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zhutoutoutousan
2026-04-01 05:40:17 +02:00
parent 28e7daf1e3
commit 842a2f8fac
53 changed files with 9222 additions and 962 deletions
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ai/dummy/ONNX_EA.mq5
+231 -62
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@@ -11,9 +11,14 @@
#include <Trade\Trade.mqh>
//--- Resource: Embed ONNX model in EA
// Based on: https://www.mql5.com/en/docs/onnx/onnx_test
// Path is relative to MQL5 directory (not starting with \\Files\\)
#resource "Files\\XAUUSD_H1_model.onnx" as uchar ExtModel[]
//--- Input parameters
input group "ONNX Model Settings"
input string InpModelPath = "models\\XAUUSD_H1_model.onnx"; // ONNX Model Path
input string InpModelPath = ""; // ONNX Model Path (leave empty to use embedded resource)
input int InpLookback = 60; // Lookback Period (bars)
input bool InpUsePrediction = true; // Use Model Prediction
@@ -21,8 +26,13 @@ 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 bool InpUsePredictedSLTP = true; // Use Predicted SL/TP (based on prediction & volatility)
input int InpStopLoss = 50; // Stop Loss (pips) - used if InpUsePredictedSLTP=false
input int InpTakeProfit = 100; // Take Profit (pips) - used if InpUsePredictedSLTP=false
input double InpSLMultiplier = 1.5; // SL Multiplier (ATR-based, e.g., 1.5 = 1.5x ATR)
input double InpTPMultiplier = 2.0; // TP Multiplier (ATR-based, e.g., 2.0 = 2x ATR)
input double InpMinSLATR = 0.5; // Minimum SL (ATR multiplier)
input double InpMinTPATR = 1.0; // Minimum TP (ATR multiplier)
input group "Prediction Settings"
input double InpPredictionThreshold = 0.00005; // Min Prediction Change (0.005% as decimal, e.g., 0.00005 = 0.005%)
@@ -47,33 +57,40 @@ int OnInit()
trade.SetTypeFilling(ORDER_FILLING_FOK);
// Load ONNX model
string model_path = InpModelPath;
// Based on: https://www.mql5.com/en/docs/onnx/onnx_test
Print("Loading ONNX model from embedded resource...");
// 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);
// Create model from resource buffer
onnx_handle = OnnxCreateFromBuffer(ExtModel, ONNX_DEBUG_LOGS);
if(onnx_handle == INVALID_HANDLE)
{
Print("ERROR: Failed to load ONNX model. Error: ", GetLastError());
Print("Make sure the model file exists at: ", model_path);
int error = GetLastError();
Print("ERROR: Failed to create ONNX model from resource. Error: ", error);
Print("Make sure the model file exists at: MQL5\\Files\\XAUUSD_H1_model.onnx");
Print("Then recompile the EA to embed it as a resource.");
return(INIT_FAILED);
}
// Set input shape - per MQL5 documentation
const long ExtInputShape[] = {1, InpLookback, 13}; // batch=1, lookback bars, 13 features
if(!OnnxSetInputShape(onnx_handle, 0, ExtInputShape))
{
Print("OnnxSetInputShape failed, error ", GetLastError());
OnnxRelease(onnx_handle);
return(INIT_FAILED);
}
// Set output shape - per MQL5 documentation
const long ExtOutputShape[] = {1, 1}; // batch=1, single output value
if(!OnnxSetOutputShape(onnx_handle, 0, ExtOutputShape))
{
Print("OnnxSetOutputShape failed, error ", GetLastError());
OnnxRelease(onnx_handle);
return(INIT_FAILED);
}
// Get model info
long input_count = OnnxGetInputCount(onnx_handle);
long output_count = OnnxGetOutputCount(onnx_handle);
@@ -137,23 +154,59 @@ void OnTick()
return;
}
// Run ONNX model
float output_data[];
if(!RunONNXModel(input_data, output_data))
// Check if input data is valid
if(ArraySize(input_data) != InpLookback * 13)
{
Print("ERROR: Input data size mismatch. Expected: ", InpLookback * 13, ", Got: ", ArraySize(input_data));
return;
}
// Convert flat array to matrixf for OnnxRun
// Shape: [lookback, features] = [60, 13] - batch dimension is added automatically
matrixf input_matrix;
input_matrix.Resize(InpLookback, 13);
// Fill matrix from flat array
int idx = 0;
for(int i = 0; i < InpLookback; i++)
{
for(int j = 0; j < 13; j++)
{
if(idx >= ArraySize(input_data))
{
Print("ERROR: Index out of bounds when filling matrix. idx=", idx, ", array size=", ArraySize(input_data));
return;
}
input_matrix[i][j] = input_data[idx++];
}
}
// Verify matrix is not empty
if(input_matrix.Rows() == 0 || input_matrix.Cols() == 0)
{
Print("ERROR: Input matrix is empty. Rows: ", input_matrix.Rows(), ", Cols: ", input_matrix.Cols());
return;
}
Print("Input matrix prepared: Rows=", input_matrix.Rows(), ", Cols=", input_matrix.Cols());
// Run ONNX model - use matrixf and vectorf per MQL5 documentation
vectorf output_vector(1);
if(!RunONNXModel(input_matrix, output_vector))
{
Print("ERROR: Failed to run ONNX model");
return;
}
// Get prediction
if(ArraySize(output_data) == 0)
if(output_vector.Size() == 0)
{
Print("ERROR: Empty output from ONNX model");
return;
}
// Model now predicts price change percentage directly (e.g., -0.003 = -0.3%)
double predicted_change_pct = output_data[0];
double predicted_change_pct = output_vector[0];
double current_price = SymbolInfoDouble(_Symbol, SYMBOL_BID);
// Check if prediction is percentage (between -1 and 1) or absolute price (old format)
@@ -190,20 +243,85 @@ void OnTick()
last_prediction = predicted_price;
last_confidence = confidence;
// Calculate ATR for dynamic SL/TP
double atr_value = 0.0;
double atr_array[];
ArraySetAsSeries(atr_array, true);
int atr_handle = iATR(_Symbol, PERIOD_CURRENT, 14);
if(atr_handle != INVALID_HANDLE)
{
if(CopyBuffer(atr_handle, 0, 0, 1, atr_array) > 0)
{
atr_value = atr_array[0];
}
IndicatorRelease(atr_handle);
}
// Calculate predicted SL/TP based on prediction, confidence, and volatility
double predicted_sl = 0.0;
double predicted_tp = 0.0;
if(InpUsePredictedSLTP && atr_value > 0)
{
// Calculate SL/TP based on ATR, prediction, and confidence
double predicted_move = MathAbs(predicted_price - current_price);
// SL: Based on ATR and confidence
// Higher confidence = tighter SL, lower confidence = wider SL
double sl_atr_mult = InpSLMultiplier / MathMax(confidence, 0.1);
sl_atr_mult = MathMax(sl_atr_mult, InpMinSLATR);
predicted_sl = atr_value * sl_atr_mult;
// TP: Use a fraction of predicted move (not the full move)
// Take 30-50% of predicted move as TP, but ensure minimum
double tp_fraction = 0.3 + (confidence * 0.2); // 30-50% based on confidence
double tp_from_prediction = predicted_move * tp_fraction;
// Also calculate TP from ATR multiplier
double tp_from_atr = atr_value * InpTPMultiplier;
// Use the smaller of the two (more conservative)
predicted_tp = MathMin(tp_from_prediction, tp_from_atr);
predicted_tp = MathMax(predicted_tp, atr_value * InpMinTPATR); // Minimum TP
// Ensure TP is at least 1.5x SL for risk/reward
if(predicted_tp < predicted_sl * 1.5)
{
predicted_tp = predicted_sl * 1.5;
}
// Cap TP at maximum 80% of predicted move (don't be too greedy)
double max_tp = predicted_move * 0.8;
if(predicted_tp > max_tp)
{
predicted_tp = max_tp;
}
}
// Log prediction
Print("Prediction: Current=", current_price,
" Predicted Change=", price_change_pct, "%",
" Predicted Price=", predicted_price,
" Confidence=", confidence);
" Confidence=", confidence,
" ATR=", atr_value);
if(InpUsePredictedSLTP && predicted_sl > 0 && predicted_tp > 0)
{
Print(" Predicted SL=", predicted_sl, " (", predicted_sl/current_price*100, "%)",
" Predicted TP=", predicted_tp, " (", predicted_tp/current_price*100, "%)");
}
// Check if we should trade
if(!InpUseConfidence || confidence >= InpMinConfidence)
{
// Check if prediction is significant
// price_change_pct is now in percentage (e.g., 0.1 = 0.1%), so compare with threshold * 100
// OR: if model outputs decimal (0.001), compare directly
double abs_change_decimal = MathAbs(predicted_change_pct); // Use raw prediction for threshold check
if(abs_change_decimal >= InpPredictionThreshold)
// price_change_pct is in percentage (e.g., 5.72 = 5.72%)
// InpPredictionThreshold is in decimal (e.g., 0.00005 = 0.005%)
// Convert threshold to percentage for comparison
double threshold_pct = InpPredictionThreshold * 100.0;
double abs_change_pct = MathAbs(price_change_pct); // Already in percentage
Print("Trade Check: Change=", price_change_pct, "% Threshold=", threshold_pct, "% Confidence=", confidence);
if(abs_change_pct >= threshold_pct)
{
// Check existing position
if(PositionSelect(_Symbol))
@@ -214,17 +332,26 @@ void OnTick()
else
{
// Open new position based on prediction
// Use raw prediction (decimal format) for threshold comparison
if(predicted_change_pct > InpPredictionThreshold)
if(price_change_pct > threshold_pct)
{
OpenBuyPosition();
Print(">>> Opening BUY position: Change=", price_change_pct, "% Threshold=", threshold_pct, "%");
OpenBuyPosition(predicted_sl, predicted_tp);
}
else if(predicted_change_pct < -InpPredictionThreshold)
else if(price_change_pct < -threshold_pct)
{
OpenSellPosition();
Print(">>> Opening SELL position: Change=", price_change_pct, "% Threshold=", threshold_pct, "%");
OpenSellPosition(predicted_sl, predicted_tp);
}
}
}
else
{
Print("Prediction below threshold: Change=", price_change_pct, "% < Threshold=", threshold_pct, "%");
}
}
else
{
Print("Confidence too low: ", confidence, " < ", InpMinConfidence);
}
}
@@ -251,16 +378,36 @@ bool PrepareInputData(float &input_array[])
ArraySetAsSeries(close, true);
ArraySetAsSeries(volume, true);
if(CopyOpen(_Symbol, PERIOD_CURRENT, 0, lookback + 50, open) < lookback)
int copied_open = CopyOpen(_Symbol, PERIOD_CURRENT, 0, lookback + 50, open);
if(copied_open < lookback)
{
Print("ERROR: CopyOpen failed. Got ", copied_open, " bars, need ", lookback);
return false;
if(CopyHigh(_Symbol, PERIOD_CURRENT, 0, lookback + 50, high) < lookback)
}
int copied_high = CopyHigh(_Symbol, PERIOD_CURRENT, 0, lookback + 50, high);
if(copied_high < lookback)
{
Print("ERROR: CopyHigh failed. Got ", copied_high, " bars, need ", lookback);
return false;
if(CopyLow(_Symbol, PERIOD_CURRENT, 0, lookback + 50, low) < lookback)
}
int copied_low = CopyLow(_Symbol, PERIOD_CURRENT, 0, lookback + 50, low);
if(copied_low < lookback)
{
Print("ERROR: CopyLow failed. Got ", copied_low, " bars, need ", lookback);
return false;
if(CopyClose(_Symbol, PERIOD_CURRENT, 0, lookback + 50, close) < lookback)
}
int copied_close = CopyClose(_Symbol, PERIOD_CURRENT, 0, lookback + 50, close);
if(copied_close < lookback)
{
Print("ERROR: CopyClose failed. Got ", copied_close, " bars, need ", lookback);
return false;
if(CopyTickVolume(_Symbol, PERIOD_CURRENT, 0, lookback + 50, volume) < lookback)
}
int copied_volume = CopyTickVolume(_Symbol, PERIOD_CURRENT, 0, lookback + 50, volume);
if(copied_volume < lookback)
{
Print("ERROR: CopyTickVolume failed. Got ", copied_volume, " bars, need ", lookback);
return false;
}
// Calculate indicators (simplified - you may need to match training exactly)
double rsi[], ema20[], ema50[], atr[];
@@ -379,21 +526,15 @@ bool PrepareInputData(float &input_array[])
//+------------------------------------------------------------------+
//| Run ONNX model |
//+------------------------------------------------------------------+
bool RunONNXModel(float &input_data[], float &output_data[])
bool RunONNXModel(matrixf &input_matrix, vectorf &output_vector)
{
if(onnx_handle == INVALID_HANDLE)
return false;
// Set input shape
long input_shape[] = {1, InpLookback, 13}; // 13 features
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))
// Run model - shapes are already set in OnInit per MQL5 documentation
// Based on: https://www.mql5.com/en/docs/onnx/onnx_test
// OnnxRun expects matrixf and vectorf, not flat arrays
if(!OnnxRun(onnx_handle, ONNX_DEBUG_LOGS | ONNX_NO_CONVERSION, input_matrix, output_vector))
{
Print("ERROR: Failed to run ONNX model. Error: ", GetLastError());
return false;
@@ -405,15 +546,29 @@ bool RunONNXModel(float &input_data[], float &output_data[])
//+------------------------------------------------------------------+
//| Open buy position |
//+------------------------------------------------------------------+
void OpenBuyPosition()
void OpenBuyPosition(double predicted_sl = 0.0, double predicted_tp = 0.0)
{
double price = SymbolInfoDouble(_Symbol, SYMBOL_ASK);
double sl = InpStopLoss > 0 ? price - InpStopLoss * _Point * 10 : 0;
double tp = InpTakeProfit > 0 ? price + InpTakeProfit * _Point * 10 : 0;
double sl = 0.0;
double tp = 0.0;
if(InpUsePredictedSLTP && predicted_sl > 0 && predicted_tp > 0)
{
// Use predicted SL/TP
sl = price - predicted_sl;
tp = price + predicted_tp;
Print("Using predicted SL/TP: SL=", sl, " TP=", tp);
}
else
{
// Use fixed SL/TP from input parameters
sl = InpStopLoss > 0 ? price - InpStopLoss * _Point * 10 : 0;
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());
Print("Buy order opened. Ticket: ", trade.ResultOrder(), " Price: ", price, " SL: ", sl, " TP: ", tp);
}
else
{
@@ -424,15 +579,29 @@ void OpenBuyPosition()
//+------------------------------------------------------------------+
//| Open sell position |
//+------------------------------------------------------------------+
void OpenSellPosition()
void OpenSellPosition(double predicted_sl = 0.0, double predicted_tp = 0.0)
{
double price = SymbolInfoDouble(_Symbol, SYMBOL_BID);
double sl = InpStopLoss > 0 ? price + InpStopLoss * _Point * 10 : 0;
double tp = InpTakeProfit > 0 ? price - InpTakeProfit * _Point * 10 : 0;
double sl = 0.0;
double tp = 0.0;
if(InpUsePredictedSLTP && predicted_sl > 0 && predicted_tp > 0)
{
// Use predicted SL/TP
sl = price + predicted_sl;
tp = price - predicted_tp;
Print("Using predicted SL/TP: SL=", sl, " TP=", tp);
}
else
{
// Use fixed SL/TP from input parameters
sl = InpStopLoss > 0 ? price + InpStopLoss * _Point * 10 : 0;
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());
Print("Sell order opened. Ticket: ", trade.ResultOrder(), " Price: ", price, " SL: ", sl, " TP: ", tp);
}
else
{
ai/dummy/README.md
+34 -5
View File
@@ -71,16 +71,45 @@ python predict_with_onnx.py --model models/XAUUSD_H1_model.onnx --symbol XAUUSD
### Step 3: Use in Expert Advisor
#### For Strategy Tester:
1. Copy the ONNX model to Tester Files folder:
```
<MT5 Data Folder>\Tester\Files\XAUUSD_H1_model.onnx
```
Or use the full path shown in error messages if file not found.
2. Compile `ONNX_EA.mq5` in MetaEditor (F7)
3. Open Strategy Tester (View → Strategy Tester or Ctrl+R)
4. Configure:
- Expert Advisor: `ONNX_EA`
- Symbol: `XAUUSD` (or your symbol)
- Period: `H1` (or your timeframe)
- Inputs:
- `InpModelPath`: `XAUUSD_H1_model.onnx` (just filename)
- Adjust other parameters as needed
5. Click Start
#### For Live/Demo Trading:
1. Copy the ONNX model to MT5's Files folder:
```
<MT5 Data Folder>\MQL5\Files\models\XAUUSD_H1_model.onnx
<MT5 Data Folder>\MQL5\Files\XAUUSD_H1_model.onnx
```
To find your Data Folder: Tools → Options → Expert Advisors → Data Folder
2. Compile `ONNX_EA.mq5` in MetaEditor
2. Compile `ONNX_EA.mq5` in MetaEditor (F7)
3. Attach the EA to a chart with:
- Model path: `models\XAUUSD_H1_model.onnx`
- Your trading parameters
3. Open a chart (e.g., XAUUSD H1)
4. Drag `ONNX_EA` from Navigator (Ctrl+N) onto the chart
5. Configure inputs:
- `InpModelPath`: `XAUUSD_H1_model.onnx` (just filename)
- Adjust trading parameters
6. Click OK and enable AutoTrading if needed
## Detailed Usage