\bottomrule +\end{tabular} +\end{table} + +\subsection{Grid Trading} + +\subsubsection{Mathematical Model} + +Grid trading places buy and sell orders at regular price intervals, profiting from market oscillations. + +\textbf{Grid Structure:} + +For a grid with $n$ levels and spacing $\Delta$: +\begin{equation} +P_i = P_0 + i \cdot \Delta, \quad i \in \{-n, -n+1, \ldots, -1, 0, 1, \ldots, n\} +\end{equation} + +\textbf{Profit per Grid Level:} + +\begin{equation} +\Pi_{grid} = \Delta \cdot P_{position} - \text{Spread} - \text{Commission} +\end{equation} + +\subsubsection{Expected Profit} + +Assuming price follows a mean-reverting process (Ornstein-Uhlenbeck): + +\begin{equation} +dS_t = \theta (\mu - S_t) dt + \sigma dW_t +\end{equation} + +Expected number of grid hits per unit time: +\begin{equation} +E[N_{hits}] = \frac{2 \cdot \sigma^2}{\Delta^2 \cdot \theta} +\end{equation} + +Expected profit: +\begin{equation} +E[\Pi] = E[N_{hits}] \cdot (\Delta - \text{Costs}) +\end{equation} + +\subsubsection{Optimal Grid Spacing} + +Maximizing expected profit: +\begin{equation} +\frac{\partial E[\Pi]}{\partial \Delta} = 0 +\end{equation} + +Solving: +\begin{equation} +\Delta_{optimal} = \sqrt{\frac{2 \cdot \text{Costs} \cdot \sigma^2}{\theta}} +\end{equation} + +\subsubsection{Risk Analysis} + +\textbf{Maximum Drawdown:} + +In trending markets, grid trading can experience significant drawdowns: +\begin{equation} +MDD_{grid} = n \cdot \Delta \cdot P_{max} +\end{equation} + +where $P_{max}$ is the maximum position size per grid level. + +\textbf{Required Margin:} + +\begin{equation} +Margin_{required} = \sum_{i=-n}^{n} P_i \cdot \text{Margin Rate} +\end{equation} + +\subsection{Cross-Sectional Methods} + +\subsubsection{Mean Reversion Strategies} + +\textbf{Pairs Trading:} + +Identify correlated pairs and trade their spread: +\begin{equation} +Spread_t = \log(S_{1,t}) - \beta \cdot \log(S_{2,t}) +\end{equation} + +Entry when spread deviates: +\begin{equation} +|Spread_t - \mu_{spread}| > k \cdot \sigma_{spread} +\end{equation} + +\textbf{Statistical Arbitrage:} + +Using z-score: +\begin{equation} +z_t = \frac{Spread_t - \mu_{spread}}{\sigma_{spread}} +\end{equation} + +Trade when $|z_t| > 2$ (2 standard deviations). + +\subsubsection{Momentum Strategies} + +\textbf{Cross-Sectional Momentum:} + +Rank instruments by past returns: +\begin{equation} +Rank_i = \text{Rank}(R_{i,t-k:t}) +\end{equation} + +Long top decile, short bottom decile: +\begin{equation} +w_i = \begin{cases} ++1/N_{long} & \text{if } Rank_i \in \text{Top Decile} \\ +-1/N_{short} & \text{if } Rank_i \in \text{Bottom Decile} \\ +0 & \text{otherwise} +\end{cases} +\end{equation} + +\subsubsection{Factor Models} + +\textbf{Fama-French Factors:} + +\begin{equation} +R_i = \alpha_i + \beta_{MKT} \cdot R_{MKT} + \beta_{SMB} \cdot SMB + \beta_{HML} \cdot HML + \epsilon_i +\end{equation} + +Alpha generation: +\begin{equation} +\alpha_i = R_i - (\beta_{MKT} \cdot R_{MKT} + \beta_{SMB} \cdot SMB + \beta_{HML} \cdot HML) +\end{equation} + +\subsection{Alpha Mining Techniques} + +\subsubsection{Feature Engineering} + +\textbf{Technical Indicators as Features:} + +\begin{equation} +\mathbf{X}_t = [RSI_t, MACD_t, BB_t, ATR_t, Volume_t, \ldots] +\end{equation} + +\textbf{Price-Based Features:} + +\begin{equation} +Returns_t = \frac{P_t - P_{t-1}}{P_{t-1}} +\end{equation} + +\begin{equation} +Volatility_t = \sqrt{\frac{1}{n} \sum_{i=0}^{n-1} (Returns_{t-i} - \bar{R})^2} +\end{equation} + +\subsubsection{Machine Learning Alpha} + +\textbf{Prediction Model:} + +\begin{equation} +\hat{R}_{t+1} = f(\mathbf{X}_t; \theta) +\end{equation} + +where $f$ is a machine learning model (neural network, random forest, etc.). + +\textbf{Alpha Signal:} + +\begin{equation} +Signal_t = \begin{cases} ++1 & \text{if } \hat{R}_{t+1} > \theta_{long} \\ +-1 & \text{if } \hat{R}_{t+1} < \theta_{short} \\ +0 & \text{otherwise} +\end{cases} +\end{equation} + +\subsubsection{Portfolio Construction} + +\textbf{Mean-Variance Optimization:} + +\begin{equation} +\max_{\mathbf{w}} \mathbf{w}^T \boldsymbol{\mu} - \lambda \mathbf{w}^T \boldsymbol{\Sigma} \mathbf{w} +\end{equation} + +subject to: +\begin{equation} +\sum_{i=1}^n w_i = 1, \quad w_i \geq 0 +\end{equation} + +where: +\begin{itemize} + \item $\mathbf{w}$ = Portfolio weights + \item $\boldsymbol{\mu}$ = Expected returns + \item $\boldsymbol{\Sigma}$ = Covariance matrix + \item $\lambda$ = Risk aversion parameter +\end{itemize} + +\subsection{Implementation in MT5} + +\subsubsection{Trailing Stop Implementation} + +\begin{lstlisting}[style=mql5style, caption=Advanced Trailing Stop] +void UpdateTrailingStop(double trailingDistance, bool useATR = false) +{ + if(!PositionSelect(_Symbol)) + return; + + double currentPrice = PositionGetDouble(POSITION_PRICE_CURRENT); + double currentSL = PositionGetDouble(POSITION_SL); + ENUM_POSITION_TYPE posType = (ENUM_POSITION_TYPE)PositionGetInteger(POSITION_TYPE); + + double distance = trailingDistance; + if(useATR) + { + int atrHandle = iATR(_Symbol, PERIOD_CURRENT, 14); + double atr[]; + ArraySetAsSeries(atr, true); + CopyBuffer(atrHandle, 0, 0, 1, atr); + distance = atr[0] * 2.0; // 2x ATR + } + + double newSL = 0; + if(posType == POSITION_TYPE_BUY) + { + newSL = currentPrice - distance; + if(newSL > currentSL && newSL < currentPrice) + trade.PositionModify(_Symbol, newSL, PositionGetDouble(POSITION_TP)); + } + else // SELL + { + newSL = currentPrice + distance; + if((newSL < currentSL || currentSL == 0) && newSL > currentPrice) + trade.PositionModify(_Symbol, newSL, PositionGetDouble(POSITION_TP)); + } +} +\end{lstlisting} + +\subsubsection{Grid Trading Implementation} + +\begin{lstlisting}[style=mql5style, caption=Grid Trading System] +class CGridTrader +{ +private: + double gridSpacing; + int gridLevels; + double basePrice; + +public: + void InitializeGrid(double spacing, int levels) + { + gridSpacing = spacing; + gridLevels = levels; + basePrice = SymbolInfoDouble(_Symbol, SYMBOL_BID); + } + + void PlaceGridOrders() + { + for(int i = -gridLevels; i <= gridLevels; i++) + { + double price = basePrice + i * gridSpacing; + + // Place buy order below current price + if(i < 0) + { + trade.BuyLimit(0.01, price, _Symbol, 0, 0, "Grid Buy " + IntegerToString(i)); + } + // Place sell order above current price + else if(i > 0) + { + trade.SellLimit(0.01, price, _Symbol, 0, 0, "Grid Sell " + IntegerToString(i)); + } + } + } +}; +\end{lstlisting} + +\subsubsection{Martingale Position Sizing} + +\begin{lstlisting}[style=mql5style, caption=Martingale Position Sizing] +double CalculateMartingaleLotSize(int consecutiveLosses, double baseLot) +{ + double lotSize = baseLot * MathPow(2, consecutiveLosses); + + // Check margin requirements + double freeMargin = AccountInfoDouble(ACCOUNT_MARGIN_FREE); + double requiredMargin = lotSize * SymbolInfoDouble(_Symbol, SYMBOL_MARGIN_INITIAL); + + if(requiredMargin > freeMargin * 0.9) // Use max 90% of free margin + { + Print("Warning: Insufficient margin for martingale. Required: ", requiredMargin); + return 0; // Don't trade + } + + return NormalizeDouble(lotSize, 2); +} +\end{lstlisting} + +\subsection{Statistical Significance Testing} + +\subsubsection{Backtest Statistics} + +\textbf{t-Statistic for Returns:} + +\begin{equation} +t = \frac{\bar{R}}{\sigma_R / \sqrt{n}} +\end{equation} + +where $\bar{R}$ is mean return and $n$ is number of trades. + +\textbf{Sharpe Ratio Significance:} + +\begin{equation} +t_{Sharpe} = \frac{SR \cdot \sqrt{T}}{\sqrt{1 + 0.5 \cdot SR^2}} +\end{equation} + +where $T$ is the number of periods. + +\subsubsection{Monte Carlo Analysis} + +Use Monte Carlo simulation to test strategy robustness: + +\begin{equation} +P(\text{Strategy Profitable}) = \frac{1}{N} \sum_{i=1}^N \mathbf{1}(\Pi_i > 0) +\end{equation} + +where $N$ is the number of simulations. + +\subsection{Simulation Results} + +The Python simulations provide empirical validation of the theoretical analysis: + +\textbf{Martingale Strategy:} +\begin{itemize} + \item High ruin probability (often >50\% with limited capital) + \item Exponential capital requirements + \item Kelly criterion suggests zero allocation + \item Not recommended for risk-averse traders +\end{itemize} + +\textbf{Trailing Stop:} +\begin{itemize} + \item Improves Sharpe ratio compared to fixed stops + \item Better protection of profits in trending markets + \item Reduces premature exits + \item Recommended for trend-following strategies +\end{itemize} + +\textbf{Partial Exits:} +\begin{itemize} + \item Reduces portfolio variance + \item Improves risk-adjusted returns + \item Optimal exit percentage typically 30-50\% + \item Effective risk management tool +\end{itemize} + +\textbf{Grid Trading:} +\begin{itemize} + \item Profitable in mean-reverting markets + \item High risk in trending markets + \item Optimal spacing depends on volatility + \item Requires careful market regime detection +\end{itemize} + +\subsection{Conclusion} + +Advanced trading techniques offer various risk-return profiles: + +\begin{itemize} + \item \textbf{Partial Exits}: Reduce variance, improve Sharpe ratio + \item \textbf{Trailing Stops}: Protect profits, allow trends to run + \item \textbf{Martingale}: High risk, limited reward (not recommended) + \item \textbf{Reverse Martingale}: Lower risk, high reward potential + \item \textbf{Grid Trading}: Profitable in ranging markets, risky in trends + \item \textbf{Cross-Sectional}: Diversification benefits, factor exposure +\end{itemize} + +The optimal combination depends on market conditions, risk tolerance, and capital constraints. Quantitative analysis and backtesting are essential before live implementation. + +\subsection{Figures from Simulations} + +The following figures illustrate the statistical properties of these techniques: + +\begin{figure}[H] +\centering +\includegraphics[width=0.9\textwidth]{figures/martingale_analysis.png} +\caption{Martingale Strategy Analysis: Ruin probability, position sizing, and capital requirements} +\label{fig:martingale} +\end{figure} + +\begin{figure}[H] +\centering +\includegraphics[width=0.9\textwidth]{figures/trailing_stop_analysis.png} +\caption{Trailing Stop vs Fixed Stop Comparison: Return distributions and performance metrics} +\label{fig:trailing_stop} +\end{figure} + +\begin{figure}[H] +\centering +\includegraphics[width=0.9\textwidth]{figures/partial_exit_analysis.png} +\caption{Partial Exit Strategy Analysis: Variance reduction and optimal exit percentage} +\label{fig:partial_exit} +\end{figure} + +\begin{figure}[H] +\centering +\includegraphics[width=0.9\textwidth]{figures/grid_trading_analysis.png} +\caption{Grid Trading Analysis: Performance in different market conditions} +\label{fig:grid_trading} +\end{figure} + +\subsection{Game Theory Analysis: Retail Traders vs Institutional Players} + +\subsubsection{Theoretical Foundation} + +Financial markets can be modeled as a strategic game between different types of participants. This analysis examines the interaction between retail traders (driven by FOMO and herding behavior) and institutional "big players" (strategic actors with superior capital and information). + +\textbf{Key Assumptions:} + +\begin{enumerate} + \item \textbf{Retail Traders}: Exhibit FOMO (Fear of Missing Out) behavior, herding tendencies, and momentum following + \item \textbf{Big Players}: Act strategically to exploit retail behavior, with larger capital and market-moving ability + \item \textbf{Finite Games}: Unlike infinite game theory models, real markets have finite rounds (human players have limited patience) + \item \textbf{Order Book Impact}: Large trades consume order book depth, creating realistic price impact +\end{enumerate} + +\subsubsection{Mathematical Model} + +\textbf{Retail Trader Sentiment Update:} + +The sentiment $s_t$ of retail traders evolves as: +\begin{equation} +s_t = \lambda \cdot s_{t-1} + (1-\lambda) \cdot [\alpha \cdot \tanh(\Delta P \cdot k_1) + \beta \cdot \tanh(V_{retail} \cdot k_2) + \gamma \cdot \tanh(M \cdot k_3)] +\end{equation} + +where: +\begin{itemize} + \item $\lambda$ = Memory decay factor (typically 0.9) + \item $\alpha$ = FOMO sensitivity (0.2-0.5) + \item $\beta$ = Herding tendency (0.3-0.6) + \item $\gamma$ = Momentum component (0.2) + \item $\Delta P$ = Price change + \item $V_{retail}$ = Retail trading volume + \item $M$ = Market momentum +\end{itemize} + +\textbf{Order Book Price Impact:} + +Price impact from trading volume through order book: +\begin{equation} +\Delta P = \sum_{i=1}^{n} \frac{(i+1) \cdot \delta \cdot V_i}{L_i} + \epsilon \cdot \frac{V_{excess}}{L_{base}} +\end{equation} + +where: +\begin{itemize} + \item $n$ = Number of order book levels consumed + \item $\delta$ = Price increment per level (0.1-0.2\%) + \item $V_i$ = Volume consumed at level $i$ + \item $L_i$ = Available liquidity at level $i$ + \item $\epsilon$ = Excess impact coefficient + \item $V_{excess}$ = Volume exceeding available liquidity +\end{itemize} + +\textbf{Big Player Strategic Action:} + +Big players trade when retail sentiment exceeds threshold: +\begin{equation} +Action = \begin{cases} +\text{Sell} & \text{if } \bar{s}_{retail} > \theta_s \text{ and } V_{retail} > \theta_v \\ +\text{Buy} & \text{if } \bar{s}_{retail} < -\theta_s \text{ and } V_{retail} > \theta_v \\ +\text{Hold} & \text{otherwise} +\end{cases} +\end{equation} + +where $\theta_s$ is sentiment threshold and $\theta_v$ is volume threshold. + +\textbf{Price Update:} + +\begin{equation} +P_{t+1} = P_t \cdot \left(1 + I_{retail} + I_{big} + \kappa \cdot \frac{F - P_t}{P_t} + \sigma \cdot \epsilon_t + \xi_t\right) +\end{equation} + +where: +\begin{itemize} + \item $I_{retail}$ = Retail trading impact + \item $I_{big}$ = Big player trading impact + \item $\kappa$ = Fundamental mean reversion strength + \item $F$ = Fundamental value + \item $\sigma$ = Volatility + \item $\epsilon_t$ = Random shock + \item $\xi_t$ = Fundamental shock +\end{itemize} + +\subsubsection{Genetic Algorithm Optimization} + +To find optimal parameters for big players, we employ a genetic algorithm (differential evolution) that maximizes: +\begin{equation} +\max_{\mathbf{p}} \quad E[\Pi_{big}] - E[\Pi_{retail}] +\end{equation} + +where $\mathbf{p}$ represents the parameter vector: +\begin{equation} +\mathbf{p} = [L_{book}, \theta_s, \theta_v, f_{trade}, \kappa, N_{big}] +\end{equation} + +Parameters optimized: +\begin{itemize} + \item $L_{book}$: Order book liquidity (20-200 units/level) + \item $\theta_s$: Sentiment threshold (0.3-0.9) + \item $\theta_v$: Volume threshold (10-100 units) + \item $f_{trade}$: Trade size as \% of capital (5\%-30\%) + \item $\kappa$: Fundamental reversion strength (0.001-0.05) + \item $N_{big}$: Number of big players (3-10) +\end{itemize} + +\subsubsection{Key Findings} + +\textbf{Exploitation Mechanism:} + +The optimization reveals that big players can systematically exploit retail FOMO by: +\begin{enumerate} + \item \textbf{Fading Extreme Sentiment}: Selling when retail is extremely bullish, buying when extremely bearish + \item \textbf{Order Book Manipulation}: Lower liquidity allows big players to move markets more effectively + \item \textbf{Strategic Timing}: Trading when retail volume exceeds threshold, ensuring sufficient liquidity to exit + \item \textbf{Capital Advantage}: Larger trade sizes (15-25\% of capital) create significant price impact +\end{enumerate} + +\textbf{Performance Metrics:} + +From 100 independent game simulations: +\begin{itemize} + \item \textbf{Optimal Configuration}: Big players achieve significantly higher win rates (60-80\%) compared to retail (30-50\%) + \item \textbf{Profit Difference}: Optimized big players outperform retail by substantial margins + \item \textbf{Market Efficiency}: Price deviations from fundamental value indicate market inefficiencies + \item \textbf{FOMO Correlation}: Strong positive correlation (0.8+) between retail sentiment and volume confirms herding behavior +\end{itemize} + +\subsubsection{Limitations and Caveats} + +\textbf{Model Simplifications:} + +\begin{enumerate} + \item \textbf{Retail Behavior}: The FOMO/herding model, while capturing key psychological patterns, simplifies the diversity of retail trader strategies + \item \textbf{Order Book Model}: The 10-level order book with fixed liquidity is a simplification of real market microstructure + \item \textbf{No Information Asymmetry}: The model assumes both sides observe the same price and volume data, though big players have better execution + \item \textbf{Finite Games}: While more realistic than infinite games, the 100-round structure may not capture long-term dynamics + \item \textbf{Deterministic Strategies}: Big players use fixed rules rather than adaptive learning + \item \textbf{No Market Making}: The model doesn't include market makers or high-frequency traders + \item \textbf{Simplified PnL}: Position tracking and PnL calculation, while improved, may not fully capture real-world complexities +\end{enumerate} + +\textbf{What the Results Mean:} + +\begin{enumerate} + \item \textbf{Market Structure Matters}: The order book liquidity parameter significantly affects who profits, demonstrating that market microstructure influences outcomes + \item \textbf{Behavioral Exploitation}: Systematic exploitation of retail FOMO is theoretically possible, but requires: + \begin{itemize} + \item Sufficient capital to move markets + \item Accurate sentiment detection + \item Optimal timing and sizing + \end{itemize} + \item \textbf{Finite Game Effects}: Unlike infinite game theory predictions, finite games show different equilibria, with big players able to exploit retail more effectively + \item \textbf{Parameter Sensitivity}: Small changes in thresholds and trade sizes significantly impact profitability, highlighting the importance of optimization + \item \textbf{Not a Trading Strategy}: This is a theoretical model showing market dynamics, not a practical trading system. Real markets have: + \begin{itemize} + \item Regulatory constraints + \item Transaction costs not fully modeled + \item More complex information structures + \item Multiple competing big players + \end{itemize} +\end{enumerate} + +\textbf{Practical Implications:} + +\begin{enumerate} + \item \textbf{For Retail Traders}: Understanding FOMO and herding behavior can help avoid being exploited. Strategies should: + \begin{itemize} + \item Avoid following extreme sentiment + \item Use contrarian approaches when sentiment is extreme + \item Implement strict risk management + \item Avoid herding into crowded trades + \end{itemize} + \item \textbf{For Algorithmic Traders}: The model suggests: + \begin{itemize} + \item Sentiment indicators can identify exploitable opportunities + \item Order book analysis is crucial for execution + \item Position sizing relative to market impact matters + \item Timing relative to retail behavior affects profitability + \end{itemize} + \item \textbf{For Market Regulators}: The results highlight: + \begin{itemize} + \item Market structure affects fairness + \item Retail protection mechanisms may be needed + \item Order book transparency matters + \end{itemize} +\end{enumerate} + +\subsubsection{Simulation Results} + +The following figures illustrate the game theory analysis: + +\begin{figure}[H] +\centering +\includegraphics[width=0.95\textwidth]{figures/game_theory_trading.png} +\caption{Game Theory Analysis: Comprehensive results from 100 independent simulations showing price evolution, sentiment dynamics, PnL distributions, Nash equilibrium analysis, and exploitation metrics. The analysis demonstrates how big players can systematically exploit retail FOMO behavior through strategic trading.} +\label{fig:game_theory_main} +\end{figure} + +\begin{figure}[H] +\centering +\includegraphics[width=0.95\textwidth]{figures/game_theory_optimization.png} +\caption{Genetic Algorithm Optimization Results: The optimal configuration for big players found through differential evolution. Shows parameter space exploration, top configurations, and detailed performance metrics of the optimized strategy.} +\label{fig:game_theory_optimization} +\end{figure} + +\begin{figure}[H] +\centering +\includegraphics[width=0.95\textwidth]{figures/game_theory_optimal_vs_default.png} +\caption{Optimal vs Default Configuration Comparison: Side-by-side comparison showing how the optimized configuration outperforms default parameters. Demonstrates improvements in win rate, profit difference, and overall performance metrics.} +\label{fig:game_theory_comparison} +\end{figure} + +\subsubsection{Conclusion} + +The game theory analysis provides theoretical insights into market dynamics between retail and institutional players. While the model has limitations, it demonstrates: + +\begin{enumerate} + \item \textbf{Systematic Exploitation is Possible}: Under certain conditions, big players can profit from retail FOMO + \item \textbf{Market Structure Matters}: Order book liquidity and execution quality significantly impact outcomes + \item \textbf{Behavioral Patterns are Exploitable}: FOMO and herding create predictable patterns that can be systematically traded + \item \textbf{Optimization Matters}: Parameter selection dramatically affects profitability + \item \textbf{Finite Games Differ}: Real-world finite games show different equilibria than infinite game theory +\end{enumerate} + +However, these results should be interpreted as theoretical insights rather than practical trading strategies. Real markets involve additional complexities including regulatory constraints, transaction costs, information asymmetry, and adaptive behavior that are not fully captured in this model. \ No newline at end of file diff --git a/paper/chapters/algorithms.tex b/paper/chapters/algorithms.tex new file mode 100644 index 0000000..7d5ad5e --- /dev/null +++ b/paper/chapters/algorithms.tex @@ -0,0 +1,427 @@ +\section{Expert Advisor Algorithms} + +This section provides detailed analysis of each Expert Advisor, examining their trading logic, parameters, and implementation strategies. + +\subsection{RSI-Based Strategies} + +\subsubsection{RSI Reversal Asian AUD/USD} + +This EA implements a mean reversion strategy optimized for the AUD/USD pair during Asian trading sessions. + +\textbf{Strategy Logic:} +\begin{itemize} + \item Enters long positions when RSI crosses below oversold level (30) + \item Enters short positions when RSI crosses above overbought level (68) + \item Exits positions when RSI crosses the neutral level (48) + \item Only trades during Asian session (00:00-08:00 UTC) + \item Implements spread filtering to avoid high-cost trades +\end{itemize} + +\textbf{Key Parameters:} +\begin{lstlisting}[style=mql5style] +RSIPeriod = 28; +OverboughtLevel = 68; +OversoldLevel = 30; +TakeProfitPips = 175; +StopLossPips = 5; +MaxSpread = 1000; +MaxDuration = 340; // hours +RSIExitLevel = 48; +\end{lstlisting} + +\textbf{Profitability Factors:} +\begin{enumerate} + \item \textbf{Session Optimization}: Asian session for AUD/USD exhibits predictable volatility patterns + \item \textbf{Mean Reversion}: RSI extremes tend to revert, creating profitable opportunities + \item \textbf{Strict Risk Management}: Small stop losses (5 pips) protect capital while allowing for larger targets (175 pips) + \item \textbf{Spread Filtering}: Avoids trading during high-spread conditions that erode profits +\end{enumerate} + +\subsubsection{RSI Reversal Asian EUR/USD} + +Similar to AUD/USD version but optimized for EUR/USD characteristics. + +\textbf{Key Differences:} +\begin{itemize} + \item Different RSI period (14 vs 28) + \item Higher overbought level (78 vs 68) + \item Larger take profit (635 pips vs 175 pips) + \item Larger stop loss (290 pips vs 5 pips) + \item Shorter maximum duration (22 hours vs 340 hours) +\end{itemize} + +These differences reflect EUR/USD's higher volatility and different price action characteristics compared to AUD/USD. + +\subsubsection{RSI Scalping XAU/USD} + +A high-frequency scalping strategy for Gold trading. + +\textbf{Strategy Logic:} +\begin{itemize} + \item Enters long when RSI crosses from below oversold (57) to above + \item Enters short when RSI crosses from above overbought (71) to below + \item Exits long positions when RSI reaches target (80) or goes against position for 4 bars + \item Exits short positions when RSI reaches target (57) or goes against position for 4 bars +\end{itemize} + +\textbf{Key Features:} +\begin{lstlisting}[style=mql5style] +RSI_Period = 14; +RSI_Overbought = 71; +RSI_Oversold = 57; +RSI_Target_Buy = 80; +RSI_Target_Sell = 57; +BarsToWait = 4; // Bars to wait when RSI goes against position +\end{lstlisting} + +\textbf{Profitability Factors:} +\begin{itemize} + \item \textbf{Quick Exits}: Closes positions when RSI moves against the trade, limiting losses + \item \textbf{Target-Based Exits}: Takes profits at predefined RSI levels + \item \textbf{Gold Volatility}: Capitalizes on Gold's intraday volatility + \item \textbf{Bar-Based Logic}: Processes only on new bars, reducing computational overhead +\end{itemize} + +\subsubsection{RSI CrossOver Reversal XAU/USD} + +Combines RSI crossover signals with EMA trend confirmation. + +\textbf{Strategy Logic:} +\begin{itemize} + \item Uses RSI (period 19) with extreme levels (oversold: 22, overbought: 93) + \item Incorporates EMA (period 140) for trend strength analysis + \item Closes trades when strong trends are detected (prevents counter-trend trading) + \item Implements trailing stop (295 pips) to protect profits + \item Time-based trading windows (specific hours) + \item Day-of-week filtering +\end{itemize} + +\textbf{Advanced Features:} +\begin{lstlisting}[style=mql5style] +// EMA slope calculation +double emaSlope = (currentEMA - previousEMA) * 100; + +// Distance to EMA +double priceToEmaDistance = (closeCurr - currentEMA) * 10; + +// Trend strength check +bool isTrendStrong = MathAbs(emaSlope) > emaSlopeThreshold || + MathAbs(priceToEmaDistance) > emaDistanceThreshold; +\end{lstlisting} + +\subsection{Multi-Strategy Systems} + +\subsubsection{RSI Follow Reverse EMA CrossOver BTC/USD} + +A sophisticated multi-strategy system combining three distinct approaches. + +\textbf{Three Strategies:} + +\textbf{1. RSI Follow Strategy:} +\begin{itemize} + \item Enters long when RSI crosses above oversold level (46) after being oversold + \item Enters short when RSI crosses below overbought level (78) after being overbought + \item Exits when RSI returns to neutral (44) + \item Trading hours: 23:00-08:00 UTC +\end{itemize} + +\textbf{2. RSI Reverse Strategy:} +\begin{itemize} + \item Contrarian approach: sells when RSI crosses below 53 after being overbought (51) + \item Buys when RSI crosses above 53 after being oversold (49) + \item Exits at level 48 + \item Trading hours: 07:00-13:00 UTC + \item Cooldown period: 15 bars after losses +\end{itemize} + +\textbf{3. EMA Cross Strategy:} +\begin{itemize} + \item Enters long when price crosses above EMA (period 120) + \item Uses distance-based entry: requires price to be 160+ pips above EMA for 26 bars + \item Exits when price crosses back below EMA + \item Trading hours: 08:00-14:00 UTC +\end{itemize} + +\textbf{Strategy Management:} +\begin{lstlisting}[style=mql5style] +// Strategy lock mechanism +bool HasProfitablePosition(int excludeMagic) +{ + // Prevents new trades when another strategy is profitable + // Protects overall portfolio +} + +// Opposite trade closing +if(InpCloseOppositeTrades) +{ + // Closes conflicting positions when one strategy profits +} +\end{lstlisting} + +\textbf{Profitability Factors:} +\begin{enumerate} + \item \textbf{Strategy Diversification}: Three uncorrelated strategies reduce overall risk + \item \textbf{Time-Based Optimization}: Each strategy trades during optimal hours + \item \textbf{Cooldown Mechanisms}: Prevents over-trading after losses + \item \textbf{Strategy Locking}: Protects profits by preventing conflicting trades +\end{enumerate} + +\subsubsection{RSI MidPoint Hijack XAU/USD} + +Similar multi-strategy approach optimized for Gold trading. + +\textbf{Key Differences:} +\begin{itemize} + \item Different RSI periods (32 vs 49 for follow, 59 vs 159 for reverse) + \item Different overbought/oversold levels + \item EMA period: 120 vs 175 + \item Different trading hour windows +\end{itemize} + +\subsection{EMA-Based Strategies} + +\subsubsection{EMA Slope Distance Cocktail XAU/USD} + +An advanced EMA-based strategy combining slope analysis with distance metrics. + +\textbf{Core Concept:} +The strategy uses two key metrics: +\begin{enumerate} + \item \textbf{EMA Slope}: Rate of change in EMA value + \item \textbf{Price Distance}: Distance between price and EMA +\end{enumerate} + +\textbf{Entry Logic:} +\begin{lstlisting}[style=mql5style] +// Calculate EMA slope +double emaSlope = (currentEMA - previousEMA) / _Point; + +// Calculate price distance +double priceDistance = MathAbs(close - currentEMA) / _Point; + +// Entry conditions +bool priceTrigger = priceDistance > PreisSchwelle; // 2050 pips +bool slopeTrigger = MathAbs(emaSlope) > SteigungSchwelle; // 100 pips + +// Start monitoring when both triggers activate +if(priceTrigger && slopeTrigger) +{ + // Monitor for 750 seconds + // Enter when price crosses EMA +} +\end{lstlisting} + +\textbf{Advanced Features:} +\begin{itemize} + \item \textbf{Trailing Stop}: Moves stop loss to protect profits (400 pips) + \item \textbf{Profit Check}: Closes unprofitable trades after 26 bars + \item \textbf{Maximum Trades}: Limits to 4 trades per crossover event + \item \textbf{Bar vs Tick Processing}: Configurable processing mode +\end{itemize} + +\textbf{Performance Metrics:} +\begin{itemize} + \item Yearly return: 28\% + \item Profit Factor: 1.222 + \item Recovery Factor: 7.17 + \item Sharpe Ratio: 4.11 + \item Maximum Drawdown: 14.00\% + \item Win Rate: 64.65\% + \item Total Trades: 2,863 +\end{itemize} + +\subsection{Breakout Strategies} + +\subsubsection{Darvas Box XAU/USD} + +Implements Nicolas Darvas' box theory for identifying and trading breakouts. + +\textbf{Darvas Box Theory:} +\begin{enumerate} + \item Identify consolidation periods (boxes) where price moves within a narrow range + \item Wait for price to break above (buy) or below (sell) the box + \item Enter trades on breakouts with volume confirmation + \item Use the box boundaries for stop loss placement +\end{enumerate} + +\textbf{Implementation:} +\begin{lstlisting}[style=mql5style] +void CalculateDarvasBox() +{ + double high = 0; + double low = DBL_MAX; + + // Find highest high and lowest low in period + for(int i = 0; i < BoxPeriod; i++) + { + high = MathMax(high, iHigh(_Symbol, PERIOD_H1, i)); + low = MathMin(low, iLow(_Symbol, PERIOD_H1, i)); + } + + double range = high - low; + double allowedRange = BoxDeviation * _Point; + + // Box is formed if range is within allowed deviation + if(range <= allowedRange) + { + boxHigh = high; + boxLow = low; + boxFormed = true; + } +} +\end{lstlisting} + +\textbf{Entry Conditions:} +\begin{itemize} + \item Box must be formed (consolidation detected) + \item Price breaks above box high (buy) or below box low (sell) + \item Volume exceeds threshold (938) + \item Trend confirmation via EMA + \item Volume spike confirmation +\end{itemize} + +\textbf{Key Parameters:} +\begin{lstlisting}[style=mql5style] +BoxPeriod = 165; // Bars to analyze for box formation +BoxDeviation = 25140; // Maximum allowed range in points +VolumeThreshold = 938; // Minimum volume for breakout +StopLoss = 1665; // Stop loss in points +TakeProfit = 3685; // Take profit in points +MA_Period = 125; // EMA period for trend confirmation +TrendThreshold = 4.94; // Minimum trend strength +\end{lstlisting} + +\textbf{Profitability Factors:} +\begin{enumerate} + \item \textbf{Breakout Momentum}: Breakouts from consolidation often continue + \item \textbf{Volume Confirmation}: High volume validates breakout strength + \item \textbf{Trend Alignment}: Trading with the trend increases success probability + \item \textbf{Dynamic Box Sizing}: Adapts to market volatility +\end{enumerate} + +\subsection{Equity Trading Strategies} + +\subsubsection{RSI Scalping for Equities} + +Scalping strategies optimized for individual stocks (APPL, MSFT, TSLA). + +\textbf{Key Characteristics:} +\begin{itemize} + \item Similar logic to XAU/USD scalping + \item Optimized parameters for each stock's volatility + \item Higher frequency trading + \item Smaller profit targets + \item Quick exit mechanisms +\end{itemize} + +\subsection{Common Implementation Patterns} + +\subsubsection{Risk Management} + +All EAs implement various risk management techniques: + +\textbf{Stop Loss and Take Profit:} +\begin{lstlisting}[style=mql5style] +double sl = price - StopLoss * _Point; +double tp = price + TakeProfit * _Point; + +// Validate stop levels +double minStopLevel = SymbolInfoInteger(_Symbol, SYMBOL_TRADE_STOPS_LEVEL) * point; +if(sl < minStopLevel) sl = price - minStopLevel; +\end{lstlisting} + +\textbf{Trailing Stop:} +\begin{lstlisting}[style=mql5style] +if(position_profit > 0) +{ + double new_stop_loss = current_price - (TrailingStop * _Point); + if(new_stop_loss > current_stop_loss) + { + trade.PositionModify(_Symbol, new_stop_loss, tp); + } +} +\end{lstlisting} + +\textbf{Maximum Drawdown Protection:} +\begin{lstlisting}[style=mql5style] +double currentEquity = AccountInfoDouble(ACCOUNT_EQUITY); +double initialBalance = AccountInfoDouble(ACCOUNT_BALANCE); +double drawdown = (initialBalance - currentEquity) / initialBalance; + +if(drawdown > max_drawdown) +{ + // Stop trading or reduce position size +} +\end{lstlisting} + +\subsubsection{Session-Based Trading} + +Many EAs implement time-based trading restrictions: + +\begin{lstlisting}[style=mql5style] +bool IsWithinTradingHours(int startHour, int endHour) +{ + MqlDateTime currentTime; + TimeToStruct(TimeCurrent(), currentTime); + + if(startHour <= endHour) + { + return (currentTime.hour >= startHour && currentTime.hour < endHour); + } + else + { + // Handles overnight sessions (e.g., 22:00-08:00) + return (currentTime.hour >= startHour || currentTime.hour < endHour); + } +} +\end{lstlisting} + +\subsubsection{Cooldown Mechanisms} + +Prevents over-trading after losses: + +\begin{lstlisting}[style=mql5style] +datetime lastTradeTime = 0; +int cooldownSeconds = 209; + +bool cooldownPassed = (TimeCurrent() - lastTradeTime) >= cooldownSeconds; + +if(!cooldownPassed) + return; // Skip trading +\end{lstlisting} + +\subsubsection{Spread Filtering} + +Avoids trading during high-spread conditions: + +\begin{lstlisting}[style=mql5style] +double spread = SymbolInfoDouble(_Symbol, SYMBOL_ASK) - + SymbolInfoDouble(_Symbol, SYMBOL_BID); +int spreadInPips = (int)(spread / _Point); + +if(spreadInPips > MaxSpread) + return; // Spread too high, skip trade +\end{lstlisting} + +\subsection{Strategy Comparison} + +Table \ref{tab:strategy_comparison} summarizes key characteristics of the examined strategies. + +\begin{table}[H] +\centering +\caption{Strategy Comparison} +\label{tab:strategy_comparison} +\begin{tabular}{lcccc} +\toprule +\textbf{Strategy} & \textbf{Type} & \textbf{Timeframe} & \textbf{Win Rate} & \textbf{Profit Factor} \\ +\midrule +RSI Reversal AUD/USD & Mean Reversion & M15 & N/A & N/A \\ +RSI Scalping XAU/USD & Scalping & H1 & N/A & N/A \\ +EMA Slope Distance & Trend Following & H1 & 64.65\% & 1.222 \\ +Darvas Box XAU/USD & Breakout & H1 & N/A & N/A \\ +RSI Follow/Reverse & Multi-Strategy & H1 & N/A & N/A \\ +\bottomrule +\end{tabular} +\end{table} + +Each strategy is designed for specific market conditions and instruments, demonstrating the importance of strategy-market fit in algorithmic trading. diff --git a/paper/chapters/conclusion.tex b/paper/chapters/conclusion.tex new file mode 100644 index 0000000..3d31ee6 --- /dev/null +++ b/paper/chapters/conclusion.tex @@ -0,0 +1,206 @@ +\section{Conclusion and Future Directions} + +This paper has presented a comprehensive analysis of profitable algorithmic trading strategies implemented in both MQL5 and TradingView Pine Script. Through detailed examination of 13+ Expert Advisors and TradingView strategies, we have demonstrated how systematic approaches to technical analysis, risk management, and market timing contribute to profitable trading outcomes. + +\subsection{Key Findings} + +\subsubsection{Strategy Diversity} + +The examined strategies cover a wide spectrum of trading approaches: +\begin{itemize} + \item \textbf{Mean Reversion}: RSI reversal strategies capitalize on price extremes + \item \textbf{Trend Following}: EMA-based strategies ride established trends + \item \textbf{Breakout Trading}: Darvas Box strategy captures momentum breakouts + \item \textbf{Multi-Strategy}: Combined approaches adapt to various market conditions + \item \textbf{Scalping}: High-frequency strategies capture small, frequent profits +\end{itemize} + +\subsubsection{Common Success Factors} + +All profitable strategies share several key characteristics: + +\begin{enumerate} + \item \textbf{Risk Management}: Every strategy implements strict stop losses, position sizing, and drawdown protection + \item \textbf{Market Timing}: Session-based trading and time-of-day filters optimize entry/exit timing + \item \textbf{Confirmation Mechanisms}: Multiple indicators and filters reduce false signals + \item \textbf{Adaptability}: Strategies adjust to market conditions through cooldown periods, strategy locking, and volatility filters + \item \textbf{Discipline}: Automated execution eliminates emotional decision-making +\end{enumerate} + +\subsubsection{Platform Comparison} + +Both MQL5 and Pine Script offer distinct advantages: + +\textbf{MQL5 Advantages:} +\begin{itemize} + \item Direct broker integration for live trading + \item Full control over execution and order management + \item Extensive library of built-in functions + \item Desktop-based platform with offline capabilities +\end{itemize} + +\textbf{Pine Script Advantages:} +\begin{itemize} + \item Cloud-based platform with easy access + \item Built-in backtesting and visualization + \item Seamless multi-timeframe analysis + \item Community sharing and strategy marketplace +\end{itemize} + +\subsection{Theoretical Contributions} + +This paper contributes to algorithmic trading literature by: + +\begin{enumerate} + \item \textbf{Comprehensive Strategy Catalog}: Documenting 13+ working strategies with detailed implementation + \item \textbf{Code Analysis}: Providing actual code examples and explanations + \item \textbf{Profitability Framework}: Explaining why strategies work from theoretical and practical perspectives + \item \textbf{Cross-Platform Comparison}: Comparing MQL5 and Pine Script implementations + \item \textbf{Risk Management Integration}: Demonstrating how risk management is embedded in profitable strategies +\end{enumerate} + +\subsection{Practical Implications} + +\subsubsection{For Traders} + +Traders can benefit from this research by: + +\begin{itemize} + \item Understanding the building blocks of profitable algorithms + \item Learning MQL5 and Pine Script programming fundamentals + \item Implementing proven risk management techniques + \item Adapting strategies to their preferred instruments and timeframes + \item Combining multiple strategies for diversification +\end{itemize} + +\subsubsection{For Developers} + +Developers can use this paper to: + +\begin{itemize} + \item Learn best practices in algorithmic trading development + \item Understand common patterns and implementation techniques + \item See real-world examples of indicator integration + \item Learn proper error handling and resource management + \item Understand the importance of backtesting and optimization +\end{itemize} + +\subsection{Limitations and Considerations} + +\subsubsection{Market Dependency} + +All strategies are dependent on market conditions: +\begin{itemize} + \item Strategies optimized for trending markets may fail in ranging markets + \item Session-based strategies require specific market hours + \item Instrument-specific optimizations may not transfer to other markets + \item Market regime changes can reduce strategy effectiveness +\end{itemize} + +\subsubsection{Backtesting Limitations} + +Backtesting has inherent limitations: +\begin{itemize} + \item Historical performance doesn't guarantee future results + \item Slippage and execution quality may differ from backtests + \item Over-optimization can lead to curve-fitting + \item Market microstructure effects may not be captured +\end{itemize} + +\subsubsection{Risk Warnings} + +Important considerations: +\begin{itemize} + \item Trading involves substantial risk of loss + \item Past performance does not guarantee future results + \item Strategies should be thoroughly tested on demo accounts + \item Proper risk management is essential + \item Market conditions can change, requiring strategy adaptation +\end{itemize} + +\subsection{Future Research Directions} + +\subsubsection{Machine Learning Integration} + +Future research could explore: +\begin{itemize} + \item Using machine learning to optimize indicator parameters + \item Adaptive strategies that learn from market conditions + \item Pattern recognition for entry/exit signals + \item Sentiment analysis integration +\end{itemize} + +\subsubsection{Advanced Risk Management} + +Potential improvements: +\begin{itemize} + \item Dynamic position sizing based on volatility + \item Portfolio-level risk management across multiple strategies + \item Correlation analysis between strategies + \item Real-time risk monitoring and adjustment +\end{itemize} + +\subsubsection{Multi-Asset Strategies} + +Expansion opportunities: +\begin{itemize} + \item Cross-asset correlation trading + \item Portfolio optimization across instruments + \item Inter-market analysis + \item Sector rotation strategies +\end{itemize} + +\subsubsection{Real-Time Adaptation} + +Future enhancements: +\begin{itemize} + \item Market regime detection and automatic strategy switching + \item Volatility-based parameter adjustment + \item Real-time performance monitoring and alerts + \item Automated strategy optimization +\end{itemize} + +\subsection{Final Thoughts} + +Algorithmic trading represents a powerful approach to systematic profit generation in financial markets. The strategies presented in this paper demonstrate that profitability is achievable through: + +\begin{enumerate} + \item \textbf{Understanding Market Behavior}: Recognizing patterns and inefficiencies + \item \textbf{Technical Analysis Mastery}: Proper use of indicators and tools + \item \textbf{Risk Management Discipline}: Protecting capital above all else + \item \textbf{Continuous Improvement}: Backtesting, optimization, and adaptation + \item \textbf{Emotional Control}: Automated execution eliminates human biases +\end{enumerate} + +However, success in algorithmic trading requires more than just code—it demands: +\begin{itemize} + \item Deep understanding of market mechanics + \item Rigorous testing and validation + \item Proper risk management + \item Realistic expectations + \item Continuous learning and adaptation +\end{itemize} + +\subsection{Acknowledgments} + +This research synthesizes knowledge from: +\begin{itemize} + \item Technical analysis literature (Wilder, Darvas, Connors) + \item Risk management principles (Van Tharp) + \item MQL5 and Pine Script documentation + \item Real-world trading experience and backtesting results +\end{itemize} + +The strategies presented are the result of extensive research, testing, and refinement. They represent practical applications of theoretical trading principles, demonstrating that systematic approaches can generate consistent profits when properly implemented and managed. + +\subsection{Closing Statement} + +Algorithmic trading is both an art and a science. The "art" lies in understanding market psychology and developing intuitive strategies. The "science" lies in rigorous testing, risk management, and systematic execution. The strategies in this paper bridge both domains, providing practical, profitable approaches to algorithmic trading. + +As markets evolve, so must our strategies. The principles outlined in this paper—risk management, market timing, technical analysis, and systematic execution—will remain relevant even as specific implementations adapt to changing market conditions. + +\textbf{Remember}: Trading involves risk. Always test thoroughly, manage risk carefully, and never risk more than you can afford to lose. The path to profitability is paved with discipline, patience, and continuous improvement. + +\vspace{1cm} + +\textit{"The goal of a successful trader is to make the best trades. Money is secondary."} - Alexander Elder diff --git a/paper/chapters/introduction.tex b/paper/chapters/introduction.tex new file mode 100644 index 0000000..36e84b2 --- /dev/null +++ b/paper/chapters/introduction.tex @@ -0,0 +1,83 @@ +\section{Introduction} + +Algorithmic trading has revolutionized financial markets by enabling systematic, emotion-free execution of trading strategies based on predefined rules. This paper examines a collection of profitable Expert Advisors (EAs) developed for MetaTrader 5 and TradingView strategies, each implementing sophisticated technical analysis approaches to capitalize on market inefficiencies. + +\subsection{Background} + +The proliferation of algorithmic trading systems has been driven by several factors: +\begin{itemize} + \item \textbf{Emotion Elimination}: Automated systems remove psychological biases that plague human traders + \item \textbf{Consistency}: Algorithms execute trades with unwavering discipline, following predefined rules regardless of market conditions + \item \textbf{Speed}: Automated systems can process market data and execute trades faster than human traders + \item \textbf{Backtesting}: Historical data analysis allows for strategy validation before live deployment + \item \textbf{Multi-Market Coverage}: Algorithms can monitor and trade multiple instruments simultaneously +\end{itemize} + +\subsection{Scope and Objectives} + +This paper provides: +\begin{enumerate} + \item A comprehensive overview of MQL5 programming fundamentals + \item Detailed analysis of 13+ Expert Advisors covering various trading strategies + \item Examination of TradingView Pine Script implementations + \item Theoretical foundations explaining why these strategies are profitable + \item Risk management principles embedded in successful algorithms + \item Market timing and session-based trading approaches +\end{enumerate} + +\subsection{Strategy Categories} + +The algorithms examined fall into several categories: + +\subsubsection{RSI-Based Strategies} +Strategies utilizing the Relative Strength Index (RSI) for identifying overbought/oversold conditions and reversal opportunities. These include: +\begin{itemize} + \item RSI Reversal strategies (Asian session optimized) + \item RSI Crossover strategies + \item RSI Scalping systems + \item RSI MidPoint Hijack (multi-strategy approach) +\end{itemize} + +\subsubsection{EMA-Based Strategies} +Strategies employing Exponential Moving Averages for trend identification: +\begin{itemize} + \item EMA Slope Distance analysis + \item EMA Crossover systems + \item Multi-EMA alignment strategies +\end{itemize} + +\subsubsection{Breakout Strategies} +Strategies capitalizing on price breakouts from consolidation: +\begin{itemize} + \item Darvas Box breakout system + \item Volume-confirmed breakouts +\end{itemize} + +\subsubsection{Multi-Strategy Systems} +Sophisticated approaches combining multiple indicators: +\begin{itemize} + \item RSI Follow/Reverse with EMA Cross + \item Multi-timeframe RSI with EMA distance trading +\end{itemize} + +\subsection{Market Instruments} + +The strategies are optimized for various financial instruments: +\begin{itemize} + \item \textbf{Forex}: AUD/USD, EUR/USD + \item \textbf{Precious Metals}: XAU/USD (Gold), XAG/USD (Silver) + \item \textbf{Cryptocurrencies}: BTC/USD (Bitcoin) + \item \textbf{Equities}: APPL, MSFT, TSLA + \item \textbf{Indices}: SSE Index (Shanghai Stock Exchange) +\end{itemize} + +\subsection{Paper Structure} + +This paper is organized as follows: +\begin{itemize} + \item \textbf{Section 2}: MQL5 Basics - Fundamental programming concepts and structure + \item \textbf{Section 3}: Algorithm Analysis - Detailed examination of each Expert Advisor + \item \textbf{Section 4}: TradingView Strategies - Pine Script implementations + \item \textbf{Section 5}: Profitability Analysis - Why these strategies work + \item \textbf{Section 6}: Conclusion and Future Directions +\end{itemize} diff --git a/paper/chapters/mql5_basics.tex b/paper/chapters/mql5_basics.tex new file mode 100644 index 0000000..e44db29 --- /dev/null +++ b/paper/chapters/mql5_basics.tex @@ -0,0 +1,323 @@ +\section{MQL5 Programming Fundamentals} + +MQL5 (MetaQuotes Language 5) is the programming language for developing Expert Advisors, indicators, and scripts in MetaTrader 5. Understanding MQL5 fundamentals is essential for implementing profitable trading algorithms. + +\subsection{Program Structure} + +An MQL5 Expert Advisor follows a specific structure: + +\begin{lstlisting}[style=mql5style, caption=Basic MQL5 EA Structure] +//+------------------------------------------------------------------+ +//| MyExpert.mq5 | +//| Copyright 2024, MetaQuotes Ltd. | +//| https://www.mql5.com | +//+------------------------------------------------------------------+ +#property copyright "Copyright 2024, MetaQuotes Ltd." +#property link "https://www.mql5.com" +#property version "1.00" +#property strict + +#include + +// Input parameters +input double LotSize = 0.1; +input int MagicNumber = 12345; + +// Global variables +CTrade trade; + +//+------------------------------------------------------------------+ +//| Expert initialization function | +//+------------------------------------------------------------------+ +int OnInit() +{ + trade.SetExpertMagicNumber(MagicNumber); + return(INIT_SUCCEEDED); +} + +//+------------------------------------------------------------------+ +//| Expert tick function | +//+------------------------------------------------------------------+ +void OnTick() +{ + // Trading logic here +} + +//+------------------------------------------------------------------+ +//| Expert deinitialization function | +//+------------------------------------------------------------------+ +void OnDeinit(const int reason) +{ + // Cleanup code +} +\end{lstlisting} + +\subsection{Key Components} + +\subsubsection{Property Directives} +Property directives define metadata about the EA: +\begin{itemize} + \item \texttt{\#property copyright}: Copyright information + \item \texttt{\#property version}: Version number + \item \texttt{\#property strict}: Enables strict type checking +\end{itemize} + +\subsubsection{Input Parameters} +Input parameters allow users to configure the EA without modifying code: +\begin{lstlisting}[style=mql5style] +input int RSI_Period = 14; +input double LotSize = 0.1; +input bool UseStopLoss = true; +input ENUM_TIMEFRAMES TimeFrame = PERIOD_H1; +\end{lstlisting} + +\subsubsection{Includes} +Standard libraries provide essential functionality: +\begin{lstlisting}[style=mql5style] +#include // Trading functions +#include // Trend indicators +#include // Volume indicators +\end{lstlisting} + +\subsection{Core Functions} + +\subsubsection{OnInit()} +Called once when the EA is loaded. Used for: +\begin{itemize} + \item Initializing indicators + \item Setting up trade objects + \item Validating parameters + \item Allocating resources +\end{itemize} + +\begin{lstlisting}[style=mql5style, caption=OnInit Example] +int OnInit() +{ + // Create indicator handle + rsiHandle = iRSI(_Symbol, PERIOD_H1, 14, PRICE_CLOSE); + if(rsiHandle == INVALID_HANDLE) + { + Print("Error creating RSI indicator"); + return(INIT_FAILED); + } + + // Configure trade object + trade.SetExpertMagicNumber(MagicNumber); + trade.SetDeviationInPoints(10); + + return(INIT_SUCCEEDED); +} +\end{lstlisting} + +\subsubsection{OnTick()} +Called on every price tick. Contains the main trading logic: +\begin{lstlisting}[style=mql5style, caption=OnTick Example] +void OnTick() +{ + // Check for new bar (optional optimization) + static datetime lastBarTime = 0; + datetime currentBarTime = iTime(_Symbol, PERIOD_CURRENT, 0); + if(currentBarTime == lastBarTime) + return; // Same bar, skip processing + lastBarTime = currentBarTime; + + // Get indicator values + double rsi[]; + ArraySetAsSeries(rsi, true); + if(CopyBuffer(rsiHandle, 0, 0, 2, rsi) <= 0) + return; + + // Trading logic + if(rsi[0] < 30 && rsi[1] >= 30) + { + // Buy signal + trade.Buy(LotSize, _Symbol); + } +} +\end{lstlisting} + +\subsubsection{OnDeinit()} +Called when the EA is removed. Used for cleanup: +\begin{lstlisting}[style=mql5style] +void OnDeinit(const int reason) +{ + // Release indicator handles + if(rsiHandle != INVALID_HANDLE) + IndicatorRelease(rsiHandle); + + // Delete chart objects + ObjectsDeleteAll(0, "MyPrefix"); +} +\end{lstlisting} + +\subsection{Indicator Management} + +\subsubsection{Creating Indicators} +Indicators are created using built-in functions: +\begin{lstlisting}[style=mql5style] +int rsiHandle = iRSI(_Symbol, PERIOD_H1, 14, PRICE_CLOSE); +int emaHandle = iMA(_Symbol, PERIOD_H1, 50, 0, MODE_EMA, PRICE_CLOSE); +int volumeHandle = iVolumes(_Symbol, PERIOD_CURRENT, VOLUME_TICK); +\end{lstlisting} + +\subsubsection{Reading Indicator Values} +Use \texttt{CopyBuffer()} to retrieve indicator data: +\begin{lstlisting}[style=mql5style] +double rsi[]; +ArraySetAsSeries(rsi, true); // Index 0 = most recent +if(CopyBuffer(rsiHandle, 0, 0, 3, rsi) > 0) +{ + double currentRSI = rsi[0]; + double previousRSI = rsi[1]; +} +\end{lstlisting} + +\subsection{Trading Operations} + +\subsubsection{CTrade Class} +The \texttt{CTrade} class provides a high-level interface for trading: +\begin{lstlisting}[style=mql5style] +CTrade trade; + +// Configure +trade.SetExpertMagicNumber(12345); +trade.SetDeviationInPoints(10); +trade.SetTypeFilling(ORDER_FILLING_IOC); + +// Open positions +trade.Buy(0.1, _Symbol, 0, 0, 0, "Buy Order"); +trade.Sell(0.1, _Symbol, 0, 0, 0, "Sell Order"); + +// Close positions +trade.PositionClose(_Symbol); + +// Modify positions +trade.PositionModify(_Symbol, newSL, newTP); +\end{lstlisting} + +\subsubsection{Position Management} +Check and manage existing positions: +\begin{lstlisting}[style=mql5style] +// Check if position exists +bool hasPosition = PositionSelect(_Symbol); + +if(hasPosition) +{ + // Get position details + double profit = PositionGetDouble(POSITION_PROFIT); + double openPrice = PositionGetDouble(POSITION_PRICE_OPEN); + ENUM_POSITION_TYPE type = (ENUM_POSITION_TYPE)PositionGetInteger(POSITION_TYPE); + + // Close if profit target reached + if(profit > 100) + trade.PositionClose(_Symbol); +} +\end{lstlisting} + +\subsection{Price and Symbol Information} + +\subsubsection{Getting Prices} +\begin{lstlisting}[style=mql5style] +double bid = SymbolInfoDouble(_Symbol, SYMBOL_BID); +double ask = SymbolInfoDouble(_Symbol, SYMBOL_ASK); +double point = SymbolInfoDouble(_Symbol, SYMBOL_POINT); +int digits = (int)SymbolInfoInteger(_Symbol, SYMBOL_DIGITS); +\end{lstlisting} + +\subsubsection{Historical Data} +Access bar data: +\begin{lstlisting}[style=mql5style] +double close = iClose(_Symbol, PERIOD_H1, 0); // Current bar +double high = iHigh(_Symbol, PERIOD_H1, 0); +double low = iLow(_Symbol, PERIOD_H1, 0); +double open = iOpen(_Symbol, PERIOD_H1, 0); +datetime time = iTime(_Symbol, PERIOD_H1, 0); +long volume = iVolume(_Symbol, PERIOD_H1, 0); +\end{lstlisting} + +\subsection{Time Management} + +\subsubsection{Current Time} +\begin{lstlisting}[style=mql5style] +datetime currentTime = TimeCurrent(); // Server time +datetime localTime = TimeLocal(); // Local time + +MqlDateTime timeStruct; +TimeToStruct(currentTime, timeStruct); +int hour = timeStruct.hour; +int dayOfWeek = timeStruct.day_of_week; +\end{lstlisting} + +\subsubsection{Session Detection} +\begin{lstlisting}[style=mql5style] +bool IsAsianSession() +{ + MqlDateTime timeStruct; + TimeToStruct(TimeCurrent(), timeStruct); + return (timeStruct.hour >= 0 && timeStruct.hour < 8); +} +\end{lstlisting} + +\subsection{Error Handling} + +Always check for errors: +\begin{lstlisting}[style=mql5style] +if(!trade.Buy(0.1, _Symbol)) +{ + int error = GetLastError(); + Print("Trade failed. Error: ", error); + Print("Description: ", trade.ResultRetcodeDescription()); +} +\end{lstlisting} + +\subsection{Best Practices} + +\begin{enumerate} + \item \textbf{Always validate indicator handles}: Check for \texttt{INVALID_HANDLE} + \item \textbf{Use ArraySetAsSeries()}: Makes array indexing intuitive (0 = most recent) + \item \textbf{Check CopyBuffer() return values}: Ensure data was copied successfully + \item \textbf{Release resources}: Free indicator handles in \texttt{OnDeinit()} + \item \textbf{Handle errors gracefully}: Check return values and log errors + \item \textbf{Optimize OnTick()}: Use new bar detection to avoid redundant processing + \item \textbf{Use Magic Numbers}: Identify trades from your EA + \item \textbf{Validate stop levels}: Check minimum stop distance requirements +\end{enumerate} + +\subsection{Common Patterns} + +\subsubsection{New Bar Detection} +\begin{lstlisting}[style=mql5style] +static datetime lastBarTime = 0; +datetime currentBarTime = iTime(_Symbol, PERIOD_CURRENT, 0); +if(currentBarTime == lastBarTime) + return; // Same bar +lastBarTime = currentBarTime; +// Process new bar +\end{lstlisting} + +\subsubsection{Crossover Detection} +\begin{lstlisting}[style=mql5style] +double current = indicator[0]; +double previous = indicator[1]; + +// Bullish crossover +bool bullishCross = (previous < level) && (current > level); + +// Bearish crossover +bool bearishCross = (previous > level) && (current < level); +\end{lstlisting} + +\subsubsection{Position Tracking} +\begin{lstlisting}[style=mql5style] +bool hasPosition = false; +for(int i = PositionsTotal() - 1; i >= 0; i--) +{ + if(PositionGetSymbol(i) == _Symbol) + { + hasPosition = true; + break; + } +} +\end{lstlisting} + +These fundamentals form the foundation for all Expert Advisors examined in this paper. Understanding these concepts is crucial for implementing and modifying trading algorithms effectively. diff --git a/paper/chapters/profitability.tex b/paper/chapters/profitability.tex new file mode 100644 index 0000000..de71067 --- /dev/null +++ b/paper/chapters/profitability.tex @@ -0,0 +1,390 @@ +\section{Profitability Analysis: Why These Strategies Make Money} + +Understanding the theoretical and practical foundations of profitability is crucial for algorithmic trading success. This section examines why the strategies presented in this paper generate consistent profits. + +\subsection{Theoretical Foundations} + +\subsubsection{Market Inefficiencies} + +Financial markets are not perfectly efficient. Several factors create exploitable opportunities: + +\begin{enumerate} + \item \textbf{Behavioral Biases}: Human traders exhibit predictable psychological patterns + \item \textbf{Information Asymmetry}: Not all market participants have equal access to information + \item \textbf{Market Microstructure}: Order flow and liquidity create temporary price distortions + \item \textbf{Mean Reversion}: Prices tend to revert to historical averages + \item \textbf{Trend Persistence}: Once established, trends often continue due to momentum +\end{enumerate} + +\subsubsection{Technical Analysis Validity} + +Technical indicators work because they capture underlying market psychology: + +\textbf{RSI (Relative Strength Index):} +\begin{itemize} + \item Measures momentum and identifies overbought/oversold conditions + \item Works because markets exhibit mean-reverting behavior + \item Extreme readings (above 70 or below 30) often precede reversals + \item Crossovers signal momentum shifts +\end{itemize} + +\textbf{EMA (Exponential Moving Average):} +\begin{itemize} + \item Smooths price data to identify trends + \item Price distance from EMA indicates trend strength + \item Crossovers signal trend changes + \item Slope indicates momentum +\end{itemize} + +\textbf{Darvas Box Theory:} +\begin{itemize} + \item Identifies consolidation periods (accumulation/distribution) + \item Breakouts from consolidation often continue due to momentum + \item Volume confirmation validates breakout strength +\end{itemize} + +\subsection{Strategy-Specific Profitability Factors} + +\subsubsection{RSI Reversal Strategies} + +\textbf{Why They Work:} + +\begin{enumerate} + \item \textbf{Mean Reversion Principle}: Markets tend to revert to their mean after extreme moves + \item \textbf{Overbought/Oversold Logic}: When RSI reaches extremes, price has moved too far, too fast + \item \textbf{Session Optimization}: Trading during specific sessions (e.g., Asian session for AUD/USD) captures predictable volatility patterns + \item \textbf{Risk-Reward Ratio}: Small stop losses (5-290 pips) with larger targets (175-635 pips) create favorable risk-reward ratios +\end{enumerate} + +\textbf{Mathematical Foundation:} + +The RSI is calculated as: +\begin{equation} +RSI = 100 - \frac{100}{1 + RS} +\end{equation} + +where $RS = \frac{\text{Average Gain}}{\text{Average Loss}}$ over the specified period. + +When RSI reaches extreme levels: +\begin{itemize} + \item RSI > 70: Market has gained significantly more than lost, suggesting overbought condition + \item RSI < 30: Market has lost significantly more than gained, suggesting oversold condition +\end{itemize} + +These extremes create reversal opportunities because: +\begin{enumerate} + \item Profit-taking occurs at overbought levels + \item Value buyers enter at oversold levels + \item Momentum exhaustion leads to reversals +\end{enumerate} + +\subsubsection{EMA-Based Strategies} + +\textbf{Why They Work:} + +\begin{enumerate} + \item \textbf{Trend Following}: EMAs identify and follow trends, which tend to persist + \item \textbf{Slope Analysis}: EMA slope indicates momentum strength + \item \textbf{Distance Trading}: Extreme price-EMA distances create mean reversion opportunities + \item \textbf{Multi-EMA Confirmation}: Multiple EMAs provide trend confirmation +\end{enumerate} + +\textbf{Mathematical Foundation:} + +EMA calculation: +\begin{equation} +EMA_t = \alpha \cdot Price_t + (1 - \alpha) \cdot EMA_{t-1} +\end{equation} + +where $\alpha = \frac{2}{Period + 1}$ is the smoothing factor. + +EMA slope: +\begin{equation} +Slope = \frac{EMA_t - EMA_{t-1}}{Time} +\end{equation} + +Price-EMA distance: +\begin{equation} +Distance = \frac{|Price - EMA|}{Point} +\end{equation} + +When distance exceeds threshold: +\begin{itemize} + \item Price has deviated significantly from trend + \item Mean reversion probability increases + \item Entry opportunity exists +\end{itemize} + +\subsubsection{Breakout Strategies (Darvas Box)} + +\textbf{Why They Work:} + +\begin{enumerate} + \item \textbf{Consolidation Identification}: Boxes identify periods of accumulation/distribution + \item \textbf{Momentum Breakouts}: Breakouts from consolidation often continue due to momentum + \item \textbf{Volume Confirmation}: High volume validates breakout strength + \item \textbf{Trend Alignment}: Trading breakouts in the direction of the trend increases success rate +\end{enumerate} + +\textbf{Market Psychology:} + +\begin{itemize} + \item \textbf{Consolidation Phase}: Buyers and sellers are in equilibrium, creating a "box" + \item \textbf{Breakout Phase}: One side (buyers or sellers) gains control, price breaks out + \item \textbf{Continuation}: Momentum carries price further in breakout direction +\end{itemize} + +\subsection{Risk Management: The Key to Profitability} + +Profitability isn't just about winning trades—it's about managing risk effectively. + +\subsubsection{Position Sizing} + +Proper position sizing ensures survival: + +\begin{equation} +Position Size = \frac{Risk Amount}{Stop Loss Distance} +\end{equation} + +Example: +\begin{itemize} + \item Account: \$10,000 + \item Risk per trade: 1\% = \$100 + \item Stop loss: 50 pips + \item Position size: \$100 / 50 pips = 2 pips per dollar +\end{itemize} + +\subsubsection{Stop Loss Placement} + +Stop losses protect capital: + +\begin{enumerate} + \item \textbf{Technical Stops}: Based on support/resistance levels + \item \textbf{Percentage Stops}: Fixed percentage of entry price + \item \textbf{ATR-Based Stops}: Based on Average True Range (volatility) + \item \textbf{Trailing Stops}: Move with price to protect profits +\end{enumerate} + +\subsubsection{Take Profit Targets} + +Profit targets lock in gains: + +\begin{itemize} + \item \textbf{Fixed Targets}: Based on risk-reward ratio (e.g., 2:1, 3:1) + \item \textbf{Technical Targets}: Based on support/resistance levels + \item \textbf{Partial Exits}: Scale out positions at multiple levels + \item \textbf{Trailing Stops}: Let winners run while protecting profits +\end{itemize} + +\subsection{Market Timing and Session Optimization} + +\subsubsection{Why Session-Based Trading Works} + +Different trading sessions exhibit distinct characteristics: + +\textbf{Asian Session (00:00-08:00 UTC):} +\begin{itemize} + \item Lower volatility + \item Range-bound price action + \item Ideal for mean reversion strategies + \item AUD/USD and JPY pairs most active +\end{itemize} + +\textbf{London Session (08:00-16:00 UTC):} +\begin{itemize} + \item High volatility + \item Strong trends + \item Ideal for breakout and trend-following strategies + \item EUR/USD, GBP/USD most active +\end{itemize} + +\textbf{New York Session (13:00-21:00 UTC):} +\begin{itemize} + \item High volatility + \item Overlaps with London (13:00-16:00) = highest volatility + \item Ideal for momentum strategies + \item USD pairs most active +\end{itemize} + +\subsubsection{Day-of-Week Patterns} + +Certain days exhibit predictable patterns: + +\begin{itemize} + \item \textbf{Monday}: Often gap-filling behavior + \item \textbf{Friday}: Profit-taking before weekend + \item \textbf{Midweek (Tue-Thu)}: Most reliable trends +\end{itemize} + +Many strategies restrict trading to Tuesday-Thursday for this reason. + +\subsection{Strategy Diversification} + +\subsubsection{Multi-Strategy Approach} + +Combining multiple strategies reduces risk: + +\textbf{Benefits:} +\begin{enumerate} + \item \textbf{Uncorrelated Returns}: Different strategies perform in different market conditions + \item \textbf{Risk Reduction}: Losses in one strategy offset by gains in another + \item \textbf{Consistent Performance}: Portfolio of strategies more stable than individual strategy + \item \textbf{Market Adaptation}: Some strategies work in trending markets, others in ranging markets +\end{enumerate} + +\textbf{Example: RSI Follow/Reverse/EMA Cross} +\begin{itemize} + \item RSI Follow: Works in trending markets + \item RSI Reverse: Works in ranging markets + \item EMA Cross: Works in breakout conditions + \item Combined: Adapts to various market conditions +\end{itemize} + +\subsection{Backtesting and Optimization} + +\subsubsection{Why Backtesting Matters} + +Backtesting validates strategies before live trading: + +\begin{enumerate} + \item \textbf{Historical Validation}: Tests strategy on past data + \item \textbf{Parameter Optimization}: Finds optimal parameter values + \item \textbf{Risk Assessment}: Identifies maximum drawdowns + \item \textbf{Performance Metrics}: Calculates win rate, profit factor, Sharpe ratio +\end{enumerate} + +\subsubsection{Key Performance Metrics} + +\textbf{Win Rate:} +\begin{equation} +Win Rate = \frac{Winning Trades}{Total Trades} \times 100\% +\end{equation} + +\textbf{Profit Factor:} +\begin{equation} +Profit Factor = \frac{Total Profit}{Total Loss} +\end{equation} +A profit factor > 1.0 indicates profitability. + +\textbf{Sharpe Ratio:} +\begin{equation} +Sharpe Ratio = \frac{Return - Risk Free Rate}{Standard Deviation of Returns} +\end{equation} +Higher Sharpe ratio indicates better risk-adjusted returns. + +\textbf{Maximum Drawdown:} +\begin{equation} +Max Drawdown = \frac{Peak Equity - Trough Equity}{Peak Equity} +\end{equation} +Lower drawdown indicates better capital preservation. + +\subsection{Common Pitfalls and How Strategies Avoid Them} + +\subsubsection{Over-Trading} + +\textbf{Problem:} Trading too frequently erodes profits through commissions and spreads. + +\textbf{Solutions in Our Strategies:} +\begin{itemize} + \item Cooldown periods after trades + \item Session-based restrictions + \item Multiple confirmation requirements + \item Maximum trades per event limits +\end{itemize} + +\subsubsection{Revenge Trading} + +\textbf{Problem:} Emotional trading after losses leads to poor decisions. + +\textbf{Solutions:} +\begin{itemize} + \item Automated execution (no emotions) + \item Cooldown periods after losses + \item Maximum drawdown protection + \item Strategy locking mechanisms +\end{itemize} + +\subsubsection{Inadequate Risk Management} + +\textbf{Problem:} Large losses wipe out multiple small wins. + +\textbf{Solutions:} +\begin{itemize} + \item Strict stop losses on every trade + \item Position sizing based on risk + \item Maximum drawdown limits + \item Trailing stops to protect profits +\end{itemize} + +\subsubsection{Market Regime Changes} + +\textbf{Problem:} Strategies that work in one market condition fail in others. + +\textbf{Solutions:} +\begin{itemize} + \item Multi-strategy approaches + \item Trend strength filters + \item Volatility-based position sizing + \item Market condition detection +\end{itemize} + +\subsection{Real-World Profitability Factors} + +\subsubsection{Execution Quality} + +\begin{itemize} + \item \textbf{Slippage}: Difference between expected and actual execution price + \item \textbf{Spread Costs}: Bid-ask spread erodes profits + \item \textbf{Latency}: Delays in execution can reduce profitability + \item \textbf{Order Fills}: IOC (Immediate or Cancel) vs FOK (Fill or Kill) strategies +\end{itemize} + +\subsubsection{Broker Selection} + +Important factors: +\begin{enumerate} + \item \textbf{Spreads}: Tighter spreads = higher profits + \item \textbf{Execution Speed}: Faster execution = better fills + \item \textbf{Reliability}: Uptime and connection stability + \item \textbf{Regulation}: Regulated brokers provide protection +\end{enumerate} + +\subsubsection{Market Conditions} + +Strategies perform differently in various conditions: + +\textbf{Trending Markets:} +\begin{itemize} + \item EMA-based strategies excel + \item Breakout strategies perform well + \item RSI follow strategies work +\end{itemize} + +\textbf{Ranging Markets:} +\begin{itemize} + \item RSI reversal strategies excel + \item Mean reversion approaches work + \item Range-bound trading profitable +\end{itemize} + +\textbf{Volatile Markets:} +\begin{itemize} + \item Larger stop losses required + \item Position sizing must be reduced + \item Trailing stops essential +\end{itemize} + +\subsection{Conclusion: The Path to Profitability} + +Successful algorithmic trading requires: + +\begin{enumerate} + \item \textbf{Sound Strategy}: Based on valid technical analysis principles + \item \textbf{Risk Management}: Strict stop losses and position sizing + \item \textbf{Market Timing}: Trading during optimal sessions and conditions + \item \textbf{Diversification}: Multiple strategies for different market conditions + \item \textbf{Discipline}: Following rules without emotion + \item \textbf{Continuous Improvement}: Backtesting, optimization, and adaptation +\end{enumerate} + +The strategies presented in this paper incorporate these principles, explaining their profitability. However, past performance does not guarantee future results, and proper risk management is essential for long-term success. diff --git a/paper/chapters/tradingview.tex b/paper/chapters/tradingview.tex new file mode 100644 index 0000000..db8ddfa --- /dev/null +++ b/paper/chapters/tradingview.tex @@ -0,0 +1,282 @@ +\section{TradingView Pine Script Strategies} + +TradingView's Pine Script provides a powerful platform for developing and backtesting trading strategies. This section examines a sophisticated multi-timeframe RSI strategy with EMA distance trading. + +\subsection{Pine Script Overview} + +Pine Script is TradingView's domain-specific language for creating custom indicators and strategies. Unlike MQL5, Pine Script is designed specifically for technical analysis and strategy development. + +\subsection{SSE Index RSI Bounce Strategy} + +This strategy is specifically designed for the Shanghai Stock Exchange (SSE) Index, combining multiple timeframe RSI analysis with EMA distance trading. + +\subsubsection{Strategy Architecture} + +The strategy implements three distinct entry mechanisms: + +\textbf{1. Weekly RSI Bounce:} +\begin{itemize} + \item Monitors weekly RSI for oversold conditions + \item Enters long when weekly RSI crosses above oversold level (27) + \item Position size: 14\% of equity + \item Targets major trend reversals +\end{itemize} + +\textbf{2. Daily RSI Bounce:} +\begin{itemize} + \item Monitors daily RSI for oversold conditions + \item Enters long when daily RSI crosses above oversold level (27) + \item Position size: 11\% of equity + \item Captures short-term momentum shifts +\end{itemize} + +\textbf{3. EMA Distance Entry:} +\begin{itemize} + \item Enters when price extends 16+ pips from 200 EMA + \item Requires price to remain above EMA + \item Position size: 53\% of equity + \item Exploits mean reversion opportunities +\end{itemize} + +\subsubsection{Core Implementation} + +\begin{lstlisting}[style=pinescriptstyle, caption=Pine Script Strategy Structure] +//@version=6 +strategy("SSE Index RSI Bounce Strategy", overlay=true, + default_qty_type=strategy.percent_of_equity, + initial_capital=10000, pyramiding=100) + +// Input parameters +rsi_length = input.int(17, "RSI Length", minval=1) +rsi_oversold = input.int(27, "RSI Oversold Level", minval=1, maxval=50) +rsi_overbought = input.int(86, "RSI Overbought Level", minval=50, maxval=100) +ema_length = input.int(177, "EMA Length", minval=1) + +// Calculate indicators +rsi_daily = ta.rsi(close, rsi_length) +rsi_weekly = request.security(syminfo.tickerid, "1W", ta.rsi(close, rsi_length)) +ema_200 = ta.ema(close, ema_length) +\end{lstlisting} + +\subsubsection{Multi-Timeframe Analysis} + +Pine Script's \texttt{request.security()} function enables seamless multi-timeframe analysis: + +\begin{lstlisting}[style=pinescriptstyle] +// Get weekly RSI +rsi_weekly = request.security(syminfo.tickerid, "1W", ta.rsi(close, rsi_length)) +rsi_weekly_prev = request.security(syminfo.tickerid, "1W", ta.rsi(close, rsi_length)[1]) + +// Weekly bounce detection +weekly_bounce = rsi_weekly_prev < rsi_oversold and rsi_weekly > rsi_oversold +\end{lstlisting} + +This allows the strategy to analyze weekly conditions while executing on any timeframe. + +\subsubsection{Entry Logic} + +\textbf{RSI Bounce Detection:} +\begin{lstlisting}[style=pinescriptstyle] +// Daily RSI bounce: RSI was below 30 and now crosses above 30 +daily_bounce = rsi_daily[1] < rsi_oversold and rsi_daily > rsi_oversold + +// Weekly RSI bounce: RSI was below 30 and now crosses above 30 +weekly_bounce = rsi_weekly_prev < rsi_oversold and rsi_weekly > rsi_oversold +\end{lstlisting} + +\textbf{EMA Distance Calculation:} +\begin{lstlisting}[style=pinescriptstyle] +// Calculate pip size (adjusts for instrument) +pip_size = syminfo.mintick * 10 +price_ema_distance = math.abs(close - ema_200) / pip_size + +// EMA distance entry condition +ema_distance_entry = price_ema_distance >= ema_distance_threshold * 100 + and close > ema_200 +\end{lstlisting} + +\subsubsection{Position Management} + +The strategy uses separate position tracking for each entry type: + +\begin{lstlisting}[style=pinescriptstyle] +// Track positions separately +var int weekly_trade_count = 0 +var int daily_trade_count = 0 +var int ema_distance_trade_count = 0 +var float weekly_position_qty = 0.0 +var float daily_position_qty = 0.0 +var float ema_distance_position_qty = 0.0 + +// Entry execution +if weekly_entry + strategy.entry("Weekly_Long", strategy.long, qty=weekly_position_size, + comment="Weekly RSI Bounce #" + str.tostring(weekly_trade_count + 1)) + weekly_trade_count := weekly_trade_count + 1 + weekly_position_qty := weekly_position_qty + weekly_position_size +\end{lstlisting} + +\subsubsection{Exit Logic} + +\textbf{Partial Exits:} +\begin{lstlisting}[style=pinescriptstyle] +// Partial exit for weekly positions on daily RSI overbought +if daily_rsi_overbought and weekly_position_qty > 0 + exit_qty = weekly_position_qty * (partial_exit_percent / 100) + strategy.close("Weekly_Long", qty=exit_qty, + comment="Weekly Partial Exit Daily OB") + weekly_position_qty := math.max(0, weekly_position_qty - exit_qty) +\end{lstlisting} + +\textbf{Complete Exits:} +\begin{lstlisting}[style=pinescriptstyle] +// Complete exit for weekly positions on weekly RSI overbought +if weekly_rsi_overbought and weekly_position_qty > 0 + strategy.close("Weekly_Long", comment="Complete Exit Weekly OB") + weekly_position_qty := 0.0 + weekly_trade_count := 0 +\end{lstlisting} + +\textbf{EMA Crossover Exit:} +\begin{lstlisting}[style=pinescriptstyle] +// Exit all positions when EMA crosses from above price to below price +ema_above_price_prev = ema_200[1] > close[1] +ema_below_price_now = ema_200 < close +ema_exit_condition = ema_above_price_prev and ema_below_price_now + +if ema_exit_condition and strategy.position_size > 0 + strategy.close_all("EMA Cross Exit") +\end{lstlisting} + +\subsubsection{Enhanced Version Features} + +The enhanced version adds sophisticated filtering mechanisms: + +\textbf{EMA Alignment Filter:} +\begin{lstlisting}[style=pinescriptstyle] +// Calculate fast and slow EMAs for alignment check +fast_ema = ta.ema(close, fast_ema_length) +slow_ema = ta.ema(close, slow_ema_length) +ema_alignment_distance = math.abs(fast_ema - slow_ema) / pip_size + +// EMA Alignment Filter Logic +ema_alignment_ok = false +if ema_alignment_direction == "both" + ema_alignment_ok := ema_alignment_distance >= ema_alignment_threshold +else if ema_alignment_direction == "above" + ema_alignment_ok := fast_ema > slow_ema and + ema_alignment_distance >= ema_alignment_threshold +\end{lstlisting} + +\textbf{Price Proximity Filter:} +\begin{lstlisting}[style=pinescriptstyle] +// Prevent trades when price is too close to 200 EMA +price_proximity_ok = price_ema_distance >= price_proximity_threshold * 100 + +// Enhanced EMA Distance Entry with filters +ema_distance_entry = price_ema_distance >= ema_distance_threshold * 100 + and close > ema_200 + and ema_alignment_ok + and price_proximity_ok +\end{lstlisting} + +\subsubsection{Visual Feedback} + +The strategy provides comprehensive visual feedback: + +\begin{lstlisting}[style=pinescriptstyle] +// Background colors for conditions +bgcolor(weekly_bounce ? color.new(color.green, 90) : na, + title="Weekly RSI Bounce") +bgcolor(daily_bounce ? color.new(color.blue, 90) : na, + title="Daily RSI Bounce") +bgcolor(ema_distance_entry ? color.new(color.purple, 90) : na, + title="EMA Distance Entry") + +// Plot entry and exit signals +plotshape(weekly_entry, "Weekly Entry", shape.triangleup, + location.belowbar, color.green, size=size.normal) +plotshape(daily_entry, "Daily Entry", shape.triangleup, + location.belowbar, color.blue, size=size.small) +plotshape(ema_distance_entry, "EMA Distance Entry", shape.triangleup, + location.belowbar, color.purple, size=size.normal) +\end{lstlisting} + +\subsubsection{Information Table} + +Real-time status display: + +\begin{lstlisting}[style=pinescriptstyle] +var table info_table = table.new(position.top_right, 2, 16, + bgcolor=color.white, border_width=1) +if barstate.islast + table.cell(info_table, 0, 0, "Indicator", bgcolor=color.gray) + table.cell(info_table, 1, 0, "Value", bgcolor=color.gray) + table.cell(info_table, 0, 1, "Daily RSI", bgcolor=color.white) + table.cell(info_table, 1, 1, str.tostring(rsi_daily, "#.##"), + bgcolor=color.white) + // ... additional cells +\end{lstlisting} + +\subsection{Key Differences: Pine Script vs MQL5} + +\begin{table}[H] +\centering +\caption{Pine Script vs MQL5 Comparison} +\label{tab:pinescript_vs_mql5} +\begin{tabular}{lll} +\toprule +\textbf{Feature} & \textbf{Pine Script} & \textbf{MQL5} \\ +\midrule +Platform & TradingView (Cloud) & MetaTrader 5 (Desktop) \\ +Execution & Backtesting/Paper Trading & Live Trading \\ +Multi-Timeframe & \texttt{request.security()} & Manual timeframe switching \\ +Position Management & Built-in strategy functions & Manual CTrade class \\ +Visualization & Built-in plotting & Manual object creation \\ +Real-time Data & Cloud-based & Broker connection required \\ +\bottomrule +\end{tabular} +\end{table} + +\subsection{Strategy Rationale} + +\textbf{Why This Strategy Works:} + +\begin{enumerate} + \item \textbf{Multi-Timeframe Confirmation}: Weekly signals provide major trend direction, daily signals capture short-term opportunities + \item \textbf{RSI Mean Reversion}: Oversold bounces in equity markets often lead to profitable reversals + \item \textbf{EMA Distance Trading}: Extreme price deviations from EMA tend to revert, creating profit opportunities + \item \textbf{Partial Profit Taking}: Scaling out positions at overbought levels locks in profits while allowing for continued upside + \item \textbf{EMA Exit Protection}: Crossover exits protect capital during major trend reversals +\end{enumerate} + +\subsection{Market-Specific Optimization} + +The strategy is optimized for Chinese equity markets: + +\begin{itemize} + \item \textbf{Volatility Characteristics}: Chinese markets exhibit high volatility, making RSI bounces more frequent + \item \textbf{Emotion-Driven Moves}: Retail-driven markets create more extreme RSI readings + \item \textbf{Session Patterns}: Trading during specific hours captures optimal market conditions + \item \textbf{Position Sizing}: Larger position sizes (up to 53\%) capitalize on high-probability setups +\end{itemize} + +\subsection{Performance Considerations} + +\textbf{Advantages of Pine Script:} +\begin{itemize} + \item Easy backtesting with historical data + \item Cloud-based execution (no local resources required) + \item Built-in visualization and debugging tools + \item Community sharing and strategy marketplace +\end{itemize} + +\textbf{Limitations:} +\begin{itemize} + \item Limited to TradingView platform + \item No direct broker integration (requires manual execution or TradingView broker) + \item Less control over execution details compared to MQL5 + \item Cloud dependency (requires internet connection) +\end{itemize} + +The TradingView strategy demonstrates how modern cloud-based platforms enable sophisticated multi-timeframe strategies with comprehensive risk management and visual feedback, complementing the MQL5 implementations for different trading needs and preferences. diff --git a/paper/data/game_theory_full_results.csv b/paper/data/game_theory_full_results.csv new file mode 100644 index 0000000..2986f89 --- /dev/null +++ b/paper/data/game_theory_full_results.csv @@ -0,0 +1,10001 @@ 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+\usepackage{subcaption} +\usepackage{booktabs} +\usepackage{multirow} + +% Page setup +\geometry{margin=1in} +\pagestyle{fancy} +\fancyhf{} +\fancyhead[L]{\leftmark} +\fancyhead[R]{\thepage} +\fancyfoot[C]{Algorithmic Trading Strategies: MQL5 and TradingView Implementation} + +% Code listing setup for MQL5 +\lstdefinestyle{mql5style}{ + language=C++, + basicstyle=\ttfamily\small, + keywordstyle=\color{blue}\bfseries, + commentstyle=\color{green!60!black}, + stringstyle=\color{red}, + numberstyle=\tiny\color{gray}, + numbers=left, + numbersep=5pt, + frame=single, + breaklines=true, + breakatwhitespace=false, + showspaces=false, + showstringspaces=false, + tabsize=2, + captionpos=b +} + +% Code listing setup for Pine Script +\lstdefinestyle{pinescriptstyle}{ + language=Python, + basicstyle=\ttfamily\small, + keywordstyle=\color{blue}\bfseries, + commentstyle=\color{green!60!black}, + stringstyle=\color{red}, + numberstyle=\tiny\color{gray}, + numbers=left, + numbersep=5pt, + frame=single, + breaklines=true, + breakatwhitespace=false, + showspaces=false, + showstringspaces=false, + tabsize=2, + captionpos=b +} + +% Title information +\title{Algorithmic Trading Strategies:\\ +A Comprehensive Analysis of MQL5 Expert Advisors\\ +and TradingView Pine Script Implementations} +\author{Algorithmic Trading Research} +\date{\today} + +\begin{document} + +\maketitle + +\begin{abstract} +This paper presents a comprehensive analysis of profitable algorithmic trading strategies implemented in both MetaTrader 5 (MQL5) and TradingView Pine Script. We examine multiple Expert Advisors (EAs) utilizing various technical indicators including RSI (Relative Strength Index), EMA (Exponential Moving Average), and Darvas Box theory. The strategies are optimized for different financial instruments including forex pairs (AUD/USD, EUR/USD), precious metals (XAU/USD, XAG/USD), cryptocurrencies (BTC/USD), and equity indices. Through detailed code analysis and strategy rationale, we demonstrate how systematic approaches to technical analysis, risk management, and market timing contribute to profitable trading outcomes. The paper covers fundamental MQL5 programming concepts, strategy implementation details, and the theoretical foundations that make these algorithms profitable in various market conditions. +\end{abstract} + +\tableofcontents +\newpage + +% Include chapters +\input{chapters/introduction} +\input{chapters/mql5_basics} +\input{chapters/algorithms} +\input{chapters/tradingview} +\input{chapters/advanced_techniques} +\input{chapters/profitability} +\input{chapters/conclusion} + +% Bibliography +\begin{thebibliography}{99} +\bibitem{darvas1957} +Darvas, N. (1957). \textit{How I Made \$2,000,000 in the Stock Market}. Lyle Stuart. + +\bibitem{wilder1978} +Wilder, J. W. (1978). \textit{New Concepts in Technical Trading Systems}. Trend Research. + +\bibitem{mql5docs} +MetaQuotes Software Corp. (2024). \textit{MQL5 Documentation}. \url{https://www.mql5.com/en/docs} + +\bibitem{tradingviewdocs} +TradingView Inc. (2024). \textit{Pine Script Language Reference Manual}. \url{https://www.tradingview.com/pine-script-docs/} + +\bibitem{vantharp1998} +Van Tharp, K. (1998). \textit{Trade Your Way to Financial Freedom}. McGraw-Hill. + +\bibitem{connors2012} +Connors, L. A., \& Alvarez, C. (2012). \textit{High Probability ETF Trading}. TradingMarkets Publishing. + +\end{thebibliography} + +\end{document} diff --git a/paper/optimal_config.json b/paper/optimal_config.json new file mode 100644 index 0000000..ef0dc65 --- /dev/null +++ b/paper/optimal_config.json @@ -0,0 +1,8 @@ +{ + "order_book_liquidity": 27.337507902560162, + "big_sentiment_threshold": 0.31249746155318936, + "big_volume_threshold": 99.1788610573351, + "big_trade_size_pct": 0.1435380702026215, + "fundamental_reversion": 0.007385008311278914, + "num_big_players": 9 +} \ No newline at end of file diff --git a/paper/simulations/README.md b/paper/simulations/README.md new file mode 100644 index 0000000..5595ca5 --- /dev/null +++ b/paper/simulations/README.md @@ -0,0 +1,117 @@ +# Trading Strategy Simulations + +This directory contains Python scripts for simulating and analyzing advanced trading techniques. + +## Scripts + +### 1. martingale_simulation.py +Analyzes the statistical properties and risk of martingale strategies. + +**Key Analyses:** +- Ruin probability calculations +- Position size growth +- Required capital analysis +- Monte Carlo simulations + +**Usage:** +```bash +python martingale_simulation.py +``` + +**Output:** +- `martingale_analysis.png`: Comprehensive analysis plots +- Console output with statistics + +### 2. trailing_stop_analysis.py +Compares fixed stop loss vs trailing stop loss performance. + +**Key Analyses:** +- Return distribution comparison +- Sharpe ratio improvement +- Exit timing analysis +- Sample price path visualization + +**Usage:** +```bash +python trailing_stop_analysis.py +``` + +**Output:** +- `trailing_stop_analysis.png`: Comparison plots +- Console output with performance metrics + +### 3. partial_exit_analysis.py +Analyzes the statistical benefits of partial exits. + +**Key Analyses:** +- Variance reduction calculation +- Sharpe ratio optimization +- Optimal exit percentage +- Return distribution comparison + +**Usage:** +```bash +python partial_exit_analysis.py +``` + +**Output:** +- `partial_exit_analysis.png`: Analysis plots +- Console output with optimization results + +### 4. grid_trading_analysis.py +Analyzes grid trading performance in different market conditions. + +**Key Analyses:** +- Mean-reverting vs trending market performance +- Optimal grid spacing +- Trade frequency analysis +- Profit distribution + +**Usage:** +```bash +python grid_trading_analysis.py +``` + +**Output:** +- `grid_trading_analysis.png`: Market condition comparison +- Console output with performance metrics + +## Installation + +```bash +pip install -r requirements.txt +``` + +## Running All Simulations + +```bash +# Run all simulations +python martingale_simulation.py +python trailing_stop_analysis.py +python partial_exit_analysis.py +python grid_trading_analysis.py +``` + +## Output Location + +All figures are saved to `../figures/` directory: +- `martingale_analysis.png` +- `trailing_stop_analysis.png` +- `partial_exit_analysis.png` +- `grid_trading_analysis.png` + +## Mathematical Foundations + +These simulations implement: +- Geometric Brownian Motion for price simulation +- Ornstein-Uhlenbeck process for mean-reverting prices +- Monte Carlo methods for statistical analysis +- Kelly Criterion for position sizing +- Sharpe ratio and other risk-adjusted metrics + +## Notes + +- Simulations use random number generation - results may vary slightly between runs +- For reproducible results, set random seeds in scripts +- Adjust parameters in each script to match your trading conditions +- Results are illustrative - actual trading results will vary diff --git a/paper/simulations/__pycache__/game_theory_trading.cpython-312.pyc b/paper/simulations/__pycache__/game_theory_trading.cpython-312.pyc new file mode 100644 index 0000000..5a61f39 Binary files /dev/null and b/paper/simulations/__pycache__/game_theory_trading.cpython-312.pyc differ diff --git a/paper/simulations/game_theory_trading.py b/paper/simulations/game_theory_trading.py new file mode 100644 index 0000000..563e7f6 --- /dev/null +++ b/paper/simulations/game_theory_trading.py @@ -0,0 +1,1757 @@ +""" +Game Theory Analysis: Retail Traders vs Big Players +Models the strategic interaction between retail traders (driven by FOMO/group psychology) +and institutional players in financial markets. + +Key Features: +- Retail traders exhibit FOMO behavior (herding, momentum following) +- Big players act strategically to exploit retail behavior +- Finite repeated game (not infinite, as big players are human) +- Nash equilibrium analysis +- Order book model: realistic price impact based on order book depth +- Big players have more capital and their trades move markets through order book consumption +""" +import numpy as np +import matplotlib +matplotlib.use('Agg') # Use non-interactive backend +import matplotlib.pyplot as plt +import pandas as pd +from scipy.optimize import minimize, differential_evolution +from scipy.stats import norm +import itertools +from collections import defaultdict + +class RetailTrader: + """Models retail trader behavior with FOMO and group psychology""" + + def __init__(self, base_risk_aversion=0.5, fomo_sensitivity=0.3, + herd_tendency=0.4, memory_decay=0.9): + self.base_risk_aversion = base_risk_aversion + self.fomo_sensitivity = fomo_sensitivity + self.herd_tendency = herd_tendency + self.memory_decay = memory_decay + self.price_memory = [] + self.sentiment = 0.0 # -1 (bearish) to +1 (bullish) + + def update_sentiment(self, price_change, market_momentum, retail_activity): + """Update sentiment based on FOMO and herding""" + # FOMO component: stronger reaction to positive moves + fomo_component = self.fomo_sensitivity * np.tanh(price_change * 10) + + # Herding component: follow the crowd + herd_component = self.herd_tendency * np.tanh(retail_activity * 5) + + # Momentum component + momentum_component = 0.2 * np.tanh(market_momentum * 3) + + # Update sentiment with memory decay + new_sentiment = fomo_component + herd_component + momentum_component + self.sentiment = self.memory_decay * self.sentiment + (1 - self.memory_decay) * new_sentiment + self.sentiment = np.clip(self.sentiment, -1, 1) + + return self.sentiment + + def decide_action(self, current_price, expected_return, volatility): + """Decide trading action based on sentiment and risk""" + # Risk-adjusted expected utility + risk_adjusted_return = expected_return - self.base_risk_aversion * volatility**2 + + # Sentiment bias + sentiment_bias = self.sentiment * (1 - self.base_risk_aversion) + + # Decision threshold + decision_score = risk_adjusted_return + sentiment_bias + + if decision_score > 0.02: + return 'buy', min(abs(decision_score) * 10, 1.0) # position size + elif decision_score < -0.02: + return 'sell', min(abs(decision_score) * 10, 1.0) + else: + return 'hold', 0.0 + + +class OrderBook: + """Models order book depth and price impact""" + + def __init__(self, base_liquidity=1000, depth_levels=10, + liquidity_decay=0.95, liquidity_replenish=0.02): + """ + base_liquidity: Base liquidity at each price level + depth_levels: Number of price levels in order book + liquidity_decay: How much liquidity is consumed (0-1) + liquidity_replenish: Rate at which liquidity replenishes per round + """ + self.base_liquidity = base_liquidity + self.depth_levels = depth_levels + self.liquidity_decay = liquidity_decay + self.liquidity_replenish = liquidity_replenish + self.bid_depth = np.ones(depth_levels) * base_liquidity # Liquidity at each level below price + self.ask_depth = np.ones(depth_levels) * base_liquidity # Liquidity at each level above price + + def calculate_price_impact(self, volume, direction, current_price): + """ + Calculate price impact based on order book consumption + direction: +1 for buy, -1 for sell + Returns: price impact as fraction (e.g., 0.01 = 1% move) + """ + if volume == 0: + return 0.0 + + remaining_volume = abs(volume) + total_impact = 0.0 + price_level = current_price + + # Determine which side of book to consume + if direction > 0: # Buying - consume ask side (above current price) + depth_array = self.ask_depth.copy() + else: # Selling - consume bid side (below current price) + depth_array = self.bid_depth.copy() + + # Consume order book levels + level_spread = 0.002 # 0.2% price increment per level (increased impact) + for level in range(self.depth_levels): + if remaining_volume <= 0: + break + + available_liquidity = depth_array[level] + consumed = min(remaining_volume, available_liquidity) + + # Price impact increases as we go deeper into the book + # Impact = (level + 1) * spread * (consumed / available_liquidity) + # More impact when consuming larger portion of available liquidity + consumption_ratio = consumed / max(available_liquidity, 1) + level_impact = (level + 1) * level_spread * consumption_ratio + total_impact += level_impact + + # Consume liquidity + depth_array[level] -= consumed + remaining_volume -= consumed + + # If volume exceeds all available depth, add extra impact + if remaining_volume > 0: + # Large impact for exceeding available liquidity + excess_impact = 0.005 * (remaining_volume / self.base_liquidity) + total_impact += excess_impact + + # Update order book + if direction > 0: + self.ask_depth = depth_array + else: + self.bid_depth = depth_array + + return total_impact * np.sign(direction) + + def replenish_liquidity(self): + """Replenish order book liquidity over time""" + # Replenish both sides + self.bid_depth = np.minimum( + self.bid_depth + self.base_liquidity * self.liquidity_replenish, + self.base_liquidity + ) + self.ask_depth = np.minimum( + self.ask_depth + self.base_liquidity * self.liquidity_replenish, + self.base_liquidity + ) + + def get_total_liquidity(self): + """Get total available liquidity""" + return np.sum(self.bid_depth) + np.sum(self.ask_depth) + + +class BigPlayer: + """Models institutional/big player strategic behavior""" + + def __init__(self, capital=1000000, market_impact_coef=0.001, + patience=0.7, exploit_retail=True, + sentiment_threshold=0.6, volume_threshold=20, trade_size_pct=0.20): + self.capital = capital + self.market_impact_coef = market_impact_coef + self.patience = patience # How long to wait before acting + self.exploit_retail = exploit_retail + self.position = 0.0 + self.retail_sentiment_history = [] + # Configurable thresholds + self.sentiment_threshold = sentiment_threshold + self.volume_threshold = volume_threshold + self.trade_size_pct = trade_size_pct + + def observe_retail_behavior(self, retail_sentiment, retail_volume): + """Observe and learn from retail behavior""" + self.retail_sentiment_history.append(retail_sentiment) + if len(self.retail_sentiment_history) > 20: + self.retail_sentiment_history.pop(0) + + def strategic_action(self, current_price, retail_sentiment, retail_volume, + fundamental_value, game_round, order_book=None): + """ + Strategic action based on retail behavior and fundamentals + Returns: (action, size) where size is in actual units (not normalized) + """ + if not self.exploit_retail: + # Simple fundamental trading + if current_price < fundamental_value * 0.98: + # Buy: use 5% of capital + size = min(100, self.capital * 0.05 / current_price) + return 'buy', size + elif current_price > fundamental_value * 1.02: + # Sell: close position + size = min(100, abs(self.position)) + return 'sell', size + else: + return 'hold', 0.0 + + # Exploit retail FOMO + avg_retail_sentiment = np.mean(self.retail_sentiment_history) if self.retail_sentiment_history else 0 + + # Strategy: fade extreme retail sentiment + # Use configurable thresholds + if avg_retail_sentiment > self.sentiment_threshold and retail_volume > self.volume_threshold: + # Retail is bullish - sell to them (large size) + size = min(300, self.capital * self.trade_size_pct / current_price) + return 'sell', size + elif avg_retail_sentiment < -self.sentiment_threshold and retail_volume > self.volume_threshold: + # Retail is bearish - buy from them (large size) + size = min(300, self.capital * self.trade_size_pct / current_price) + return 'buy', size + + # Trade on fundamentals more frequently (less patience) + if game_round % max(1, int(3 / (1 - self.patience))) == 0: # Trade every 3-10 rounds + if current_price < fundamental_value * 0.98: # More sensitive + # Medium size fundamental trade + size = min(100, self.capital * 0.05 / current_price) + return 'buy', size + elif current_price > fundamental_value * 1.02: # More sensitive + # Medium size fundamental trade + size = min(100, abs(self.position)) + return 'sell', size + + return 'hold', 0.0 + + +class TradingGame: + """Simulates the repeated game between retail traders and big players""" + + def __init__(self, num_retail_traders=100, num_big_players=5, + initial_price=100, fundamental_value=100, volatility=0.02, + order_book_liquidity=50, fundamental_reversion=0.01, + big_sentiment_threshold=0.6, big_volume_threshold=20, + big_trade_size_pct=0.20): + self.num_retail_traders = num_retail_traders + self.num_big_players = num_big_players + self.initial_price = initial_price + self.fundamental_value = fundamental_value + self.volatility = volatility + self.fundamental_reversion = fundamental_reversion + + # Initialize order book with realistic liquidity + # Lower liquidity = more price impact from trades + # 50 units per level means 500 total units (10 levels) + # Retail can trade 0-200 units total, so this creates meaningful impact + self.order_book = OrderBook( + base_liquidity=order_book_liquidity, + depth_levels=10, + liquidity_decay=0.95, + liquidity_replenish=0.05 # Faster replenishment + ) + + # Create independent random state for this game + # Use a unique seed based on time, object id, and random component + import time + import os + base_seed = (int(time.time() * 1000000) % (2**31) + + id(self) % 1000000 + + os.getpid() * 1000) % (2**31) + # Add some randomness from global RNG to ensure uniqueness + try: + base_seed = (base_seed + np.random.randint(0, 1000000)) % (2**31) + except: + pass + self.rng = np.random.RandomState(base_seed) + + # Initialize players with game-specific random state + self.retail_traders = [RetailTrader( + base_risk_aversion=self.rng.uniform(0.3, 0.7), + fomo_sensitivity=self.rng.uniform(0.2, 0.5), + herd_tendency=self.rng.uniform(0.3, 0.6) + ) for _ in range(num_retail_traders)] + + self.big_players = [BigPlayer( + capital=self.rng.uniform(500000, 2000000), + exploit_retail=True, + sentiment_threshold=big_sentiment_threshold, + volume_threshold=big_volume_threshold, + trade_size_pct=big_trade_size_pct + ) for _ in range(num_big_players)] + + self.price_history = [initial_price] + self.retail_sentiment_history = [] + self.retail_volume_history = [] + self.big_player_volume_history = [] + self.retail_pnl_history = [] + self.big_player_pnl_history = [] + + # Track cumulative positions for proper PnL calculation + self.retail_position = 0.0 # Cumulative position (positive = long, negative = short) + self.big_player_position = 0.0 + + def update_price(self, retail_net_volume, big_player_net_volume, + fundamental_shock=0): + """ + Update price based on trading through order book and fundamentals + Volumes are in actual units (not normalized) + """ + current_price = self.price_history[-1] + + # Calculate price impact through order book + # Retail traders aggregate volume can be significant (100 traders * 0-2 units = 0-200 units) + retail_impact = 0.0 + if retail_net_volume != 0: + retail_direction = np.sign(retail_net_volume) + retail_impact = self.order_book.calculate_price_impact( + abs(retail_net_volume), retail_direction, current_price + ) + # Order book naturally gives less impact per unit for smaller trades + # But when retail herds together (FOMO), aggregate volume creates impact + + # Big players have much larger trades - more impact + big_impact = 0.0 + if big_player_net_volume != 0: + big_direction = np.sign(big_player_net_volume) + # Big players' trades consume more order book depth + big_impact = self.order_book.calculate_price_impact( + abs(big_player_net_volume), big_direction, current_price + ) + # Big players' trades have full impact (they move markets) + big_impact *= 1.0 + + # Replenish order book liquidity + self.order_book.replenish_liquidity() + + # Fundamental mean reversion (much stronger to prevent price explosion) + # Pull price back toward fundamental value + price_deviation = (current_price - self.fundamental_value) / self.fundamental_value + fundamental_drift = -self.fundamental_reversion * price_deviation + + # Random shock using game's independent random state + random_shock = self.rng.normal(0, self.volatility) + + # Price update (multiplicative but bounded) + total_change = retail_impact + big_impact + fundamental_drift + random_shock + fundamental_shock + total_change = np.clip(total_change, -0.1, 0.1) # Cap at 10% per round + new_price = current_price * (1 + total_change) + + return max(new_price, 0.01) # Prevent negative prices + + def play_round(self, game_round, fundamental_shock=0): + """Play one round of the game""" + current_price = self.price_history[-1] + + # Calculate market momentum + if len(self.price_history) > 1: + momentum = (self.price_history[-1] - self.price_history[-2]) / self.price_history[-2] + else: + momentum = 0 + + # Retail traders decide + retail_actions = [] + retail_sentiments = [] + retail_net_volume = 0 + + for trader in self.retail_traders: + expected_return = momentum # Simple expectation + sentiment = trader.update_sentiment(momentum, momentum, + len([a for a in retail_actions if a[0] != 'hold']) / max(len(retail_actions), 1)) + action, normalized_size = trader.decide_action(current_price, expected_return, self.volatility) + + # Convert normalized size to actual units (retail traders trade small) + # Normalized size is 0-1, convert to 0.1-2 units per trader + actual_size = normalized_size * 2.0 # Retail traders trade 0-2 units each + retail_actions.append((action, actual_size)) + retail_sentiments.append(sentiment) + + if action == 'buy': + retail_net_volume += actual_size + elif action == 'sell': + retail_net_volume -= actual_size + + avg_retail_sentiment = np.mean(retail_sentiments) + retail_volume = sum([size for _, size in retail_actions]) + + # Big players observe and act + big_player_net_volume = 0 + for big_player in self.big_players: + big_player.observe_retail_behavior(avg_retail_sentiment, retail_volume) + action, size = big_player.strategic_action( + current_price, avg_retail_sentiment, retail_volume, + self.fundamental_value, game_round, order_book=self.order_book + ) + + # Big players trade in actual units (already calculated based on capital) + if action == 'buy': + big_player_net_volume += size + elif action == 'sell': + big_player_net_volume -= size + + # Update price + new_price = self.update_price(retail_net_volume, big_player_net_volume, + fundamental_shock) + + # Calculate PnL properly + # PnL on existing position: position_at_start * price_change + # PnL on new trades: new_trades * price_change (they entered at current_price, price moved to new_price) + # Total PnL = (position_at_start + new_trades) * price_change = position_at_end * price_change + + price_change_amount = new_price - current_price + + # Calculate PnL on average position during the round + # This accounts for both existing positions and new trades + position_at_start = self.retail_position + position_at_end = self.retail_position + retail_net_volume + average_position = (position_at_start + position_at_end) / 2 + + # Retail PnL: average position * price change + retail_pnl = average_position * price_change_amount + + # Update retail position for next round + self.retail_position = position_at_end + + # Big player PnL: same calculation + position_at_start_big = self.big_player_position + position_at_end_big = self.big_player_position + big_player_net_volume + average_position_big = (position_at_start_big + position_at_end_big) / 2 + + big_player_pnl = average_position_big * price_change_amount + + # Update big player position for next round + self.big_player_position = position_at_end_big + + # Update history + self.price_history.append(new_price) + self.retail_sentiment_history.append(avg_retail_sentiment) + self.retail_volume_history.append(retail_volume) + self.big_player_volume_history.append(abs(big_player_net_volume)) + self.retail_pnl_history.append(retail_pnl) + self.big_player_pnl_history.append(big_player_pnl) + + # Calculate exploitation metric: big players trading opposite to retail sentiment + # Positive sentiment (bullish retail) -> big players should sell (negative volume) + # Negative sentiment (bearish retail) -> big players should buy (positive volume) + exploitation_signal = -np.sign(avg_retail_sentiment) * np.sign(big_player_net_volume) if big_player_net_volume != 0 else 0 + + return { + 'price': new_price, + 'retail_sentiment': avg_retail_sentiment, + 'retail_volume': retail_volume, + 'big_player_volume': abs(big_player_net_volume), + 'big_player_direction': np.sign(big_player_net_volume), + 'retail_pnl': retail_pnl, + 'big_player_pnl': big_player_pnl, + 'exploitation_signal': exploitation_signal + } + + def simulate_game(self, num_rounds=100, fundamental_shocks=None): + """Simulate the full game""" + if fundamental_shocks is None: + fundamental_shocks = [0] * num_rounds + + results = [] + for round_num in range(num_rounds): + shock = fundamental_shocks[round_num] if round_num < len(fundamental_shocks) else 0 + result = self.play_round(round_num, shock) + result['round'] = round_num + results.append(result) + + return pd.DataFrame(results) + + def calculate_nash_equilibrium(self): + """Calculate approximate Nash equilibrium""" + # Simplified Nash equilibrium calculation + # In equilibrium, big players should not be able to improve by changing strategy + # given retail behavior, and vice versa + + # Average retail sentiment in equilibrium + if len(self.retail_sentiment_history) > 10: + eq_retail_sentiment = np.mean(self.retail_sentiment_history[-10:]) + else: + eq_retail_sentiment = 0 + + # Equilibrium price should be close to fundamental when both sides are balanced + if len(self.price_history) > 10: + eq_price = np.mean(self.price_history[-10:]) + else: + eq_price = self.initial_price + + return { + 'equilibrium_price': eq_price, + 'equilibrium_retail_sentiment': eq_retail_sentiment, + 'price_deviation': abs(eq_price - self.fundamental_value) / self.fundamental_value + } + + +def analyze_game_theory(num_simulations=50, num_rounds=100): + """Run multiple game simulations and analyze results""" + all_results = [] + nash_equilibria = [] + + for sim in range(num_simulations): + # Each game gets completely independent random state + # Use simulation number + time + random component for unique seed + import time + base_seed = int(time.time() * 1000000) % (2**31) + game_seed = (base_seed + sim * 7919 + np.random.randint(0, 1000000)) % (2**31) + game_rng = np.random.RandomState(game_seed) + + # Vary initial conditions for more diversity + initial_price = 100 + game_rng.normal(0, 2) # Small variation in starting price + fundamental_value = 100 + game_rng.normal(0, 1) # Small variation in fundamental + + game = TradingGame( + num_retail_traders=100, + num_big_players=5, + initial_price=initial_price, + fundamental_value=fundamental_value, + volatility=0.02 + ) + + # Override game's RNG with our independent one for shocks + game.rng = game_rng + + # Add random fundamental shocks - unique per game + fundamental_shocks = game_rng.normal(0, 0.01, num_rounds) + # Add random large shocks at random times (not fixed rounds) + num_large_shocks = game_rng.randint(1, 4) # 1-3 large shocks per game + shock_times = game_rng.choice(num_rounds, num_large_shocks, replace=False) + shock_sizes = game_rng.normal(0, 0.03, num_large_shocks) # Random shock sizes + for time, size in zip(shock_times, shock_sizes): + fundamental_shocks[time] = size + + results = game.simulate_game(num_rounds, fundamental_shocks) + results['simulation'] = sim + + # Calculate Nash equilibrium + nash = game.calculate_nash_equilibrium() + nash['simulation'] = sim + nash_equilibria.append(nash) + + all_results.append(results) + + combined_results = pd.concat(all_results, ignore_index=True) + nash_df = pd.DataFrame(nash_equilibria) + + return combined_results, nash_df + + +def calculate_meta_analysis(results_df, nash_df): + """Calculate comprehensive meta-analysis across all games""" + meta_stats = {} + + # Aggregate PnL statistics + final_retail_pnl = results_df.groupby('simulation')['retail_pnl'].sum() + final_big_pnl = results_df.groupby('simulation')['big_player_pnl'].sum() + + meta_stats['retail_pnl'] = { + 'mean': final_retail_pnl.mean(), + 'median': final_retail_pnl.median(), + 'std': final_retail_pnl.std(), + 'min': final_retail_pnl.min(), + 'max': final_retail_pnl.max(), + 'win_rate': (final_retail_pnl > 0).mean(), + 'sharpe': final_retail_pnl.mean() / final_retail_pnl.std() if final_retail_pnl.std() > 0 else 0 + } + + meta_stats['big_player_pnl'] = { + 'mean': final_big_pnl.mean(), + 'median': final_big_pnl.median(), + 'std': final_big_pnl.std(), + 'min': final_big_pnl.min(), + 'max': final_big_pnl.max(), + 'win_rate': (final_big_pnl > 0).mean(), + 'sharpe': final_big_pnl.mean() / final_big_pnl.std() if final_big_pnl.std() > 0 else 0 + } + + # Price efficiency + price_efficiency = [] + for sim in results_df['simulation'].unique(): + sim_data = results_df[results_df['simulation'] == sim] + price_deviations = np.abs(sim_data['price'].values - 100) / 100 + efficiency = 1 - price_deviations.mean() + price_efficiency.append(max(0, efficiency)) + + meta_stats['market_efficiency'] = { + 'mean': np.mean(price_efficiency), + 'median': np.median(price_efficiency), + 'std': np.std(price_efficiency), + 'min': np.min(price_efficiency), + 'max': np.max(price_efficiency) + } + + # Nash equilibrium statistics + meta_stats['nash_equilibrium'] = { + 'mean_price': nash_df['equilibrium_price'].mean(), + 'std_price': nash_df['equilibrium_price'].std(), + 'mean_deviation': nash_df['price_deviation'].mean(), + 'mean_sentiment': nash_df['equilibrium_retail_sentiment'].mean() + } + + # FOMO and herding metrics + sentiment_volatility = results_df.groupby('simulation')['retail_sentiment'].std() + volume_volatility = results_df.groupby('simulation')['retail_volume'].std() + sentiment_volume_corr = results_df.groupby('simulation').apply( + lambda x: x['retail_sentiment'].corr(x['retail_volume']) + ) + + meta_stats['fomo_herding'] = { + 'avg_sentiment_volatility': sentiment_volatility.mean(), + 'avg_volume_volatility': volume_volatility.mean(), + 'sentiment_volume_correlation': sentiment_volume_corr.mean(), + 'sentiment_volume_correlation_std': sentiment_volume_corr.std() + } + + # Exploitation metrics + exploitation_scores = [] + for sim in results_df['simulation'].unique(): + sim_data = results_df[results_df['simulation'] == sim] + if len(sim_data) < 10: + continue + retail_sentiment = sim_data['retail_sentiment'].values + big_volume = sim_data['big_player_volume'].values + abs_sentiment = np.abs(retail_sentiment) + if len(abs_sentiment) > 5 and big_volume.std() > 0 and abs_sentiment.std() > 0: + try: + corr = np.corrcoef(abs_sentiment, big_volume)[0, 1] + if not np.isnan(corr) and not np.isinf(corr): + exploitation_scores.append(corr) + except: + pass + + if len(exploitation_scores) > 0: + meta_stats['exploitation'] = { + 'mean': np.mean(exploitation_scores), + 'median': np.median(exploitation_scores), + 'std': np.std(exploitation_scores) + } + else: + meta_stats['exploitation'] = { + 'mean': 0, + 'median': 0, + 'std': 0 + } + + return meta_stats + + +def plot_game_analysis(num_simulations=100, show_random_games=10): + """Plot comprehensive game theory analysis with meta-analysis""" + results_df, nash_df = analyze_game_theory(num_simulations, num_rounds=100) + + # Calculate meta-analysis + meta_stats = calculate_meta_analysis(results_df, nash_df) + + fig = plt.figure(figsize=(20, 16)) + gs = fig.add_gridspec(4, 3, hspace=0.4, wspace=0.3) + + # Plot 1: Random 10 Games Evolution (Price) + ax1 = fig.add_subplot(gs[0, 0]) + random_sims = np.random.choice(results_df['simulation'].unique(), + min(show_random_games, len(results_df['simulation'].unique())), + replace=False) + colors = plt.cm.tab10(np.linspace(0, 1, len(random_sims))) + for idx, sim in enumerate(random_sims): + sim_data = results_df[results_df['simulation'] == sim] + ax1.plot(sim_data['round'], sim_data['price'], + color=colors[idx], alpha=0.6, linewidth=1.5, label=f'Game {sim}') + ax1.axhline(100, color='r', linestyle='--', linewidth=2, label='Fundamental Value') + ax1.set_xlabel('Game Round') + ax1.set_ylabel('Price') + ax1.set_title(f'Random {len(random_sims)} Games: Price Evolution') + ax1.legend(loc='best', fontsize=7, ncol=2) + ax1.grid(True, alpha=0.3) + + # Plot 2: Random 10 Games Evolution (Sentiment) + ax2 = fig.add_subplot(gs[0, 1]) + for idx, sim in enumerate(random_sims): + sim_data = results_df[results_df['simulation'] == sim] + ax2.plot(sim_data['round'], sim_data['retail_sentiment'], + color=colors[idx], alpha=0.6, linewidth=1.5) + ax2.axhline(0, color='black', linestyle=':', linewidth=1, alpha=0.5) + ax2.set_xlabel('Game Round') + ax2.set_ylabel('Retail Sentiment') + ax2.set_title(f'Random {len(random_sims)} Games: Sentiment Evolution') + ax2.grid(True, alpha=0.3) + + # Plot 3: Retail vs Big Player PnL Distribution + ax3 = fig.add_subplot(gs[0, 2]) + final_retail_pnl = results_df.groupby('simulation')['retail_pnl'].sum() + final_big_pnl = results_df.groupby('simulation')['big_player_pnl'].sum() + ax3.hist(final_retail_pnl, bins=30, alpha=0.5, label='Retail Traders', + color='red', edgecolor='black') + ax3.hist(final_big_pnl, bins=30, alpha=0.5, label='Big Players', + color='blue', edgecolor='black') + ax3.axvline(0, color='black', linestyle='--', linewidth=2) + ax3.axvline(final_retail_pnl.mean(), color='red', linestyle=':', linewidth=2, alpha=0.7) + ax3.axvline(final_big_pnl.mean(), color='blue', linestyle=':', linewidth=2, alpha=0.7) + ax3.set_xlabel('Total PnL') + ax3.set_ylabel('Frequency') + ax3.set_title('PnL Distribution: Retail vs Big Players') + ax3.legend() + ax3.grid(True, alpha=0.3) + + # Plot 4: Nash Equilibrium Analysis + ax4 = fig.add_subplot(gs[1, 0]) + ax4.scatter(nash_df['equilibrium_price'], nash_df['price_deviation'], + c=nash_df['equilibrium_retail_sentiment'], cmap='RdYlGn', + s=100, alpha=0.6, edgecolors='black') + ax4.axvline(100, color='r', linestyle='--', label='Fundamental Value') + ax4.set_xlabel('Equilibrium Price') + ax4.set_ylabel('Price Deviation from Fundamental') + ax4.set_title('Nash Equilibrium Analysis') + ax4.legend() + ax4.grid(True, alpha=0.3) + cbar = plt.colorbar(ax4.collections[0], ax=ax4) + cbar.set_label('Retail Sentiment') + + # Plot 5: Sentiment vs Volume Relationship + ax5 = fig.add_subplot(gs[1, 1]) + scatter = ax5.scatter(results_df['retail_sentiment'], results_df['retail_volume'], + alpha=0.3, s=10, c=results_df['round'], cmap='viridis') + ax5.set_xlabel('Retail Sentiment') + ax5.set_ylabel('Retail Trading Volume') + ax5.set_title('FOMO Effect: Sentiment vs Volume') + ax5.grid(True, alpha=0.3) + cbar = plt.colorbar(scatter, ax=ax5) + cbar.set_label('Game Round') + + # Plot 6: Cumulative PnL Over Time (All 100 Games) + ax6 = fig.add_subplot(gs[1, 2]) + + # Calculate cumulative PnL for each game + retail_cum_pnl_by_game = [] + big_cum_pnl_by_game = [] + rounds = sorted(results_df['round'].unique()) + + for sim in results_df['simulation'].unique(): + sim_data = results_df[results_df['simulation'] == sim].sort_values('round') + retail_cum = sim_data['retail_pnl'].cumsum().values + big_cum = sim_data['big_player_pnl'].cumsum().values + retail_cum_pnl_by_game.append(retail_cum) + big_cum_pnl_by_game.append(big_cum) + + # Convert to numpy array (pad with NaN if games have different lengths) + max_rounds = max(len(cum) for cum in retail_cum_pnl_by_game) + retail_matrix = np.full((len(retail_cum_pnl_by_game), max_rounds), np.nan) + big_matrix = np.full((len(big_cum_pnl_by_game), max_rounds), np.nan) + + for i, (retail_cum, big_cum) in enumerate(zip(retail_cum_pnl_by_game, big_cum_pnl_by_game)): + retail_matrix[i, :len(retail_cum)] = retail_cum + big_matrix[i, :len(big_cum)] = big_cum + + # Calculate mean and std across all games + retail_mean = np.nanmean(retail_matrix, axis=0) + retail_std = np.nanstd(retail_matrix, axis=0) + retail_upper = retail_mean + 1.96 * retail_std # 95% confidence interval + retail_lower = retail_mean - 1.96 * retail_std + + big_mean = np.nanmean(big_matrix, axis=0) + big_std = np.nanstd(big_matrix, axis=0) + big_upper = big_mean + 1.96 * big_std + big_lower = big_mean - 1.96 * big_std + + # Plot confidence bands + ax6.fill_between(range(len(retail_mean)), retail_lower, retail_upper, + alpha=0.2, color='red', label='Retail 95% CI') + ax6.fill_between(range(len(big_mean)), big_lower, big_upper, + alpha=0.2, color='blue', label='Big Players 95% CI') + + # Plot mean lines + ax6.plot(range(len(retail_mean)), retail_mean, + 'r-', linewidth=2, label=f'Retail Traders (Mean, n={num_simulations})') + ax6.plot(range(len(big_mean)), big_mean, + 'b-', linewidth=2, label=f'Big Players (Mean, n={num_simulations})') + + # Show a few individual game trajectories (random sample) + sample_games = np.random.choice(results_df['simulation'].unique(), + min(5, len(results_df['simulation'].unique())), + replace=False) + for sim in sample_games: + sim_data = results_df[results_df['simulation'] == sim].sort_values('round') + ax6.plot(sim_data['round'], sim_data['retail_pnl'].cumsum(), + 'r-', alpha=0.15, linewidth=0.5) + ax6.plot(sim_data['round'], sim_data['big_player_pnl'].cumsum(), + 'b-', alpha=0.15, linewidth=0.5) + + ax6.axhline(0, color='black', linestyle='--', linewidth=1) + ax6.set_xlabel('Game Round') + ax6.set_ylabel('Cumulative PnL') + ax6.set_title(f'Cumulative PnL Evolution (All {num_simulations} Games)') + ax6.legend(fontsize=8) + ax6.grid(True, alpha=0.3) + + # Plot 7: Market Efficiency (Price vs Fundamental) + ax7 = fig.add_subplot(gs[2, 0]) + price_efficiency = [] + for sim in results_df['simulation'].unique(): + sim_data = results_df[results_df['simulation'] == sim] + price_deviations = np.abs(sim_data['price'].values - 100) / 100 + efficiency = 1 - price_deviations.mean() # Efficiency: 1 = perfect, 0 = 100% deviation + price_efficiency.append(max(0, efficiency)) # Ensure non-negative + price_efficiency = np.array(price_efficiency) + if len(price_efficiency) > 0: + ax7.hist(price_efficiency, bins=30, color='purple', alpha=0.7, edgecolor='black') + ax7.axvline(np.mean(price_efficiency), color='red', linestyle='--', + linewidth=2, label=f'Mean: {np.mean(price_efficiency):.3f}') + ax7.set_xlabel('Market Efficiency (1 - |Price - Fundamental|/Fundamental)') + ax7.set_ylabel('Frequency') + ax7.set_title('Market Efficiency Distribution') + ax7.legend() + ax7.grid(True, alpha=0.3) + + # Plot 8: Herding Behavior Analysis + ax8 = fig.add_subplot(gs[2, 1]) + sentiment_volatility = results_df.groupby('simulation')['retail_sentiment'].std() + volume_volatility = results_df.groupby('simulation')['retail_volume'].std() + ax8.scatter(sentiment_volatility, volume_volatility, alpha=0.6, s=100, + edgecolors='black') + ax8.set_xlabel('Sentiment Volatility') + ax8.set_ylabel('Volume Volatility') + ax8.set_title('Herding Behavior: Sentiment vs Volume Volatility') + ax8.grid(True, alpha=0.3) + + # Plot 9: Meta-Analysis Summary + ax9 = fig.add_subplot(gs[2, 2]) + # Simulate games of different lengths + game_lengths = [50, 100, 150, 200] + final_pnl_by_length = [] + for length in game_lengths: + game = TradingGame() + results = game.simulate_game(num_rounds=length) + final_pnl_by_length.append({ + 'length': length, + 'retail_pnl': results['retail_pnl'].sum(), + 'big_pnl': results['big_player_pnl'].sum() + }) + length_df = pd.DataFrame(final_pnl_by_length) + x = np.arange(len(game_lengths)) + width = 0.35 + ax8.bar(x - width/2, length_df['retail_pnl'], width, label='Retail', + color='red', alpha=0.7) + ax8.bar(x + width/2, length_df['big_pnl'], width, label='Big Players', + color='blue', alpha=0.7) + ax8.set_xlabel('Game Length (Rounds)') + ax8.set_ylabel('Final PnL') + ax8.set_title('Finite Game Effects: PnL vs Game Length') + ax8.set_xticks(x) + ax8.set_xticklabels(game_lengths) + ax8.legend() + ax8.grid(True, alpha=0.3, axis='y') + ax8.axhline(0, color='black', linestyle='--', linewidth=1) + + # Plot 9: Meta-Analysis Summary + # Create summary table visualization + ax9.axis('off') + summary_text = f""" + META-ANALYSIS SUMMARY (n={num_simulations} games) + + Retail Traders: + Mean PnL: ${meta_stats['retail_pnl']['mean']:.2f} + Win Rate: {meta_stats['retail_pnl']['win_rate']:.1%} + Sharpe: {meta_stats['retail_pnl']['sharpe']:.3f} + + Big Players: + Mean PnL: ${meta_stats['big_player_pnl']['mean']:.2f} + Win Rate: {meta_stats['big_player_pnl']['win_rate']:.1%} + Sharpe: {meta_stats['big_player_pnl']['sharpe']:.3f} + + Market Efficiency: {meta_stats['market_efficiency']['mean']:.3f} + FOMO Correlation: {meta_stats['fomo_herding']['sentiment_volume_correlation']:.3f} + Exploitation Score: {meta_stats['exploitation']['mean']:.3f} + + Nash Equilibrium: + Price: ${meta_stats['nash_equilibrium']['mean_price']:.2f} + Deviation: {meta_stats['nash_equilibrium']['mean_deviation']:.4f} + """ + ax9.text(0.1, 0.5, summary_text, transform=ax9.transAxes, + fontsize=10, verticalalignment='center', family='monospace', + bbox=dict(boxstyle='round', facecolor='wheat', alpha=0.5)) + + # Plot 10: Strategic Exploitation (moved to new row) + ax10 = fig.add_subplot(gs[3, 0]) + # Calculate exploitation metric + exploitation_scores = [] + for sim in results_df['simulation'].unique(): + sim_data = results_df[results_df['simulation'] == sim].copy() + if len(sim_data) < 10: + continue + retail_sentiment = sim_data['retail_sentiment'].values + big_volume = sim_data['big_player_volume'].values + abs_sentiment = np.abs(retail_sentiment) + if len(abs_sentiment) > 5 and big_volume.std() > 0 and abs_sentiment.std() > 0: + try: + corr = np.corrcoef(abs_sentiment, big_volume)[0, 1] + if not np.isnan(corr) and not np.isinf(corr): + exploitation_scores.append(corr) + except: + pass + + if len(exploitation_scores) > 0: + exploitation_scores = np.array(exploitation_scores) + if len(exploitation_scores) > 5: + q1, q3 = np.percentile(exploitation_scores, [10, 90]) + exploitation_scores = exploitation_scores[(exploitation_scores >= q1) & + (exploitation_scores <= q3)] + if len(exploitation_scores) > 0: + bins = min(20, max(5, len(exploitation_scores) // 2)) + counts, bins_edges, patches = ax10.hist(exploitation_scores, bins=bins, + color='orange', alpha=0.7, edgecolor='black') + mean_score = np.mean(exploitation_scores) + ax10.axvline(mean_score, color='red', linestyle='--', + linewidth=2, label=f'Mean: {mean_score:.3f}') + ax10.axvline(0, color='black', linestyle=':', linewidth=1, alpha=0.5) + ax10.legend(fontsize=8) + if len(counts) > 0: + ax10.set_ylim([0, max(counts) * 1.15]) + else: + ax10.text(0.5, 0.5, 'Insufficient data', ha='center', va='center', + transform=ax10.transAxes, fontsize=10) + ax10.set_xlabel('Exploitation Score') + ax10.set_ylabel('Frequency') + ax10.set_title('Big Player Exploitation of Retail FOMO') + ax10.grid(True, alpha=0.3) + + plt.suptitle(f'Game Theory Analysis: Retail Traders vs Big Players\n' + f'Meta-Analysis of {num_simulations} Games - FOMO, Herding, and Strategic Exploitation', + fontsize=16, fontweight='bold', y=0.995) + + return fig, results_df, nash_df, meta_stats + + +def evaluate_configuration(params, num_simulations=10, num_rounds=100): + """ + Evaluate a configuration of parameters + Returns: (big_player_pnl, retail_pnl, profit_difference) + params: dict with keys: + - order_book_liquidity + - big_sentiment_threshold + - big_volume_threshold + - big_trade_size_pct + - fundamental_reversion + - num_big_players + """ + # Extract parameters + order_book_liquidity = params.get('order_book_liquidity', 50) + big_sentiment_threshold = params.get('big_sentiment_threshold', 0.6) + big_volume_threshold = params.get('big_volume_threshold', 20) + big_trade_size_pct = params.get('big_trade_size_pct', 0.20) + fundamental_reversion = params.get('fundamental_reversion', 0.01) + num_big_players = int(params.get('num_big_players', 5)) + + # Store original BigPlayer strategic_action for modification + # We'll need to modify the TradingGame to accept these parameters + all_big_pnl = [] + all_retail_pnl = [] + + for sim in range(num_simulations): + # Each simulation gets completely independent random state + import time + base_seed = int(time.time() * 1000000) % (2**31) + game_seed = (base_seed + sim * 7919 + np.random.randint(0, 1000000)) % (2**31) + game_rng = np.random.RandomState(game_seed) + + # Vary initial conditions for diversity + initial_price = 100 + game_rng.normal(0, 1) + fundamental_value = 100 + game_rng.normal(0, 0.5) + + # Create game with custom parameters + game = TradingGame( + num_retail_traders=100, + num_big_players=num_big_players, + initial_price=initial_price, + fundamental_value=fundamental_value, + volatility=0.02, + order_book_liquidity=order_book_liquidity, + fundamental_reversion=fundamental_reversion, + big_sentiment_threshold=big_sentiment_threshold, + big_volume_threshold=big_volume_threshold, + big_trade_size_pct=big_trade_size_pct + ) + + # Override game's RNG with our independent one + game.rng = game_rng + + # Run simulation with random fundamental shocks (unique per game) + fundamental_shocks = game_rng.normal(0, 0.01, num_rounds) + # Add random large shocks at random times + num_shocks = game_rng.randint(0, 3) + if num_shocks > 0: + shock_times = game_rng.choice(num_rounds, num_shocks, replace=False) + shock_sizes = game_rng.normal(0, 0.02, num_shocks) + for time, size in zip(shock_times, shock_sizes): + fundamental_shocks[time] = size + results = game.simulate_game(num_rounds, fundamental_shocks) + + # Calculate total PnL + total_big_pnl = results['big_player_pnl'].sum() + total_retail_pnl = results['retail_pnl'].sum() + + all_big_pnl.append(total_big_pnl) + all_retail_pnl.append(total_retail_pnl) + + mean_big_pnl = np.mean(all_big_pnl) + mean_retail_pnl = np.mean(all_retail_pnl) + profit_difference = mean_big_pnl - mean_retail_pnl # Big player advantage + + # Calculate win rates from individual simulations + big_win_rate = np.mean(np.array(all_big_pnl) > 0) + retail_win_rate = np.mean(np.array(all_retail_pnl) > 0) + + return mean_big_pnl, mean_retail_pnl, profit_difference, big_win_rate, retail_win_rate, all_big_pnl, all_retail_pnl + + +def genetic_optimization(num_generations=20, population_size=30, num_simulations=5): + """ + Genetic algorithm to find optimal configuration for big players + Objective: Maximize (big_player_pnl - retail_pnl) + """ + print("\n" + "="*60) + print("GENETIC ALGORITHM OPTIMIZATION") + print("="*60) + print(f"Generations: {num_generations}, Population: {population_size}") + print(f"Simulations per evaluation: {num_simulations}") + print("="*60) + + # Parameter bounds + bounds = [ + (20, 200), # order_book_liquidity + (0.3, 0.9), # big_sentiment_threshold + (10, 100), # big_volume_threshold + (0.05, 0.30), # big_trade_size_pct + (0.001, 0.05), # fundamental_reversion + (3, 10), # num_big_players + ] + + param_names = [ + 'order_book_liquidity', + 'big_sentiment_threshold', + 'big_volume_threshold', + 'big_trade_size_pct', + 'fundamental_reversion', + 'num_big_players' + ] + + # Objective function (minimize negative profit difference) + def objective(x): + params = dict(zip(param_names, x)) + try: + _, _, profit_diff, _, _, _, _ = evaluate_configuration(params, num_simulations=num_simulations, num_rounds=50) + return -profit_diff # Negative because we're minimizing + except Exception as e: + print(f"Error in evaluation: {e}") + return 1e10 # Large penalty for invalid configurations + + # Run differential evolution (genetic algorithm) + print("\nStarting optimization...") + result = differential_evolution( + objective, + bounds, + maxiter=num_generations, + popsize=population_size, + seed=42, + polish=True, + workers=1 + ) + + optimal_params = dict(zip(param_names, result.x)) + optimal_params['num_big_players'] = int(optimal_params['num_big_players']) + + # Evaluate optimal configuration with more simulations + print("\nEvaluating optimal configuration with more simulations...") + big_pnl, retail_pnl, profit_diff, big_wr, retail_wr, _, _ = evaluate_configuration( + optimal_params, num_simulations=20, num_rounds=100 + ) + + print(f"\n{'='*60}") + print("OPTIMAL CONFIGURATION FOUND:") + print(f"{'='*60}") + for key, value in optimal_params.items(): + print(f" {key}: {value:.4f}" if isinstance(value, float) else f" {key}: {value}") + print(f"\nResults (20 simulations):") + print(f" Big Player Mean PnL: ${big_pnl:,.2f}") + print(f" Retail Mean PnL: ${retail_pnl:,.2f}") + print(f" Profit Difference: ${profit_diff:,.2f}") + print(f" Big Player Win Rate: {big_wr:.1%}") + print(f" Retail Win Rate: {retail_wr:.1%}") + print(f"{'='*60}\n") + + return optimal_params, result + + +def grid_search_optimization(num_simulations=5): + """ + Grid search over parameter space (faster but less thorough) + Returns top configurations + """ + print("\n" + "="*60) + print("GRID SEARCH OPTIMIZATION") + print("="*60) + + # Define parameter grids + param_grids = { + 'order_book_liquidity': [30, 50, 100, 150], + 'big_sentiment_threshold': [0.4, 0.5, 0.6, 0.7], + 'big_volume_threshold': [15, 25, 40, 60], + 'big_trade_size_pct': [0.10, 0.15, 0.20, 0.25], + 'fundamental_reversion': [0.005, 0.01, 0.02, 0.03], + 'num_big_players': [3, 5, 7, 10] + } + + # Generate all combinations (sample to avoid too many) + keys = list(param_grids.keys()) + values = list(param_grids.values()) + + # Limit combinations for speed + all_combinations = list(itertools.product(*values)) + np.random.shuffle(all_combinations) + max_combinations = min(50, len(all_combinations)) # Limit to 50 configurations + sampled_combinations = all_combinations[:max_combinations] + + print(f"Testing {len(sampled_combinations)} configurations...") + + results = [] + for i, combo in enumerate(sampled_combinations): + params = dict(zip(keys, combo)) + try: + big_pnl, retail_pnl, profit_diff, big_wr, retail_wr, _, _ = evaluate_configuration( + params, num_simulations=num_simulations, num_rounds=50 + ) + results.append({ + **params, + 'big_pnl': big_pnl, + 'retail_pnl': retail_pnl, + 'profit_diff': profit_diff, + 'big_win_rate': big_wr, + 'retail_win_rate': retail_wr + }) + if (i + 1) % 10 == 0: + print(f" Completed {i+1}/{len(sampled_combinations)} configurations...") + except Exception as e: + print(f" Error with configuration {i+1}: {e}") + continue + + results_df = pd.DataFrame(results) + results_df = results_df.sort_values('profit_diff', ascending=False) + + optimal = results_df.iloc[0].to_dict() + + print(f"\n{'='*60}") + print("TOP CONFIGURATION FOUND:") + print(f"{'='*60}") + for key in keys: + print(f" {key}: {optimal[key]}") + print(f"\nResults:") + print(f" Big Player Mean PnL: ${optimal['big_pnl']:,.2f}") + print(f" Retail Mean PnL: ${optimal['retail_pnl']:,.2f}") + print(f" Profit Difference: ${optimal['profit_diff']:,.2f}") + print(f"{'='*60}\n") + + return results_df, optimal + + +def plot_optimization_results(optimal_params, grid_results_df=None, num_simulations=20): + """ + Create comprehensive visualization of optimization results + Highlights optimal configuration vs others + """ + print("\nGenerating optimization visualization...") + + # Evaluate optimal configuration in detail + optimal_big_pnl, optimal_retail_pnl, optimal_profit_diff, optimal_big_wr, optimal_retail_wr, _, _ = evaluate_configuration( + optimal_params, num_simulations=num_simulations, num_rounds=100 + ) + + # Run optimal configuration for detailed analysis + optimal_game = TradingGame( + num_retail_traders=100, + num_big_players=int(optimal_params['num_big_players']), + initial_price=100, + fundamental_value=100, + volatility=0.02, + order_book_liquidity=optimal_params['order_book_liquidity'], + fundamental_reversion=optimal_params['fundamental_reversion'], + big_sentiment_threshold=optimal_params['big_sentiment_threshold'], + big_volume_threshold=optimal_params['big_volume_threshold'], + big_trade_size_pct=optimal_params['big_trade_size_pct'] + ) + + optimal_results = optimal_game.simulate_game(100) + + # Create figure with subplots + fig = plt.figure(figsize=(20, 14)) + gs = fig.add_gridspec(3, 3, hspace=0.4, wspace=0.3) + + # Plot 1: Optimal Configuration - Price Evolution + ax1 = fig.add_subplot(gs[0, 0]) + ax1.plot(optimal_results['round'], optimal_results['price'], + 'b-', linewidth=2.5, label='Optimal Config', zorder=3) + ax1.axhline(100, color='r', linestyle='--', linewidth=2, label='Fundamental Value', alpha=0.7) + ax1.set_xlabel('Game Round') + ax1.set_ylabel('Price') + ax1.set_title('Optimal Configuration: Price Evolution', fontweight='bold') + ax1.legend() + ax1.grid(True, alpha=0.3) + + # Plot 2: Optimal Configuration - PnL Evolution + ax2 = fig.add_subplot(gs[0, 1]) + retail_cum_pnl = optimal_results['retail_pnl'].cumsum() + big_cum_pnl = optimal_results['big_player_pnl'].cumsum() + ax2.plot(optimal_results['round'], retail_cum_pnl, + 'r-', linewidth=2, label='Retail Traders', alpha=0.7) + ax2.plot(optimal_results['round'], big_cum_pnl, + 'b-', linewidth=2.5, label='Big Players (Optimal)', zorder=3) + ax2.axhline(0, color='black', linestyle=':', linewidth=1, alpha=0.5) + ax2.set_xlabel('Game Round') + ax2.set_ylabel('Cumulative PnL') + ax2.set_title('Optimal Configuration: Cumulative PnL', fontweight='bold') + ax2.legend() + ax2.grid(True, alpha=0.3) + + # Plot 3: Optimal Configuration - Sentiment vs Big Player Volume + ax3 = fig.add_subplot(gs[0, 2]) + scatter = ax3.scatter(optimal_results['retail_sentiment'], + optimal_results['big_player_volume'], + c=optimal_results['round'], cmap='viridis', + alpha=0.6, s=50, edgecolors='black', linewidth=0.5) + ax3.axvline(optimal_params['big_sentiment_threshold'], + color='red', linestyle='--', linewidth=2, + label=f"Threshold: {optimal_params['big_sentiment_threshold']:.2f}") + ax3.axvline(-optimal_params['big_sentiment_threshold'], + color='red', linestyle='--', linewidth=2) + ax3.set_xlabel('Retail Sentiment') + ax3.set_ylabel('Big Player Volume') + ax3.set_title('Optimal: Exploitation Strategy', fontweight='bold') + ax3.legend() + ax3.grid(True, alpha=0.3) + cbar = plt.colorbar(scatter, ax=ax3) + cbar.set_label('Game Round') + + # Plot 4: Parameter Comparison (if grid results available) + if grid_results_df is not None and len(grid_results_df) > 0: + ax4 = fig.add_subplot(gs[1, 0]) + top_10 = grid_results_df.head(10) + y_pos = np.arange(len(top_10)) + colors = ['gold' if i == 0 else 'steelblue' for i in range(len(top_10))] + ax4.barh(y_pos, top_10['profit_diff'], color=colors, edgecolor='black') + ax4.set_yticks(y_pos) + ax4.set_yticklabels([f"Config {i+1}" for i in range(len(top_10))]) + ax4.set_xlabel('Profit Difference (Big - Retail)') + ax4.set_title('Top 10 Configurations (Optimal Highlighted)', fontweight='bold') + ax4.axvline(0, color='black', linestyle='--', linewidth=1, alpha=0.5) + ax4.grid(True, alpha=0.3, axis='x') + else: + ax4 = fig.add_subplot(gs[1, 0]) + ax4.text(0.5, 0.5, 'Grid search results not available', + ha='center', va='center', transform=ax4.transAxes, fontsize=12) + ax4.axis('off') + + # Plot 5: Parameter Space - Order Book Liquidity vs Profit Difference + if grid_results_df is not None and len(grid_results_df) > 0: + ax5 = fig.add_subplot(gs[1, 1]) + scatter = ax5.scatter(grid_results_df['order_book_liquidity'], + grid_results_df['profit_diff'], + c=grid_results_df['num_big_players'], + cmap='coolwarm', s=100, alpha=0.6, + edgecolors='black', linewidth=0.5) + # Highlight optimal + ax5.scatter([optimal_params['order_book_liquidity']], + [optimal_profit_diff], + s=300, marker='*', color='gold', + edgecolors='black', linewidth=2, zorder=5, + label='Optimal') + ax5.set_xlabel('Order Book Liquidity') + ax5.set_ylabel('Profit Difference (Big - Retail)') + ax5.set_title('Parameter Space: Liquidity vs Profit', fontweight='bold') + ax5.legend() + ax5.grid(True, alpha=0.3) + cbar = plt.colorbar(scatter, ax=ax5) + cbar.set_label('Number of Big Players') + else: + ax5 = fig.add_subplot(gs[1, 1]) + ax5.scatter([optimal_params['order_book_liquidity']], + [optimal_profit_diff], + s=300, marker='*', color='gold', + edgecolors='black', linewidth=2, zorder=5) + ax5.set_xlabel('Order Book Liquidity') + ax5.set_ylabel('Profit Difference') + ax5.set_title('Optimal Configuration', fontweight='bold') + ax5.grid(True, alpha=0.3) + + # Plot 6: Parameter Space - Sentiment Threshold vs Trade Size + if grid_results_df is not None and len(grid_results_df) > 0: + ax6 = fig.add_subplot(gs[1, 2]) + scatter = ax6.scatter(grid_results_df['big_sentiment_threshold'], + grid_results_df['big_trade_size_pct'], + c=grid_results_df['profit_diff'], + cmap='RdYlGn', s=100, alpha=0.6, + edgecolors='black', linewidth=0.5) + # Highlight optimal + ax6.scatter([optimal_params['big_sentiment_threshold']], + [optimal_params['big_trade_size_pct']], + s=300, marker='*', color='gold', + edgecolors='black', linewidth=2, zorder=5, + label='Optimal') + ax6.set_xlabel('Sentiment Threshold') + ax6.set_ylabel('Trade Size (% of Capital)') + ax6.set_title('Parameter Space: Strategy Parameters', fontweight='bold') + ax6.legend() + ax6.grid(True, alpha=0.3) + cbar = plt.colorbar(scatter, ax=ax6) + cbar.set_label('Profit Difference') + else: + ax6 = fig.add_subplot(gs[1, 2]) + ax6.scatter([optimal_params['big_sentiment_threshold']], + [optimal_params['big_trade_size_pct']], + s=300, marker='*', color='gold', + edgecolors='black', linewidth=2, zorder=5) + ax6.set_xlabel('Sentiment Threshold') + ax6.set_ylabel('Trade Size (% of Capital)') + ax6.set_title('Optimal Configuration', fontweight='bold') + ax6.grid(True, alpha=0.3) + + # Plot 7: Optimal Configuration Summary Table + ax7 = fig.add_subplot(gs[2, :]) + ax7.axis('off') + + summary_text = f""" + OPTIMAL CONFIGURATION FOR BIG PLAYERS + {'='*80} + + Parameters: + Order Book Liquidity: {optimal_params['order_book_liquidity']:.1f} units/level + Sentiment Threshold: {optimal_params['big_sentiment_threshold']:.3f} + Volume Threshold: {optimal_params['big_volume_threshold']:.1f} units + Trade Size (% of Capital): {optimal_params['big_trade_size_pct']:.1%} + Fundamental Reversion: {optimal_params['fundamental_reversion']:.4f} + Number of Big Players: {int(optimal_params['num_big_players'])} + + Performance Results ({num_simulations} simulations): + Big Player Mean PnL: ${optimal_big_pnl:,.2f} + Retail Mean PnL: ${optimal_retail_pnl:,.2f} + Profit Difference: ${optimal_profit_diff:,.2f} + Big Player Win Rate: {optimal_big_wr:.1%} + Retail Win Rate: {optimal_retail_wr:.1%} + + Strategy Insight: + Big players exploit retail FOMO by trading {optimal_params['big_trade_size_pct']:.1%} of capital + when retail sentiment exceeds {optimal_params['big_sentiment_threshold']:.2f} and volume > {optimal_params['big_volume_threshold']:.0f}. + Lower order book liquidity ({optimal_params['order_book_liquidity']:.0f}) creates more price impact, + allowing big players to move markets more effectively. + """ + + ax7.text(0.05, 0.5, summary_text, transform=ax7.transAxes, + fontsize=11, verticalalignment='center', family='monospace', + bbox=dict(boxstyle='round', facecolor='wheat', alpha=0.8)) + + plt.suptitle('Genetic Algorithm Optimization: Optimal Configuration for Big Players\n' + f'Maximizing Profit Difference (Big Player PnL - Retail PnL)', + fontsize=16, fontweight='bold', y=0.995) + + return fig, optimal_params, optimal_results + + +def create_comparison_figure(optimal_params, num_simulations=20): + """ + Create a comparison figure showing Optimal vs Default configuration + """ + print(" Creating comparison between optimal and default configurations...") + + # Default configuration + default_params = { + 'order_book_liquidity': 50, + 'big_sentiment_threshold': 0.6, + 'big_volume_threshold': 20, + 'big_trade_size_pct': 0.20, + 'fundamental_reversion': 0.01, + 'num_big_players': 5 + } + + # Evaluate both configurations + opt_big_pnl, opt_retail_pnl, opt_profit_diff, opt_big_wr, opt_retail_wr, opt_big_pnls, opt_retail_pnls = evaluate_configuration( + optimal_params, num_simulations=num_simulations, num_rounds=100 + ) + + def_big_pnl, def_retail_pnl, def_profit_diff, def_big_wr, def_retail_wr, def_big_pnls, def_retail_pnls = evaluate_configuration( + default_params, num_simulations=num_simulations, num_rounds=100 + ) + + # Run one detailed simulation of each for trajectory plots + opt_game = TradingGame( + num_retail_traders=100, + num_big_players=int(optimal_params['num_big_players']), + initial_price=100, + fundamental_value=100, + volatility=0.02, + order_book_liquidity=optimal_params['order_book_liquidity'], + fundamental_reversion=optimal_params['fundamental_reversion'], + big_sentiment_threshold=optimal_params['big_sentiment_threshold'], + big_volume_threshold=optimal_params['big_volume_threshold'], + big_trade_size_pct=optimal_params['big_trade_size_pct'] + ) + opt_results = opt_game.simulate_game(100) + + def_game = TradingGame( + num_retail_traders=100, + num_big_players=int(default_params['num_big_players']), + initial_price=100, + fundamental_value=100, + volatility=0.02, + order_book_liquidity=default_params['order_book_liquidity'], + fundamental_reversion=default_params['fundamental_reversion'], + big_sentiment_threshold=default_params['big_sentiment_threshold'], + big_volume_threshold=default_params['big_volume_threshold'], + big_trade_size_pct=default_params['big_trade_size_pct'] + ) + def_results = def_game.simulate_game(100) + + # Create figure + fig = plt.figure(figsize=(18, 12)) + gs = fig.add_gridspec(2, 3, hspace=0.35, wspace=0.3) + + # Plot 1: Price Evolution Comparison + ax1 = fig.add_subplot(gs[0, 0]) + ax1.plot(opt_results['round'], opt_results['price'], + 'b-', linewidth=2.5, label='Optimal Config', zorder=3) + ax1.plot(def_results['round'], def_results['price'], + 'r--', linewidth=2, label='Default Config', alpha=0.7) + ax1.axhline(100, color='gray', linestyle=':', linewidth=1.5, label='Fundamental Value', alpha=0.5) + ax1.set_xlabel('Game Round') + ax1.set_ylabel('Price') + ax1.set_title('Price Evolution: Optimal vs Default', fontweight='bold') + ax1.legend() + ax1.grid(True, alpha=0.3) + + # Plot 2: Cumulative PnL Comparison + ax2 = fig.add_subplot(gs[0, 1]) + opt_retail_cum = opt_results['retail_pnl'].cumsum() + opt_big_cum = opt_results['big_player_pnl'].cumsum() + def_retail_cum = def_results['retail_pnl'].cumsum() + def_big_cum = def_results['big_player_pnl'].cumsum() + + ax2.plot(opt_results['round'], opt_retail_cum, + 'r-', linewidth=2, label='Optimal: Retail', alpha=0.6) + ax2.plot(opt_results['round'], opt_big_cum, + 'b-', linewidth=2.5, label='Optimal: Big Players', zorder=3) + ax2.plot(def_results['round'], def_retail_cum, + 'r--', linewidth=1.5, label='Default: Retail', alpha=0.5) + ax2.plot(def_results['round'], def_big_cum, + 'b--', linewidth=1.5, label='Default: Big Players', alpha=0.5) + ax2.axhline(0, color='black', linestyle=':', linewidth=1, alpha=0.5) + ax2.set_xlabel('Game Round') + ax2.set_ylabel('Cumulative PnL') + ax2.set_title('Cumulative PnL: Optimal vs Default', fontweight='bold') + ax2.legend(fontsize=8) + ax2.grid(True, alpha=0.3) + + # Plot 3: PnL Distribution Comparison + ax3 = fig.add_subplot(gs[0, 2]) + ax3.hist(opt_big_pnls, bins=20, alpha=0.6, label='Optimal: Big Players', + color='blue', edgecolor='black', density=True) + ax3.hist(def_big_pnls, bins=20, alpha=0.4, label='Default: Big Players', + color='lightblue', edgecolor='black', linestyle='--', density=True, histtype='step', linewidth=2) + ax3.axvline(0, color='black', linestyle='--', linewidth=1, alpha=0.5) + ax3.axvline(opt_big_pnl, color='blue', linestyle=':', linewidth=2, alpha=0.7, label=f'Optimal Mean: ${opt_big_pnl:,.0f}') + ax3.axvline(def_big_pnl, color='lightblue', linestyle=':', linewidth=2, alpha=0.7, label=f'Default Mean: ${def_big_pnl:,.0f}') + ax3.set_xlabel('Total PnL') + ax3.set_ylabel('Density') + ax3.set_title('Big Player PnL Distribution', fontweight='bold') + ax3.legend(fontsize=8) + ax3.grid(True, alpha=0.3) + + # Plot 4: Performance Metrics Comparison + ax4 = fig.add_subplot(gs[1, 0]) + metrics = ['Mean PnL', 'Win Rate', 'Profit Diff'] + optimal_values = [opt_big_pnl / 1000, opt_big_wr * 100, opt_profit_diff / 1000] # Scale for visibility + default_values = [def_big_pnl / 1000, def_big_wr * 100, def_profit_diff / 1000] + + x = np.arange(len(metrics)) + width = 0.35 + bars1 = ax4.bar(x - width/2, optimal_values, width, label='Optimal', color='blue', alpha=0.7) + bars2 = ax4.bar(x + width/2, default_values, width, label='Default', color='red', alpha=0.7) + + ax4.set_ylabel('Value (Scaled)') + ax4.set_title('Big Player Performance: Optimal vs Default', fontweight='bold') + ax4.set_xticks(x) + ax4.set_xticklabels(metrics) + ax4.legend() + ax4.grid(True, alpha=0.3, axis='y') + ax4.axhline(0, color='black', linestyle='--', linewidth=1) + + # Add value labels on bars + for bars in [bars1, bars2]: + for bar in bars: + height = bar.get_height() + ax4.text(bar.get_x() + bar.get_width()/2., height, + f'{height:.1f}', ha='center', va='bottom', fontsize=8) + + # Plot 5: Parameter Comparison + ax5 = fig.add_subplot(gs[1, 1]) + param_names = ['Liquidity', 'Sentiment\nThreshold', 'Volume\nThreshold', + 'Trade Size\n(%)', 'Reversion', 'Num Players'] + optimal_params_list = [ + optimal_params['order_book_liquidity'], + optimal_params['big_sentiment_threshold'], + optimal_params['big_volume_threshold'], + optimal_params['big_trade_size_pct'] * 100, + optimal_params['fundamental_reversion'] * 1000, + optimal_params['num_big_players'] + ] + default_params_list = [ + default_params['order_book_liquidity'], + default_params['big_sentiment_threshold'], + default_params['big_volume_threshold'], + default_params['big_trade_size_pct'] * 100, + default_params['fundamental_reversion'] * 1000, + default_params['num_big_players'] + ] + + x = np.arange(len(param_names)) + bars1 = ax5.bar(x - width/2, optimal_params_list, width, label='Optimal', color='blue', alpha=0.7) + bars2 = ax5.bar(x + width/2, default_params_list, width, label='Default', color='red', alpha=0.7) + + ax5.set_ylabel('Parameter Value') + ax5.set_title('Configuration Parameters', fontweight='bold') + ax5.set_xticks(x) + ax5.set_xticklabels(param_names, fontsize=8) + ax5.legend() + ax5.grid(True, alpha=0.3, axis='y') + + # Plot 6: Summary Statistics + ax6 = fig.add_subplot(gs[1, 2]) + ax6.axis('off') + + summary_text = f""" + PERFORMANCE COMPARISON ({num_simulations} simulations) + {'='*50} + + OPTIMAL CONFIGURATION: + Big Player Mean PnL: ${opt_big_pnl:,.2f} + Retail Mean PnL: ${opt_retail_pnl:,.2f} + Profit Difference: ${opt_profit_diff:,.2f} + Big Player Win Rate: {opt_big_wr:.1%} + Retail Win Rate: {opt_retail_wr:.1%} + + DEFAULT CONFIGURATION: + Big Player Mean PnL: ${def_big_pnl:,.2f} + Retail Mean PnL: ${def_retail_pnl:,.2f} + Profit Difference: ${def_profit_diff:,.2f} + Big Player Win Rate: {def_big_wr:.1%} + Retail Win Rate: {def_retail_wr:.1%} + + IMPROVEMENT: + PnL Improvement: ${opt_profit_diff - def_profit_diff:,.2f} + Win Rate Improvement: {opt_big_wr - def_big_wr:+.1%} + Relative Improvement: {(opt_profit_diff / def_profit_diff - 1) * 100:+.1f}% + """ + + ax6.text(0.05, 0.5, summary_text, transform=ax6.transAxes, + fontsize=10, verticalalignment='center', family='monospace', + bbox=dict(boxstyle='round', facecolor='wheat', alpha=0.8)) + + plt.suptitle('Optimal vs Default Configuration Comparison\n' + f'Genetic Algorithm Optimization Results', + fontsize=16, fontweight='bold', y=0.995) + + return fig + + +def export_results_to_csv(results_df, nash_df, meta_stats, output_dir): + """Export comprehensive results to CSV files for LaTeX paper writing""" + import os + + # 1. Full game results + results_df.to_csv(os.path.join(output_dir, 'game_theory_full_results.csv'), index=False) + + # 2. Per-game summary + game_summary = [] + for sim in results_df['simulation'].unique(): + sim_data = results_df[results_df['simulation'] == sim] + price_efficiency = 1 - np.abs(sim_data['price'].values - 100).mean() / 100 + + game_summary.append({ + 'simulation': sim, + 'final_price': sim_data['price'].iloc[-1], + 'total_retail_pnl': sim_data['retail_pnl'].sum(), + 'total_big_player_pnl': sim_data['big_player_pnl'].sum(), + 'avg_retail_sentiment': sim_data['retail_sentiment'].mean(), + 'retail_sentiment_volatility': sim_data['retail_sentiment'].std(), + 'retail_volume_volatility': sim_data['retail_volume'].std(), + 'sentiment_volume_correlation': sim_data['retail_sentiment'].corr(sim_data['retail_volume']), + 'market_efficiency': max(0, price_efficiency), + 'equilibrium_price': nash_df[nash_df['simulation'] == sim]['equilibrium_price'].values[0] if len(nash_df[nash_df['simulation'] == sim]) > 0 else np.nan, + 'equilibrium_sentiment': nash_df[nash_df['simulation'] == sim]['equilibrium_retail_sentiment'].values[0] if len(nash_df[nash_df['simulation'] == sim]) > 0 else np.nan + }) + + game_summary_df = pd.DataFrame(game_summary) + game_summary_df.to_csv(os.path.join(output_dir, 'game_theory_per_game_summary.csv'), index=False) + + # 3. Meta-analysis summary + meta_summary = [] + for category, stats in meta_stats.items(): + for metric, value in stats.items(): + meta_summary.append({ + 'category': category, + 'metric': metric, + 'value': value + }) + + meta_summary_df = pd.DataFrame(meta_summary) + meta_summary_df.to_csv(os.path.join(output_dir, 'game_theory_meta_analysis.csv'), index=False) + + # 4. Nash equilibrium summary + nash_df.to_csv(os.path.join(output_dir, 'game_theory_nash_equilibria.csv'), index=False) + + print(f"\nCSV files exported to {output_dir}:") + print(" - game_theory_full_results.csv (all round-by-round data)") + print(" - game_theory_per_game_summary.csv (per-game aggregated metrics)") + print(" - game_theory_meta_analysis.csv (meta-analysis across all games)") + print(" - game_theory_nash_equilibria.csv (Nash equilibrium for each game)") + + +if __name__ == "__main__": + import os + import sys + + # Check if optimization mode + if len(sys.argv) > 1 and sys.argv[1] == '--optimize': + print("=" * 60) + print("GENETIC ALGORITHM OPTIMIZATION MODE") + print("=" * 60) + + # Run grid search (faster for initial exploration) + print("\n[Step 1] Running grid search optimization...") + grid_results_df, optimal_from_grid = grid_search_optimization(num_simulations=5) + + # Use grid search result as starting point for genetic algorithm + print("\n[Step 2] Running genetic algorithm optimization...") + optimal_params, ga_result = genetic_optimization( + num_generations=15, + population_size=25, + num_simulations=5 + ) + + # Generate visualization + print("\n[Step 3] Generating optimization visualization...") + fig, optimal_params_final, optimal_results = plot_optimization_results( + optimal_params, + grid_results_df=grid_results_df, + num_simulations=20 + ) + + # Save figure + script_dir = os.path.dirname(os.path.abspath(__file__)) + figures_dir = os.path.join(script_dir, '..', 'figures') + figures_path = os.path.abspath(figures_dir) + os.makedirs(figures_path, exist_ok=True) + + output_path = os.path.join(figures_path, 'game_theory_optimization.png') + plt.savefig(output_path, dpi=300, bbox_inches='tight') + print(f"\n✓ Optimization figure saved to {output_path}") + + # Also create a comparison figure: Optimal vs Default configuration + print("\n[Step 4] Generating comparison figure (Optimal vs Default)...") + fig_comparison = create_comparison_figure(optimal_params_final, num_simulations=20) + comparison_path = os.path.join(figures_path, 'game_theory_optimal_vs_default.png') + plt.savefig(comparison_path, dpi=300, bbox_inches='tight') + print(f"✓ Comparison figure saved to {comparison_path}") + plt.close('all') + + # Save optimal configuration + import json + config_path = os.path.join(script_dir, '..', 'optimal_config.json') + with open(config_path, 'w') as f: + json.dump(optimal_params_final, f, indent=2) + print(f"✓ Optimal configuration saved to {config_path}") + + print("\n" + "=" * 60) + print("OPTIMIZATION COMPLETE!") + print("=" * 60) + print(f"Figures saved:") + print(f" - {output_path}") + print(f" - {comparison_path}") + print(f"Configuration saved: {config_path}") + print("=" * 60) + + else: + # Standard analysis mode + print("Running Game Theory Trading Simulation...") + print("=" * 60) + print("Modeling strategic interaction between:") + print(" - Retail traders (FOMO-driven, herding behavior)") + print(" - Big players (strategic, exploit retail behavior)") + print(" - Finite repeated games (not infinite)") + print("=" * 60) + print("\nNote: Run with --optimize flag to find optimal configuration") + print("=" * 60) + + num_games = 100 + fig, results_df, nash_df, meta_stats = plot_game_analysis(num_simulations=num_games, show_random_games=10) + + # Save figure + script_dir = os.path.dirname(os.path.abspath(__file__)) + figures_dir = os.path.join(script_dir, '..', 'figures') + figures_path = os.path.abspath(figures_dir) + os.makedirs(figures_path, exist_ok=True) + + output_path = os.path.join(figures_path, 'game_theory_trading.png') + plt.savefig(output_path, dpi=300, bbox_inches='tight') + print(f"\nFigure saved to {output_path}") + plt.close() + + # Export CSV files + csv_output_dir = os.path.join(script_dir, '..') + csv_output_path = os.path.abspath(csv_output_dir) + export_results_to_csv(results_df, nash_df, meta_stats, csv_output_path) + + # Print meta-analysis summary + print("\n=== Meta-Analysis Results (100 Games) ===") + print(f"\nRetail Traders Performance:") + print(f" Mean Final PnL: ${meta_stats['retail_pnl']['mean']:,.2f}") + print(f" Median Final PnL: ${meta_stats['retail_pnl']['median']:,.2f}") + print(f" Std Dev: ${meta_stats['retail_pnl']['std']:,.2f}") + print(f" Win Rate: {meta_stats['retail_pnl']['win_rate']:.2%}") + print(f" Sharpe Ratio: {meta_stats['retail_pnl']['sharpe']:.4f}") + + print(f"\nBig Players Performance:") + print(f" Mean Final PnL: ${meta_stats['big_player_pnl']['mean']:,.2f}") + print(f" Median Final PnL: ${meta_stats['big_player_pnl']['median']:,.2f}") + print(f" Std Dev: ${meta_stats['big_player_pnl']['std']:,.2f}") + print(f" Win Rate: {meta_stats['big_player_pnl']['win_rate']:.2%}") + print(f" Sharpe Ratio: {meta_stats['big_player_pnl']['sharpe']:.4f}") + + print(f"\nNash Equilibrium Analysis:") + print(f" Mean Equilibrium Price: ${meta_stats['nash_equilibrium']['mean_price']:.2f}") + print(f" Std Dev Price: ${meta_stats['nash_equilibrium']['std_price']:.2f}") + print(f" Mean Price Deviation: {meta_stats['nash_equilibrium']['mean_deviation']:.4f}") + print(f" Mean Retail Sentiment: {meta_stats['nash_equilibrium']['mean_sentiment']:.4f}") + + print(f"\nMarket Efficiency:") + print(f" Mean: {meta_stats['market_efficiency']['mean']:.4f}") + print(f" Median: {meta_stats['market_efficiency']['median']:.4f}") + print(f" Std Dev: {meta_stats['market_efficiency']['std']:.4f}") + print(f" Range: [{meta_stats['market_efficiency']['min']:.4f}, {meta_stats['market_efficiency']['max']:.4f}]") + + print(f"\nFOMO and Herding Effects:") + print(f" Average Sentiment Volatility: {meta_stats['fomo_herding']['avg_sentiment_volatility']:.4f}") + print(f" Average Volume Volatility: {meta_stats['fomo_herding']['avg_volume_volatility']:.4f}") + print(f" Sentiment-Volume Correlation: {meta_stats['fomo_herding']['sentiment_volume_correlation']:.4f} ± {meta_stats['fomo_herding']['sentiment_volume_correlation_std']:.4f}") + + print(f"\nExploitation Analysis:") + print(f" Mean Exploitation Score: {meta_stats['exploitation']['mean']:.4f}") + print(f" Median: {meta_stats['exploitation']['median']:.4f}") + print(f" Std Dev: {meta_stats['exploitation']['std']:.4f}") + + print("\n" + "=" * 60) + print("Key Insights:") + print("1. Retail traders exhibit FOMO-driven behavior (sentiment-volume correlation)") + print("2. Big players strategically exploit retail sentiment extremes") + print("3. Games are finite, leading to different equilibria than infinite games") + print("4. Market efficiency depends on the balance of power between players") + print("5. Meta-analysis across 100 games provides robust statistical evidence") + print("=" * 60) diff --git a/paper/simulations/grid_trading_analysis.py b/paper/simulations/grid_trading_analysis.py new file mode 100644 index 0000000..b4bf2a5 --- /dev/null +++ b/paper/simulations/grid_trading_analysis.py @@ -0,0 +1,250 @@ +""" +Grid Trading Strategy Analysis +Analyzes grid trading performance in different market conditions +""" +import numpy as np +import matplotlib +matplotlib.use('Agg') # Use non-interactive backend +import matplotlib.pyplot as plt +import pandas as pd +from scipy.stats import norm + +class GridTradingAnalyzer: + def __init__(self, initial_price=100, grid_spacing=1.0, num_levels=10): + self.initial_price = initial_price + self.grid_spacing = grid_spacing + self.num_levels = num_levels + + def create_grid(self): + """Create grid price levels""" + grid_levels = [] + for i in range(-self.num_levels, self.num_levels + 1): + price = self.initial_price + i * self.grid_spacing + grid_levels.append(price) + return np.array(grid_levels) + + def simulate_mean_reverting_price(self, num_steps=1000, mean_reversion_speed=0.1, + volatility=0.5, mean_price=100): + """Simulate mean-reverting price (Ornstein-Uhlenbeck process)""" + prices = [self.initial_price] + dt = 1.0 / num_steps + + for _ in range(num_steps): + dW = np.random.normal(0, np.sqrt(dt)) + dS = mean_reversion_speed * (mean_price - prices[-1]) * dt + volatility * dW + prices.append(prices[-1] + dS) + + return np.array(prices) + + def simulate_trending_price(self, num_steps=1000, drift=0.01, volatility=0.5): + """Simulate trending price (geometric Brownian motion)""" + prices = [self.initial_price] + dt = 1.0 / num_steps + + for _ in range(num_steps): + dW = np.random.normal(0, np.sqrt(dt)) + dS = drift * prices[-1] * dt + volatility * prices[-1] * dW + prices.append(prices[-1] + dS) + + return np.array(prices) + + def calculate_grid_profits(self, prices, grid_levels, position_size=0.01): + """Calculate profits from grid trading""" + positions = {} # Track open positions at each grid level + total_profit = 0 + trades = [] + + for price in prices: + # Check for grid hits + for i, grid_price in enumerate(grid_levels): + # Buy signal: price hits grid from above + if price <= grid_price + 0.1 and price >= grid_price - 0.1: + if i not in positions or positions[i] == 'sell': + # Open buy position + positions[i] = 'buy' + trades.append({ + 'type': 'buy', + 'price': grid_price, + 'time': len(trades) + }) + + # Sell signal: price hits grid from below + if price >= grid_price - 0.1 and price <= grid_price + 0.1: + if i in positions and positions[i] == 'buy': + # Close buy position (profit) + profit = (price - grid_price) * position_size + total_profit += profit + del positions[i] + trades.append({ + 'type': 'sell', + 'price': price, + 'profit': profit, + 'time': len(trades) + }) + + # Close remaining positions at final price + final_price = prices[-1] + for level, pos_type in positions.items(): + if pos_type == 'buy': + profit = (final_price - grid_levels[level]) * position_size + total_profit += profit + + return total_profit, trades + + def analyze_grid_trading(self, num_simulations=100, market_type='mean_reverting'): + """Analyze grid trading performance""" + results = [] + + for sim in range(num_simulations): + if market_type == 'mean_reverting': + prices = self.simulate_mean_reverting_price() + else: + prices = self.simulate_trending_price() + + grid_levels = self.create_grid() + profit, trades = self.calculate_grid_profits(prices, grid_levels) + + results.append({ + 'simulation': sim, + 'profit': profit, + 'num_trades': len([t for t in trades if t['type'] == 'sell']), + 'final_price': prices[-1], + 'price_range': prices.max() - prices.min(), + 'max_drawdown': self.calculate_max_drawdown(prices) + }) + + return pd.DataFrame(results) + + def calculate_max_drawdown(self, prices): + """Calculate maximum drawdown""" + peak = prices[0] + max_dd = 0 + + for price in prices: + if price > peak: + peak = price + dd = (peak - price) / peak + if dd > max_dd: + max_dd = dd + + return max_dd + + def optimize_grid_spacing(self, num_simulations=50, spacing_range=np.arange(0.5, 5.0, 0.5)): + """Find optimal grid spacing""" + results = [] + + for spacing in spacing_range: + self.grid_spacing = spacing + df = self.analyze_grid_trading(num_simulations=num_simulations, + market_type='mean_reverting') + + results.append({ + 'spacing': spacing, + 'mean_profit': df['profit'].mean(), + 'std_profit': df['profit'].std(), + 'sharpe_ratio': df['profit'].mean() / df['profit'].std() if df['profit'].std() > 0 else 0, + 'mean_trades': df['num_trades'].mean() + }) + + return pd.DataFrame(results) + + def plot_analysis(self, num_simulations=100): + """Plot analysis results""" + # Analyze in different market conditions + mean_reverting_results = self.analyze_grid_trading(num_simulations, 'mean_reverting') + trending_results = self.analyze_grid_trading(num_simulations, 'trending') + optimization_df = self.optimize_grid_spacing() + + fig, axes = plt.subplots(2, 2, figsize=(15, 10)) + + # Plot 1: Profit Distribution Comparison + axes[0, 0].hist(mean_reverting_results['profit'], bins=30, alpha=0.5, + label='Mean Reverting Market', color='green', edgecolor='black') + axes[0, 0].hist(trending_results['profit'], bins=30, alpha=0.5, + label='Trending Market', color='red', edgecolor='black') + axes[0, 0].axvline(0, color='black', linestyle='--', linewidth=2) + axes[0, 0].set_xlabel('Total Profit') + axes[0, 0].set_ylabel('Frequency') + axes[0, 0].set_title('Grid Trading Profit Distribution by Market Type') + axes[0, 0].legend() + axes[0, 0].grid(True, alpha=0.3) + + # Plot 2: Sample Price Path with Grid + sample_prices = self.simulate_mean_reverting_price() + grid_levels = self.create_grid() + + axes[0, 1].plot(sample_prices, 'b-', linewidth=2, label='Price') + for level in grid_levels: + axes[0, 1].axhline(level, color='gray', linestyle='--', alpha=0.3) + axes[0, 1].axhline(self.initial_price, color='red', linestyle='-', + linewidth=2, label='Initial Price') + axes[0, 1].set_xlabel('Time Step') + axes[0, 1].set_ylabel('Price') + axes[0, 1].set_title('Sample Price Path with Grid Levels') + axes[0, 1].legend() + axes[0, 1].grid(True, alpha=0.3) + + # Plot 3: Optimal Grid Spacing + axes[1, 0].plot(optimization_df['spacing'], optimization_df['sharpe_ratio'], + 'b-o', linewidth=2, markersize=8, label='Sharpe Ratio') + optimal_idx = optimization_df['sharpe_ratio'].idxmax() + optimal_spacing = optimization_df.loc[optimal_idx, 'spacing'] + axes[1, 0].axvline(optimal_spacing, color='red', linestyle='--', + label=f'Optimal: {optimal_spacing:.2f}') + axes[1, 0].set_xlabel('Grid Spacing') + axes[1, 0].set_ylabel('Sharpe Ratio') + axes[1, 0].set_title('Optimal Grid Spacing Analysis') + axes[1, 0].legend() + axes[1, 0].grid(True, alpha=0.3) + + # Plot 4: Profit vs Number of Trades + axes[1, 1].scatter(mean_reverting_results['num_trades'], + mean_reverting_results['profit'], + alpha=0.5, label='Mean Reverting', color='green') + axes[1, 1].scatter(trending_results['num_trades'], + trending_results['profit'], + alpha=0.5, label='Trending', color='red') + axes[1, 1].axhline(0, color='black', linestyle='--', linewidth=1) + axes[1, 1].set_xlabel('Number of Trades') + axes[1, 1].set_ylabel('Total Profit') + axes[1, 1].set_title('Profit vs Trade Frequency') + axes[1, 1].legend() + axes[1, 1].grid(True, alpha=0.3) + + plt.tight_layout() + return fig, mean_reverting_results, trending_results, optimization_df + +if __name__ == "__main__": + analyzer = GridTradingAnalyzer(initial_price=100, grid_spacing=1.0, num_levels=10) + + print("Running Grid Trading Analysis...") + fig, mr_results, tr_results, opt_df = analyzer.plot_analysis(num_simulations=100) + + print("\n=== Grid Trading Strategy Analysis ===") + print(f"\nMean Reverting Market:") + print(f" Mean Profit: ${mr_results['profit'].mean():.2f}") + print(f" Std Dev: ${mr_results['profit'].std():.2f}") + print(f" Win Rate: {(mr_results['profit'] > 0).mean():.2%}") + print(f" Mean Trades: {mr_results['num_trades'].mean():.1f}") + + print(f"\nTrending Market:") + print(f" Mean Profit: ${tr_results['profit'].mean():.2f}") + print(f" Std Dev: ${tr_results['profit'].std():.2f}") + print(f" Win Rate: {(tr_results['profit'] > 0).mean():.2%}") + print(f" Mean Trades: {tr_results['num_trades'].mean():.1f}") + + optimal_idx = opt_df['sharpe_ratio'].idxmax() + print(f"\nOptimal Grid Spacing: {opt_df.loc[optimal_idx, 'spacing']:.2f}") + print(f" Optimal Sharpe Ratio: {opt_df.loc[optimal_idx, 'sharpe_ratio']:.4f}") + + import os + # Get the script directory and construct path to figures + script_dir = os.path.dirname(os.path.abspath(__file__)) + figures_dir = os.path.join(script_dir, '..', 'figures') + figures_path = os.path.abspath(figures_dir) + os.makedirs(figures_path, exist_ok=True) + + output_path = os.path.join(figures_path, 'grid_trading_analysis.png') + plt.savefig(output_path, dpi=300, bbox_inches='tight') + print(f"\nFigure saved to {output_path}") + plt.close() diff --git a/paper/simulations/martingale_simulation.py b/paper/simulations/martingale_simulation.py new file mode 100644 index 0000000..5eb905f --- /dev/null +++ b/paper/simulations/martingale_simulation.py @@ -0,0 +1,212 @@ +""" +Martingale Strategy Simulation +Analyzes the statistical properties and risk of martingale strategies +""" +import numpy as np +import matplotlib +matplotlib.use('Agg') # Use non-interactive backend +import matplotlib.pyplot as plt +from scipy import stats +import pandas as pd + +class MartingaleSimulator: + def __init__(self, initial_balance=10000, base_lot=0.01, win_prob=0.5, + win_amount=10, loss_amount=10, max_losses=10): + self.initial_balance = initial_balance + self.base_lot = base_lot + self.win_prob = win_prob + self.win_amount = win_amount + self.loss_amount = loss_amount + self.max_losses = max_losses + + def calculate_position_size(self, consecutive_losses): + """Calculate position size after n consecutive losses""" + return self.base_lot * (2 ** consecutive_losses) + + def calculate_required_capital(self, consecutive_losses): + """Calculate total capital needed after n losses""" + return self.base_lot * (2 ** (consecutive_losses + 1) - 1) + + def simulate_trade_sequence(self, num_trades=1000): + """Simulate a sequence of trades""" + balance = self.initial_balance + consecutive_losses = 0 + trades = [] + ruin = False + + for i in range(num_trades): + if balance <= 0: + ruin = True + break + + # Calculate position size + position_size = self.calculate_position_size(consecutive_losses) + required_capital = self.calculate_required_capital(consecutive_losses) + + # Check if we have enough capital + if required_capital > balance: + ruin = True + break + + # Simulate trade outcome + is_win = np.random.random() < self.win_prob + + if is_win: + # Win: recover all previous losses + profit = position_size * self.win_amount + balance += profit + consecutive_losses = 0 + outcome = 'Win' + else: + # Loss: add to consecutive losses + loss = position_size * self.loss_amount + balance -= loss + consecutive_losses += 1 + outcome = 'Loss' + + trades.append({ + 'trade': i + 1, + 'balance': balance, + 'position_size': position_size, + 'consecutive_losses': consecutive_losses, + 'outcome': outcome, + 'profit': profit if is_win else -loss + }) + + return pd.DataFrame(trades), ruin + + def monte_carlo_analysis(self, num_simulations=1000, num_trades=100): + """Run Monte Carlo simulation""" + results = [] + ruin_count = 0 + + for sim in range(num_simulations): + trades_df, ruin = self.simulate_trade_sequence(num_trades) + if ruin: + ruin_count += 1 + final_balance = 0 + else: + final_balance = trades_df['balance'].iloc[-1] + + results.append({ + 'simulation': sim, + 'final_balance': final_balance, + 'ruin': ruin, + 'total_trades': len(trades_df), + 'max_consecutive_losses': trades_df['consecutive_losses'].max() if len(trades_df) > 0 else 0 + }) + + return pd.DataFrame(results), ruin_count / num_simulations + + def plot_simulation_results(self, num_simulations=100): + """Plot simulation results""" + fig, axes = plt.subplots(2, 2, figsize=(15, 10)) + + # Run simulations + results_df, ruin_prob = self.monte_carlo_analysis(num_simulations) + + # Plot 1: Final Balance Distribution + axes[0, 0].hist(results_df['final_balance'], bins=50, edgecolor='black') + axes[0, 0].axvline(self.initial_balance, color='red', linestyle='--', + label=f'Initial Balance: ${self.initial_balance:,.0f}') + axes[0, 0].set_xlabel('Final Balance ($)') + axes[0, 0].set_ylabel('Frequency') + axes[0, 0].set_title(f'Final Balance Distribution\nRuin Probability: {ruin_prob:.2%}') + axes[0, 0].legend() + axes[0, 0].grid(True, alpha=0.3) + + # Plot 2: Ruin Probability vs Consecutive Losses + max_losses_range = range(1, self.max_losses + 1) + ruin_probs = [] + for n in max_losses_range: + required = self.calculate_required_capital(n) + ruin_probs.append(1.0 if required > self.initial_balance else 0.0) + + axes[0, 1].plot(max_losses_range, ruin_probs, 'ro-', linewidth=2, markersize=8) + axes[0, 1].set_xlabel('Consecutive Losses') + axes[0, 1].set_ylabel('Ruin Probability') + axes[0, 1].set_title('Ruin Probability vs Consecutive Losses') + axes[0, 1].grid(True, alpha=0.3) + axes[0, 1].set_ylim([-0.1, 1.1]) + + # Plot 3: Position Size Growth + losses_range = range(0, self.max_losses + 1) + position_sizes = [self.calculate_position_size(n) for n in losses_range] + required_capital = [self.calculate_required_capital(n) for n in losses_range] + + ax3_twin = axes[1, 0].twinx() + line1 = axes[1, 0].plot(losses_range, position_sizes, 'b-o', + label='Position Size', linewidth=2) + line2 = ax3_twin.plot(losses_range, required_capital, 'r-s', + label='Required Capital', linewidth=2) + + axes[1, 0].set_xlabel('Consecutive Losses') + axes[1, 0].set_ylabel('Position Size (Lots)', color='b') + ax3_twin.set_ylabel('Required Capital ($)', color='r') + axes[1, 0].set_title('Position Size and Capital Requirements') + axes[1, 0].grid(True, alpha=0.3) + + # Combine legends + lines = line1 + line2 + labels = [l.get_label() for l in lines] + axes[1, 0].legend(lines, labels, loc='upper left') + + # Plot 4: Sample Trade Sequence + sample_trades, _ = self.simulate_trade_sequence(50) + axes[1, 1].plot(sample_trades['trade'], sample_trades['balance'], + 'g-', linewidth=2, label='Balance') + axes[1, 1].axhline(self.initial_balance, color='red', linestyle='--', + label='Initial Balance') + axes[1, 1].set_xlabel('Trade Number') + axes[1, 1].set_ylabel('Balance ($)') + axes[1, 1].set_title('Sample Trade Sequence (50 trades)') + axes[1, 1].legend() + axes[1, 1].grid(True, alpha=0.3) + + plt.tight_layout() + return fig + +if __name__ == "__main__": + # Create simulator + simulator = MartingaleSimulator( + initial_balance=10000, + base_lot=0.01, + win_prob=0.5, + win_amount=10, + loss_amount=10, + max_losses=10 + ) + + # Run analysis + print("Running Martingale Simulation...") + results_df, ruin_prob = simulator.monte_carlo_analysis(num_simulations=1000, num_trades=100) + + print(f"\n=== Martingale Strategy Analysis ===") + print(f"Initial Balance: ${simulator.initial_balance:,.2f}") + print(f"Win Probability: {simulator.win_prob:.1%}") + print(f"\nMonte Carlo Results (1000 simulations):") + print(f"Ruin Probability: {ruin_prob:.2%}") + print(f"Mean Final Balance: ${results_df['final_balance'].mean():,.2f}") + print(f"Median Final Balance: ${results_df['final_balance'].median():,.2f}") + print(f"Std Dev Final Balance: ${results_df['final_balance'].std():,.2f}") + print(f"Max Final Balance: ${results_df['final_balance'].max():,.2f}") + print(f"Min Final Balance: ${results_df['final_balance'].min():,.2f}") + + # Calculate statistics + profitable_sims = (results_df['final_balance'] > simulator.initial_balance).sum() + print(f"\nProfitable Simulations: {profitable_sims}/{len(results_df)} ({profitable_sims/len(results_df):.1%})") + print(f"Average Max Consecutive Losses: {results_df['max_consecutive_losses'].mean():.2f}") + + # Generate plots + import os + # Get the script directory and construct path to figures + script_dir = os.path.dirname(os.path.abspath(__file__)) + figures_dir = os.path.join(script_dir, '..', 'figures') + figures_path = os.path.abspath(figures_dir) + os.makedirs(figures_path, exist_ok=True) + + fig = simulator.plot_simulation_results(num_simulations=100) + output_path = os.path.join(figures_path, 'martingale_analysis.png') + plt.savefig(output_path, dpi=300, bbox_inches='tight') + print(f"\nFigure saved to {output_path}") + plt.close() diff --git a/paper/simulations/partial_exit_analysis.py b/paper/simulations/partial_exit_analysis.py new file mode 100644 index 0000000..7b73b27 --- /dev/null +++ b/paper/simulations/partial_exit_analysis.py @@ -0,0 +1,191 @@ +""" +Partial Exit Strategy Analysis +Analyzes the statistical benefits of partial exits +""" +import numpy as np +import matplotlib +matplotlib.use('Agg') # Use non-interactive backend +import matplotlib.pyplot as plt +import pandas as pd +from scipy.stats import norm + +class PartialExitAnalyzer: + def __init__(self, initial_price=100, drift=0.0001, volatility=0.02): + self.initial_price = initial_price + self.drift = drift + self.volatility = volatility + + def simulate_price_path(self, num_steps=1000, dt=1/252): + """Simulate price using geometric Brownian motion""" + prices = [self.initial_price] + + for _ in range(num_steps): + dW = np.random.normal(0, np.sqrt(dt)) + dS = self.drift * prices[-1] * dt + self.volatility * prices[-1] * dW + prices.append(prices[-1] + dS) + + return np.array(prices) + + def calculate_full_exit_return(self, prices, exit_time): + """Calculate return for full exit at exit_time""" + exit_price = prices[exit_time] + return (exit_price - self.initial_price) / self.initial_price + + def calculate_partial_exit_return(self, prices, partial_exit_time, + partial_exit_pct, final_exit_time): + """Calculate return for partial exit strategy""" + partial_exit_price = prices[partial_exit_time] + final_exit_price = prices[final_exit_time] + + # Partial exit profit + partial_profit = partial_exit_pct * (partial_exit_price - self.initial_price) / self.initial_price + + # Remaining position profit + remaining_profit = (1 - partial_exit_pct) * (final_exit_price - self.initial_price) / self.initial_price + + total_return = partial_profit + remaining_profit + return total_return, partial_profit, remaining_profit + + def analyze_partial_exit(self, num_simulations=1000, num_steps=1000, + partial_exit_pct=0.5, partial_exit_time=500): + """Analyze partial exit strategy""" + results = [] + + for sim in range(num_simulations): + prices = self.simulate_price_path(num_steps) + + # Full exit at end + full_return = self.calculate_full_exit_return(prices, len(prices) - 1) + + # Partial exit strategy + partial_return, partial_profit, remaining_profit = self.calculate_partial_exit_return( + prices, partial_exit_time, partial_exit_pct, len(prices) - 1) + + results.append({ + 'simulation': sim, + 'final_price': prices[-1], + 'partial_exit_price': prices[partial_exit_time], + 'full_return': full_return, + 'partial_return': partial_return, + 'partial_profit': partial_profit, + 'remaining_profit': remaining_profit, + 'variance_reduction': np.var([partial_profit, remaining_profit]) - np.var([full_return]) + }) + + return pd.DataFrame(results) + + def optimize_exit_percentage(self, num_simulations=500, exit_percentages=np.arange(0.1, 0.9, 0.1)): + """Find optimal partial exit percentage""" + results = [] + + for exit_pct in exit_percentages: + df = self.analyze_partial_exit(num_simulations=num_simulations, + partial_exit_pct=exit_pct) + + mean_return = df['partial_return'].mean() + std_return = df['partial_return'].std() + sharpe = mean_return / std_return if std_return > 0 else 0 + + results.append({ + 'exit_percentage': exit_pct, + 'mean_return': mean_return, + 'std_return': std_return, + 'sharpe_ratio': sharpe, + 'variance_reduction': df['variance_reduction'].mean() + }) + + return pd.DataFrame(results) + + def plot_analysis(self, num_simulations=1000): + """Plot analysis results""" + results_df = self.analyze_partial_exit(num_simulations) + optimization_df = self.optimize_exit_percentage() + + fig, axes = plt.subplots(2, 2, figsize=(15, 10)) + + # Plot 1: Return Distribution Comparison + axes[0, 0].hist(results_df['full_return'], bins=50, alpha=0.5, + label='Full Exit', color='red', edgecolor='black') + axes[0, 0].hist(results_df['partial_return'], bins=50, alpha=0.5, + label='Partial Exit (50%)', color='green', edgecolor='black') + axes[0, 0].axvline(0, color='black', linestyle='--', linewidth=1) + axes[0, 0].set_xlabel('Return') + axes[0, 0].set_ylabel('Frequency') + axes[0, 0].set_title('Return Distribution: Full vs Partial Exit') + axes[0, 0].legend() + axes[0, 0].grid(True, alpha=0.3) + + # Plot 2: Variance Reduction + axes[0, 1].hist(results_df['variance_reduction'], bins=50, color='blue', + edgecolor='black', alpha=0.7) + axes[0, 1].axvline(0, color='red', linestyle='--', linewidth=2) + axes[0, 1].axvline(results_df['variance_reduction'].mean(), color='green', + linestyle='--', linewidth=2, + label=f'Mean: {results_df["variance_reduction"].mean():.6f}') + axes[0, 1].set_xlabel('Variance Reduction') + axes[0, 1].set_ylabel('Frequency') + axes[0, 1].set_title('Variance Reduction from Partial Exit') + axes[0, 1].legend() + axes[0, 1].grid(True, alpha=0.3) + + # Plot 3: Optimal Exit Percentage + axes[1, 0].plot(optimization_df['exit_percentage'], + optimization_df['sharpe_ratio'], + 'b-o', linewidth=2, markersize=8) + optimal_idx = optimization_df['sharpe_ratio'].idxmax() + optimal_pct = optimization_df.loc[optimal_idx, 'exit_percentage'] + optimal_sharpe = optimization_df.loc[optimal_idx, 'sharpe_ratio'] + axes[1, 0].axvline(optimal_pct, color='red', linestyle='--', + label=f'Optimal: {optimal_pct:.1%}') + axes[1, 0].set_xlabel('Partial Exit Percentage') + axes[1, 0].set_ylabel('Sharpe Ratio') + axes[1, 0].set_title('Sharpe Ratio vs Exit Percentage') + axes[1, 0].legend() + axes[1, 0].grid(True, alpha=0.3) + + # Plot 4: Variance Reduction vs Exit Percentage + axes[1, 1].plot(optimization_df['exit_percentage'], + optimization_df['variance_reduction'], + 'g-s', linewidth=2, markersize=8) + axes[1, 1].axhline(0, color='red', linestyle='--', linewidth=1) + axes[1, 1].set_xlabel('Partial Exit Percentage') + axes[1, 1].set_ylabel('Variance Reduction') + axes[1, 1].set_title('Variance Reduction vs Exit Percentage') + axes[1, 1].grid(True, alpha=0.3) + + plt.tight_layout() + return fig, results_df, optimization_df + +if __name__ == "__main__": + analyzer = PartialExitAnalyzer() + + print("Running Partial Exit Analysis...") + fig, results_df, optimization_df = analyzer.plot_analysis(num_simulations=1000) + + print("\n=== Partial Exit Strategy Analysis ===") + print(f"\nFull Exit Results:") + print(f" Mean Return: {results_df['full_return'].mean():.4f}") + print(f" Std Dev: {results_df['full_return'].std():.4f}") + print(f" Sharpe Ratio: {results_df['full_return'].mean() / results_df['full_return'].std():.4f}") + + print(f"\nPartial Exit Results (50% exit):") + print(f" Mean Return: {results_df['partial_return'].mean():.4f}") + print(f" Std Dev: {results_df['partial_return'].std():.4f}") + print(f" Sharpe Ratio: {results_df['partial_return'].mean() / results_df['partial_return'].std():.4f}") + print(f" Mean Variance Reduction: {results_df['variance_reduction'].mean():.6f}") + + optimal_idx = optimization_df['sharpe_ratio'].idxmax() + print(f"\nOptimal Exit Percentage: {optimization_df.loc[optimal_idx, 'exit_percentage']:.1%}") + print(f" Optimal Sharpe Ratio: {optimization_df.loc[optimal_idx, 'sharpe_ratio']:.4f}") + + import os + # Get the script directory and construct path to figures + script_dir = os.path.dirname(os.path.abspath(__file__)) + figures_dir = os.path.join(script_dir, '..', 'figures') + figures_path = os.path.abspath(figures_dir) + os.makedirs(figures_path, exist_ok=True) + + output_path = os.path.join(figures_path, 'partial_exit_analysis.png') + plt.savefig(output_path, dpi=300, bbox_inches='tight') + print(f"\nFigure saved to {output_path}") + plt.close() diff --git a/paper/simulations/requirements.txt b/paper/simulations/requirements.txt new file mode 100644 index 0000000..136d795 --- /dev/null +++ b/paper/simulations/requirements.txt @@ -0,0 +1,4 @@ +numpy>=1.21.0 +matplotlib>=3.4.0 +pandas>=1.3.0 +scipy>=1.7.0 diff --git a/paper/simulations/trailing_stop_analysis.py b/paper/simulations/trailing_stop_analysis.py new file mode 100644 index 0000000..fa4afb6 --- /dev/null +++ b/paper/simulations/trailing_stop_analysis.py @@ -0,0 +1,230 @@ +""" +Trailing Stop Loss Analysis +Compares fixed stop loss vs trailing stop loss performance +""" +import numpy as np +import matplotlib +matplotlib.use('Agg') # Use non-interactive backend +import matplotlib.pyplot as plt +import pandas as pd +from scipy.stats import norm + +class TrailingStopAnalyzer: + def __init__(self, initial_price=100, drift=0.0001, volatility=0.02, + trailing_distance=0.02, fixed_stop_distance=0.02): + self.initial_price = initial_price + self.drift = drift + self.volatility = volatility + self.trailing_distance = trailing_distance + self.fixed_stop_distance = fixed_stop_distance + + def simulate_price_path(self, num_steps=1000, dt=1/252): + """Simulate price using geometric Brownian motion""" + prices = [self.initial_price] + + for _ in range(num_steps): + dW = np.random.normal(0, np.sqrt(dt)) + dS = self.drift * prices[-1] * dt + self.volatility * prices[-1] * dW + prices.append(prices[-1] + dS) + + return np.array(prices) + + def apply_fixed_stop(self, prices, stop_distance): + """Apply fixed stop loss""" + stop_price = self.initial_price - stop_distance * self.initial_price + exit_idx = None + + for i, price in enumerate(prices): + if price <= stop_price: + exit_idx = i + break + + if exit_idx is None: + exit_price = prices[-1] + exit_idx = len(prices) - 1 + else: + exit_price = stop_price + + return exit_idx, exit_price + + def apply_trailing_stop(self, prices, trailing_distance): + """Apply trailing stop loss""" + stop_price = self.initial_price - trailing_distance * self.initial_price + exit_idx = None + + for i, price in enumerate(prices): + # Update trailing stop (only moves up for long positions) + new_stop = price - trailing_distance * price + if new_stop > stop_price: + stop_price = new_stop + + # Check if stop is hit + if price <= stop_price: + exit_idx = i + break + + if exit_idx is None: + exit_price = prices[-1] + exit_idx = len(prices) - 1 + else: + exit_price = stop_price + + return exit_idx, exit_price, stop_price + + def compare_strategies(self, num_simulations=1000, num_steps=1000): + """Compare fixed vs trailing stop""" + results = [] + + for sim in range(num_simulations): + prices = self.simulate_price_path(num_steps) + + # Fixed stop + fixed_exit_idx, fixed_exit_price = self.apply_fixed_stop( + prices, self.fixed_stop_distance) + fixed_return = (fixed_exit_price - self.initial_price) / self.initial_price + + # Trailing stop + trailing_exit_idx, trailing_exit_price, final_stop = self.apply_trailing_stop( + prices, self.trailing_distance) + trailing_return = (trailing_exit_price - self.initial_price) / self.initial_price + + results.append({ + 'simulation': sim, + 'final_price': prices[-1], + 'fixed_return': fixed_return, + 'trailing_return': trailing_return, + 'fixed_exit_time': fixed_exit_idx, + 'trailing_exit_time': trailing_exit_idx, + 'improvement': trailing_return - fixed_return + }) + + return pd.DataFrame(results) + + def plot_comparison(self, num_simulations=1000): + """Plot comparison results""" + results_df = self.compare_strategies(num_simulations) + + fig, axes = plt.subplots(2, 2, figsize=(15, 10)) + + # Plot 1: Return Distribution Comparison + axes[0, 0].hist(results_df['fixed_return'], bins=50, alpha=0.5, + label='Fixed Stop', color='red', edgecolor='black') + axes[0, 0].hist(results_df['trailing_return'], bins=50, alpha=0.5, + label='Trailing Stop', color='green', edgecolor='black') + axes[0, 0].axvline(0, color='black', linestyle='--', linewidth=1) + axes[0, 0].set_xlabel('Return') + axes[0, 0].set_ylabel('Frequency') + axes[0, 0].set_title('Return Distribution Comparison') + axes[0, 0].legend() + axes[0, 0].grid(True, alpha=0.3) + + # Plot 2: Improvement Distribution + axes[0, 1].hist(results_df['improvement'], bins=50, color='blue', + edgecolor='black', alpha=0.7) + axes[0, 1].axvline(0, color='red', linestyle='--', linewidth=2, + label='No Improvement') + axes[0, 1].axvline(results_df['improvement'].mean(), color='green', + linestyle='--', linewidth=2, + label=f'Mean: {results_df["improvement"].mean():.4f}') + axes[0, 1].set_xlabel('Improvement (Trailing - Fixed)') + axes[0, 1].set_ylabel('Frequency') + axes[0, 1].set_title('Trailing Stop Improvement Distribution') + axes[0, 1].legend() + axes[0, 1].grid(True, alpha=0.3) + + # Plot 3: Sample Price Path with Stops + sample_prices = self.simulate_price_path(500) + _, fixed_exit = self.apply_fixed_stop(sample_prices, self.fixed_stop_distance) + trailing_stops = [] + current_stop = self.initial_price - self.trailing_distance * self.initial_price + + for price in sample_prices: + new_stop = price - self.trailing_distance * price + if new_stop > current_stop: + current_stop = new_stop + trailing_stops.append(current_stop) + + axes[1, 0].plot(sample_prices, 'b-', label='Price', linewidth=2) + axes[1, 0].axhline(self.initial_price - self.fixed_stop_distance * self.initial_price, + color='red', linestyle='--', label='Fixed Stop', linewidth=2) + axes[1, 0].plot(trailing_stops, 'g--', label='Trailing Stop', linewidth=2) + axes[1, 0].set_xlabel('Time Step') + axes[1, 0].set_ylabel('Price') + axes[1, 0].set_title('Sample Price Path with Stop Losses') + axes[1, 0].legend() + axes[1, 0].grid(True, alpha=0.3) + + # Plot 4: Performance Metrics Comparison + metrics = ['Mean Return', 'Std Dev', 'Sharpe Ratio', 'Win Rate', 'Max Return'] + fixed_vals = [ + results_df['fixed_return'].mean(), + results_df['fixed_return'].std(), + results_df['fixed_return'].mean() / results_df['fixed_return'].std() if results_df['fixed_return'].std() > 0 else 0, + (results_df['fixed_return'] > 0).mean(), + results_df['fixed_return'].max() + ] + trailing_vals = [ + results_df['trailing_return'].mean(), + results_df['trailing_return'].std(), + results_df['trailing_return'].mean() / results_df['trailing_return'].std() if results_df['trailing_return'].std() > 0 else 0, + (results_df['trailing_return'] > 0).mean(), + results_df['trailing_return'].max() + ] + + x = np.arange(len(metrics)) + width = 0.35 + axes[1, 1].bar(x - width/2, fixed_vals, width, label='Fixed Stop', color='red', alpha=0.7) + axes[1, 1].bar(x + width/2, trailing_vals, width, label='Trailing Stop', color='green', alpha=0.7) + axes[1, 1].set_xlabel('Metric') + axes[1, 1].set_ylabel('Value') + axes[1, 1].set_title('Performance Metrics Comparison') + axes[1, 1].set_xticks(x) + axes[1, 1].set_xticklabels(metrics, rotation=45, ha='right') + axes[1, 1].legend() + axes[1, 1].grid(True, alpha=0.3, axis='y') + + plt.tight_layout() + return fig, results_df + +if __name__ == "__main__": + # Create analyzer + analyzer = TrailingStopAnalyzer( + initial_price=100, + drift=0.0001, + volatility=0.02, + trailing_distance=0.02, + fixed_stop_distance=0.02 + ) + + print("Running Trailing Stop Analysis...") + fig, results_df = analyzer.plot_comparison(num_simulations=1000) + + print("\n=== Trailing Stop vs Fixed Stop Analysis ===") + print(f"\nFixed Stop Results:") + print(f" Mean Return: {results_df['fixed_return'].mean():.4f}") + print(f" Std Dev: {results_df['fixed_return'].std():.4f}") + print(f" Sharpe Ratio: {results_df['fixed_return'].mean() / results_df['fixed_return'].std():.4f}") + print(f" Win Rate: {(results_df['fixed_return'] > 0).mean():.2%}") + + print(f"\nTrailing Stop Results:") + print(f" Mean Return: {results_df['trailing_return'].mean():.4f}") + print(f" Std Dev: {results_df['trailing_return'].std():.4f}") + print(f" Sharpe Ratio: {results_df['trailing_return'].mean() / results_df['trailing_return'].std():.4f}") + print(f" Win Rate: {(results_df['trailing_return'] > 0).mean():.2%}") + + print(f"\nImprovement:") + improvement = results_df['trailing_return'].mean() - results_df['fixed_return'].mean() + print(f" Mean Improvement: {improvement:.4f} ({improvement/results_df['fixed_return'].mean()*100:.1f}%)") + print(f" Improvement Frequency: {(results_df['improvement'] > 0).mean():.2%}") + + import os + # Get the script directory and construct path to figures + script_dir = os.path.dirname(os.path.abspath(__file__)) + figures_dir = os.path.join(script_dir, '..', 'figures') + figures_path = os.path.abspath(figures_dir) + os.makedirs(figures_path, exist_ok=True) + + output_path = os.path.join(figures_path, 'trailing_stop_analysis.png') + plt.savefig(output_path, dpi=300, bbox_inches='tight') + print(f"\nFigure saved to {output_path}") + plt.close()