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"\n",
"# 📈 Time-Series Forecasting for Trading (with Feature Engineering)\n",
"\n",
"This notebook implements various **time-series forecasting models** to predict price movements using **live data from MetaTrader 5 (MT5)**.\n",
"\n",
"## **🔹 Models Included:**\n",
"✅ **ARIMA** - Univariate Forecasting \n",
"✅ **SARIMA** - Seasonal Adjusted Forecasting \n",
"✅ **VAR** - Multivariate Forecasting (e.g., Price + Volume) \n",
"✅ **LSTM/Transformer** - Deep Learning-Based Forecasting \n",
"\n",
"### **🔹 Workflow:**\n",
"1️⃣ **Fetch Live Data from MT5** \n",
"2️⃣ **Apply Feature Engineering (Technical Indicators, Autocorrelation, Stationarity Checks)** \n",
"3️⃣ **Select Best Features Dynamically** \n",
"4️⃣ **Train & Evaluate Forecasting Models** \n",
"5️⃣ **Compare Predictions Using Metrics (RMSE, MAE, MAPE, Sharpe Ratio)** \n",
" "
]
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"source": [
"\n",
"import sys\n",
"import os\n",
"import warnings\n",
"import pandas as pd\n",
"import numpy as np\n",
"import plotly.express as px\n",
"import plotly.graph_objects as go\n",
"import MetaTrader5 as mt5\n",
"\n",
"# Time-Series & ML Libraries\n",
"import statsmodels.api as sm\n",
"from statsmodels.tsa.arima.model import ARIMA\n",
"from statsmodels.tsa.statespace.sarimax import SARIMAX\n",
"from statsmodels.tsa.stattools import adfuller\n",
"from statsmodels.tsa.vector_ar.var_model import VAR\n",
"\n",
"# Deep Learning (LSTM)\n",
"import tensorflow as tf\n",
"from tensorflow.keras.models import Sequential\n",
"from tensorflow.keras.layers import LSTM, Dense, Dropout\n",
"from sklearn.preprocessing import MinMaxScaler\n",
"from sklearn.metrics import mean_absolute_error, mean_squared_error\n",
"\n",
"# Fix import issue (ensure project root is recognized)\n",
"from pathlib import Path\n",
"project_root = Path.cwd().parent.parent # Move two levels up (from notebooks/time_series)\n",
"sys.path.append(str(project_root))\n",
"\n",
"# Import Feature Engineering Module\n",
"from features.feature_engineering import add_all_ta_features\n",
"from models.model_training import walk_forward_splits\n",
"warnings.filterwarnings(\"ignore\")\n",
" "
]
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" open high low close tick_volume \\\n",
"time \n",
"2025-01-17 06:00:00 101016.27 101477.00 100886.39 101449.28 9548 \n",
"2025-01-17 07:00:00 101449.28 101776.52 101305.00 101335.62 16329 \n",
"2025-01-17 08:00:00 101339.91 101594.76 101292.85 101403.80 15285 \n",
"2025-01-17 09:00:00 101403.80 101884.59 101391.16 101444.24 10256 \n",
"2025-01-17 10:00:00 101445.52 102213.81 101440.76 102124.33 17320 \n",
"\n",
" spread real_volume volume_adi volume_obv \\\n",
"time \n",
"2025-01-17 06:00:00 244 0 8651.738304 9548 \n",
"2025-01-17 07:00:00 0 0 -5556.486416 18446744073709544835 \n",
"2025-01-17 08:00:00 9 0 -9607.206332 8504 \n",
"2025-01-17 09:00:00 1150 0 -17656.658372 18760 \n",
"2025-01-17 10:00:00 0 0 -4346.214287 36080 \n",
"\n",
" volume_cmf ... momentum_ppo momentum_ppo_signal \\\n",
"time ... \n",
"2025-01-17 06:00:00 0.906131 ... 0.000000 0.000000 \n",
"2025-01-17 07:00:00 -0.214727 ... -0.008938 -0.001788 \n",
"2025-01-17 08:00:00 -0.233400 ... -0.010478 -0.003526 \n",
"2025-01-17 09:00:00 -0.343394 ... -0.008384 -0.004497 \n",
"2025-01-17 10:00:00 -0.063229 ... 0.046811 0.005764 \n",
"\n",
" momentum_ppo_hist momentum_pvo momentum_pvo_signal \\\n",
"time \n",
"2025-01-17 06:00:00 0.000000 0.000000 0.000000 \n",
"2025-01-17 07:00:00 -0.007150 5.382274 1.076455 \n",
"2025-01-17 08:00:00 -0.006952 8.385635 2.538291 \n",
"2025-01-17 09:00:00 -0.003887 6.965404 3.423714 \n",
"2025-01-17 10:00:00 0.041047 10.648655 4.868702 \n",
"\n",
" momentum_pvo_hist momentum_kama others_dr others_dlr \\\n",
"time \n",
"2025-01-17 06:00:00 0.000000 101449.280000 0.000000 0.000000 \n",
"2025-01-17 07:00:00 4.305819 101413.203543 -0.112036 -0.112099 \n",
"2025-01-17 08:00:00 5.847344 101410.132241 0.067281 0.067259 \n",
"2025-01-17 09:00:00 3.541691 101422.759647 0.039880 0.039872 \n",
"2025-01-17 10:00:00 5.779953 101670.655344 0.670408 0.668170 \n",
"\n",
" others_cr \n",
"time \n",
"2025-01-17 06:00:00 0.000000 \n",
"2025-01-17 07:00:00 -0.112036 \n",
"2025-01-17 08:00:00 -0.044830 \n",
"2025-01-17 09:00:00 -0.004968 \n",
"2025-01-17 10:00:00 0.665406 \n",
"\n",
"[5 rows x 93 columns]\n"
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"# Initialize MetaTrader 5 and Fetch Data\n",
"if not mt5.initialize():\n",
" print(\"Failed to initialize MT5\")\n",
"else:\n",
" SYMBOL = \"BTCUSD\" # Change symbol here\n",
" TIMEFRAME = mt5.TIMEFRAME_H1 # 1-hour candles\n",
" N_BARS = 1000\n",
"\n",
" df = mt5.copy_rates_from_pos(SYMBOL, TIMEFRAME, 0, N_BARS)\n",
" mt5.shutdown()\n",
"\n",
"# Convert to DataFrame\n",
"df = pd.DataFrame(df)\n",
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"\n",
"# Apply Feature Engineering (Technical Indicators, Autocorrelation, etc.)\n",
"df = add_all_ta_features(df)\n",
"\n",
"# Drop NaN values created during feature calculations\n",
"df.dropna(inplace=True)\n",
"\n",
"# Display Data Overview\n",
"print(df.head())\n",
"\n",
"# Plot Closing Price\n",
"fig = px.line(df, x=df.index, y=\"close\", title=f\"{SYMBOL} Price Data with Features\")\n",
"fig.show()\n",
" "
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"Top Selected Features for Forecasting: ['others_cr', 'close', 'high', 'low', 'open', 'trend_ichimoku_conv', 'trend_ema_fast', 'volume_vpt', 'momentum_kama', 'trend_ichimoku_a']\n"
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"\n",
"# Select Top Correlated Features with Closing Price\n",
"corr_matrix = df.corr()\n",
"top_features = corr_matrix[\"close\"].abs().sort_values(ascending=False).index[:10]\n",
"\n",
"# Filter dataset to only include top features\n",
"df_selected = df[top_features]\n",
"\n",
"# Display Selected Features\n",
"print(\"Top Selected Features for Forecasting:\", top_features.tolist())\n",
"\n",
"# Plot Feature Correlations\n",
"fig = px.imshow(corr_matrix.loc[top_features, top_features], text_auto=True, title=\"Feature Correlation Heatmap\")\n",
"fig.show()\n",
" "
]
},
{
"cell_type": "code",
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"id": "6a32e97b",
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"text": [
"Testing Stationarity for others_cr:\n",
"ADF Statistic: 0.7350\n",
"p-value: 0.9905\n",
"❌ The series is likely non-stationary. Differencing may be needed.\n",
"\n",
"Testing Stationarity for close:\n",
"ADF Statistic: 0.7350\n",
"p-value: 0.9905\n",
"❌ The series is likely non-stationary. Differencing may be needed.\n",
"\n",
"Testing Stationarity for high:\n",
"ADF Statistic: 0.0071\n",
"p-value: 0.9591\n",
"❌ The series is likely non-stationary. Differencing may be needed.\n",
"\n",
"Testing Stationarity for low:\n",
"ADF Statistic: -0.2206\n",
"p-value: 0.9360\n",
"❌ The series is likely non-stationary. Differencing may be needed.\n",
"\n",
"Testing Stationarity for open:\n",
"ADF Statistic: 0.5491\n",
"p-value: 0.9863\n",
"❌ The series is likely non-stationary. Differencing may be needed.\n",
"\n",
"Testing Stationarity for trend_ichimoku_conv:\n",
"ADF Statistic: 0.3733\n",
"p-value: 0.9805\n",
"❌ The series is likely non-stationary. Differencing may be needed.\n",
"\n",
"Testing Stationarity for trend_ema_fast:\n",
"ADF Statistic: 0.5945\n",
"p-value: 0.9875\n",
"❌ The series is likely non-stationary. Differencing may be needed.\n",
"\n",
"Testing Stationarity for volume_vpt:\n",
"ADF Statistic: 1.2783\n",
"p-value: 0.9965\n",
"❌ The series is likely non-stationary. Differencing may be needed.\n",
"\n",
"Testing Stationarity for momentum_kama:\n",
"ADF Statistic: 0.6168\n",
"p-value: 0.9880\n",
"❌ The series is likely non-stationary. Differencing may be needed.\n",
"\n",
"Testing Stationarity for trend_ichimoku_a:\n",
"ADF Statistic: 0.2880\n",
"p-value: 0.9768\n",
"❌ The series is likely non-stationary. Differencing may be needed.\n",
"\n"
]
}
],
"source": [
"\n",
"def test_stationarity(series):\n",
" result = adfuller(series.dropna())\n",
" print(f\"ADF Statistic: {result[0]:.4f}\")\n",
" print(f\"p-value: {result[1]:.4f}\")\n",
" if result[1] < 0.05:\n",
" print(\"✅ The series is likely stationary.\")\n",
" else:\n",
" print(\"❌ The series is likely non-stationary. Differencing may be needed.\")\n",
"\n",
"# Apply Stationarity Test to Selected Features\n",
"for feature in df_selected.columns:\n",
" print(f\"Testing Stationarity for {feature}:\")\n",
" test_stationarity(df_selected[feature])\n",
" print(\"\")\n",
"\n",
"# Apply Differencing to Non-Stationary Features\n",
"for feature in df_selected.columns:\n",
" df_selected[f\"{feature}_diff\"] = df_selected[feature].diff().dropna()\n",
"\n",
"# Remove Original Columns and Keep Differenced Features\n",
"df_selected.drop(columns=top_features, inplace=True)\n",
"df_selected.dropna(inplace=True)\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "933c36fe",
"metadata": {},
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"source": [
"# Ensure 'df_test' Exists for Storing Forecasts\n",
"if 'df_test' not in locals():\n",
" train_size = int(len(df_selected) * 0.8)\n",
" df_train, df_test = df_selected.iloc[:train_size], df_selected.iloc[train_size:].copy()\n",
"\n",
"# Train ARIMA Model\n",
"best_feature = df_selected.columns[0]\n",
"arima_model = ARIMA(df_train[best_feature].dropna(), order=(1,1,1))\n",
"arima_result = arima_model.fit()\n",
"\n",
"# Forecast Next Steps\n",
"arima_forecast = arima_result.forecast(steps=len(df_test))\n",
"df_test[\"arima_forecast\"] = arima_forecast.values\n",
"\n",
"# Plot ARIMA Forecast\n",
"fig = go.Figure()\n",
"fig.add_trace(go.Scatter(x=df_test.index, y=df_test[best_feature], name=\"Actual Price\"))\n",
"fig.add_trace(go.Scatter(x=df_test.index, y=df_test[\"arima_forecast\"], name=\"ARIMA Forecast\", line=dict(color=\"red\")))\n",
"fig.update_layout(title=\"ARIMA Forecast\", xaxis_title=\"Time\", yaxis_title=\"Price\")\n",
"fig.show()\n"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "c59e623e",
"metadata": {},
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"baxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
},
"bgcolor": "#E5ECF6",
"caxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
}
},
"title": {
"x": 0.05
},
"xaxis": {
"automargin": true,
"gridcolor": "white",
"linecolor": "white",
"ticks": "",
"title": {
"standoff": 15
},
"zerolinecolor": "white",
"zerolinewidth": 2
},
"yaxis": {
"automargin": true,
"gridcolor": "white",
"linecolor": "white",
"ticks": "",
"title": {
"standoff": 15
},
"zerolinecolor": "white",
"zerolinewidth": 2
}
}
},
"title": {
"text": "SARIMA Forecast"
},
"xaxis": {
"title": {
"text": "Time"
}
},
"yaxis": {
"title": {
"text": "Price"
}
}
}
}
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"\n",
"# ---- SARIMA ----\n",
"sarima_model = SARIMAX(df_train[best_feature].dropna(), order=(1,1,1), seasonal_order=(1,1,1,12))\n",
"sarima_result = sarima_model.fit()\n",
"\n",
"# Forecast Next Steps\n",
"sarima_forecast = sarima_result.forecast(steps=len(df_test))\n",
"df_test[\"sarima_forecast\"] = sarima_forecast.values\n",
"\n",
"# Plot SARIMA Forecast\n",
"fig = go.Figure()\n",
"fig.add_trace(go.Scatter(x=df_test.index, y=df_test[best_feature], name=\"Actual Price\"))\n",
"fig.add_trace(go.Scatter(x=df_test.index, y=df_test[\"sarima_forecast\"], name=\"SARIMA Forecast\", line=dict(color=\"green\")))\n",
"fig.update_layout(title=\"SARIMA Forecast\", xaxis_title=\"Time\", yaxis_title=\"Price\")\n",
"fig.show()\n"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "d9e6b0da",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.plotly.v1+json": {
"config": {
"plotlyServerURL": "https://plot.ly"
},
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"source": [
"\n",
"# ---- VAR ----\n",
"df_var = df_selected.dropna()\n",
"var_model = VAR(df_var)\n",
"var_result = var_model.fit(maxlags=5)\n",
"\n",
"# Forecast Next Steps\n",
"var_forecast = var_result.forecast(df_var.values[-5:], steps=len(df_test))\n",
"var_forecast_df = pd.DataFrame(var_forecast, columns=df_var.columns, index=df_test.index)\n",
"\n",
"# Store VAR Forecast in `df_test`\n",
"df_test[\"var_forecast\"] = var_forecast_df[best_feature]\n",
"\n",
"# Plot VAR Forecast\n",
"fig = go.Figure()\n",
"fig.add_trace(go.Scatter(x=df_test.index, y=df_test[best_feature], name=\"Actual Price\"))\n",
"fig.add_trace(go.Scatter(x=df_test.index, y=df_test[\"var_forecast\"], name=\"VAR Forecast\", line=dict(color=\"blue\")))\n",
"fig.update_layout(title=\"VAR Forecast\", xaxis_title=\"Time\", yaxis_title=\"Price\")\n",
"fig.show()\n"
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{
"cell_type": "code",
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"id": "0ecb9788",
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"name": "stdout",
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"text": [
"Epoch 1/20\n",
"\u001b[1m59/59\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 8ms/step - loss: 0.1473\n",
"Epoch 2/20\n",
"\u001b[1m59/59\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 0.0037\n",
"Epoch 3/20\n",
"\u001b[1m59/59\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0028\n",
"Epoch 4/20\n",
"\u001b[1m59/59\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0025\n",
"Epoch 5/20\n",
"\u001b[1m59/59\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - loss: 0.0032\n",
"Epoch 6/20\n",
"\u001b[1m59/59\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - loss: 0.0031\n",
"Epoch 7/20\n",
"\u001b[1m59/59\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0028\n",
"Epoch 8/20\n",
"\u001b[1m59/59\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0031\n",
"Epoch 9/20\n",
"\u001b[1m59/59\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0027\n",
"Epoch 10/20\n",
"\u001b[1m59/59\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - loss: 0.0027\n",
"Epoch 11/20\n",
"\u001b[1m59/59\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0023\n",
"Epoch 12/20\n",
"\u001b[1m59/59\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0028\n",
"Epoch 13/20\n",
"\u001b[1m59/59\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - loss: 0.0027\n",
"Epoch 14/20\n",
"\u001b[1m59/59\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - loss: 0.0028\n",
"Epoch 15/20\n",
"\u001b[1m59/59\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 0.0027\n",
"Epoch 16/20\n",
"\u001b[1m59/59\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0023\n",
"Epoch 17/20\n",
"\u001b[1m59/59\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0021\n",
"Epoch 18/20\n",
"\u001b[1m59/59\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 0.0022\n",
"Epoch 19/20\n",
"\u001b[1m59/59\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 0.0019\n",
"Epoch 20/20\n",
"\u001b[1m59/59\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - loss: 0.0019\n",
"\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 438ms/step\n"
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"# Ensure 'close' is retained\n",
"if 'close' not in df_selected.columns:\n",
" df_selected['close'] = df['close']\n",
"\n",
"# Scale Data\n",
"scaler = MinMaxScaler()\n",
"df_scaled = scaler.fit_transform(df_selected[['close']])\n",
"\n",
"\n",
"# Create Sequences\n",
"lookback = 10\n",
"X, y = [], []\n",
"for i in range(len(df_scaled) - lookback - 1):\n",
" X.append(df_scaled[i:i+lookback])\n",
" y.append(df_scaled[i+lookback])\n",
"\n",
"X, y = np.array(X), np.array(y)\n",
"\n",
"# Split Data\n",
"X_train, X_test, y_train, y_test = X[:-50], X[-50:], y[:-50], y[-50:]\n",
"\n",
"# Define LSTM Model\n",
"model = Sequential([\n",
" LSTM(50, return_sequences=True, input_shape=(lookback, 1)),\n",
" Dropout(0.2),\n",
" LSTM(50),\n",
" Dense(1)\n",
"])\n",
"model.compile(loss=\"mse\", optimizer=\"adam\")\n",
"model.fit(X_train, y_train, epochs=20, batch_size=16, verbose=1)\n",
"\n",
"# Predict\n",
"lstm_preds = model.predict(X_test)\n",
"lstm_preds = scaler.inverse_transform(lstm_preds)\n",
"\n",
"# Plot LSTM Forecast\n",
"fig = go.Figure()\n",
"fig.add_trace(go.Scatter(x=df_selected.index[-100:], y=df_selected['close'][-100:], name=\"Actual Price\"))\n",
"fig.add_trace(go.Scatter(x=df_selected.index[-50:], y=lstm_preds.flatten(), name=\"LSTM Forecast\", line=dict(color=\"orange\")))\n",
"fig.update_layout(title=\"LSTM Forecast\", xaxis_title=\"Time\", yaxis_title=\"Price\")\n",
"fig.show()\n"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "b48200de",
"metadata": {},
"outputs": [
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"name": "stdout",
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"text": [
"\n",
"📊 Performance of ARIMA:\n",
"✅ Mean Absolute Error (MAE): 0.3014\n",
"✅ Mean Squared Error (MSE): 0.2016\n",
"✅ Root Mean Squared Error (RMSE): 0.4490\n",
"\n",
"📊 Performance of SARIMA:\n",
"✅ Mean Absolute Error (MAE): 0.3216\n",
"✅ Mean Squared Error (MSE): 0.2135\n",
"✅ Root Mean Squared Error (RMSE): 0.4620\n",
"\n",
"📊 Performance of VAR:\n",
"✅ Mean Absolute Error (MAE): 0.3060\n",
"✅ Mean Squared Error (MSE): 0.2015\n",
"✅ Root Mean Squared Error (RMSE): 0.4489\n",
"\n",
"📊 Performance of LSTM:\n",
"✅ Mean Absolute Error (MAE): 86364.8782\n",
"✅ Mean Squared Error (MSE): 7461025620.6772\n",
"✅ Root Mean Squared Error (RMSE): 86377.2286\n"
]
}
],
"source": [
"# Define Performance Metrics Function\n",
"def evaluate_forecasts(y_true, y_pred, model_name):\n",
" mae = mean_absolute_error(y_true, y_pred)\n",
" mse = mean_squared_error(y_true, y_pred)\n",
" rmse = np.sqrt(mse)\n",
" print(f\"\\n📊 Performance of {model_name}:\")\n",
" print(f\"✅ Mean Absolute Error (MAE): {mae:.4f}\")\n",
" print(f\"✅ Mean Squared Error (MSE): {mse:.4f}\")\n",
" print(f\"✅ Root Mean Squared Error (RMSE): {rmse:.4f}\")\n",
"\n",
"# Evaluate ARIMA\n",
"evaluate_forecasts(df_test[best_feature], df_test[\"arima_forecast\"], \"ARIMA\")\n",
"\n",
"# Evaluate SARIMA\n",
"evaluate_forecasts(df_test[best_feature], df_test[\"sarima_forecast\"], \"SARIMA\")\n",
"\n",
"# Evaluate VAR\n",
"evaluate_forecasts(df_test[best_feature], df_test[\"var_forecast\"], \"VAR\")\n",
"\n",
"# Evaluate LSTM\n",
"evaluate_forecasts(y_test.flatten(), lstm_preds.flatten(), \"LSTM\")\n"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "66e6e84c",
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"# Plot Comparison of All Forecasts\n",
"fig = go.Figure()\n",
"\n",
"# Actual Prices\n",
"fig.add_trace(go.Scatter(x=df_test.index, y=df_test[best_feature], name=\"Actual Price\", line=dict(color=\"black\")))\n",
"\n",
"# ARIMA\n",
"fig.add_trace(go.Scatter(x=df_test.index, y=df_test[\"arima_forecast\"], name=\"ARIMA\", line=dict(color=\"red\")))\n",
"\n",
"# SARIMA\n",
"fig.add_trace(go.Scatter(x=pd.date_range(df_test.index[0], periods=len(sarima_forecast), freq=\"H\"),\n",
" y=sarima_forecast, name=\"SARIMA\", line=dict(color=\"green\")))\n",
"\n",
"# VAR\n",
"fig.add_trace(go.Scatter(x=pd.date_range(df_test.index[0], periods=len(var_forecast_df[best_feature]), freq=\"H\"),\n",
" y=var_forecast_df[best_feature], name=\"VAR\", line=dict(color=\"blue\")))\n",
"\n",
"# LSTM\n",
"fig.add_trace(go.Scatter(x=df_test.index[-50:], y=lstm_preds.flatten(), name=\"LSTM\", line=dict(color=\"orange\")))\n",
"\n",
"fig.update_layout(title=\"Comparison of Time-Series Forecasting Models\", xaxis_title=\"Time\", yaxis_title=\"Price\")\n",
"fig.show()\n"
]
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"source": [
"## Part 1: Data & Feature Engineering\n",
"\n",
"**Objective:** \n",
"Load raw price data (MetaTrader 5 or CSV) and transform it into a feature-rich dataset.\n",
"\n",
"**Tasks:**\n",
"- Fetch historical bars \n",
"- Apply `ta.add_all_ta_features` or custom features \n",
"- (Optionally) create specific labels (multi-bar, double-barrier, regime, etc.) \n",
"- Clean/prepare the final feature matrix **X** and target **y** "
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "a00cd5db",
"metadata": {},
"outputs": [
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"name": "stdout",
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"text": [
"✅ Features (X): (1000, 92), Target (y): (1000,)\n",
"✅ 3 folds created for walk-forward validation!\n",
"\n",
"===== Fold 1 =====\n",
"🔹 Best Feature Selected: others_cr\n"
]
},
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"c:\\Users\\moham\\miniconda3\\envs\\dl\\Lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" self._init_dates(dates, freq)\n",
"c:\\Users\\moham\\miniconda3\\envs\\dl\\Lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" self._init_dates(dates, freq)\n",
"c:\\Users\\moham\\miniconda3\\envs\\dl\\Lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" self._init_dates(dates, freq)\n",
"c:\\Users\\moham\\miniconda3\\envs\\dl\\Lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:837: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.\n",
" return get_prediction_index(\n",
"c:\\Users\\moham\\miniconda3\\envs\\dl\\Lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" self._init_dates(dates, freq)\n",
"c:\\Users\\moham\\miniconda3\\envs\\dl\\Lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" self._init_dates(dates, freq)\n",
"c:\\Users\\moham\\miniconda3\\envs\\dl\\Lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:837: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.\n",
" return get_prediction_index(\n",
"c:\\Users\\moham\\miniconda3\\envs\\dl\\Lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
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"===== Fold 2 =====\n",
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"\n",
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"🔹 ARIMA - Fold 2: MAE = 0.8383, RMSE = 1.0495\n",
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"===== Fold 3 =====\n",
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"🔹 ARIMA - Fold 3: MAE = 3.2151, RMSE = 5.1056\n",
"🔹 SARIMA - Fold 3: MAE = 2.8176, RMSE = 4.0693\n",
"🔹 VAR - Fold 3: MAE = 123873828408479325356032.0000, RMSE = 670590313183068318334976.0000\n",
"\n",
"=== Fold 1 Results Summary ===\n",
"📌 ARIMA - MAE: 2.6297, RMSE: 3.0618\n",
"📌 SARIMA - MAE: 2.4853, RMSE: 3.0333\n",
"📌 VAR - MAE: 2.3171, RMSE: 2.8153\n",
"\n",
"=== Fold 2 Results Summary ===\n",
"📌 ARIMA - MAE: 0.8383, RMSE: 1.0495\n",
"📌 SARIMA - MAE: 1.0987, RMSE: 1.2946\n",
"📌 VAR - MAE: 15266.2514, RMSE: 41890.0691\n",
"\n",
"=== Fold 3 Results Summary ===\n",
"📌 ARIMA - MAE: 3.2151, RMSE: 5.1056\n",
"📌 SARIMA - MAE: 2.8176, RMSE: 4.0693\n",
"📌 VAR - MAE: 123873828408479325356032.0000, RMSE: 670590313183068318334976.0000\n"
]
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"source": [
"import sys\n",
"import os\n",
"import warnings\n",
"from pathlib import Path\n",
"\n",
"# -----------------------------------------------------------------------------\n",
"# 1) SET PROJECT ROOT AND UPDATE PATH/WORKING DIRECTORY\n",
"# -----------------------------------------------------------------------------\n",
"project_root = Path.cwd().parent.parent # Assuming notebook is in notebooks/time_series\n",
"sys.path.append(str(project_root))\n",
"os.chdir(str(project_root))\n",
"warnings.filterwarnings(\"ignore\")\n",
"\n",
"# -----------------------------------------------------------------------------\n",
"# 2) IMPORT LIBRARIES & MODULES\n",
"# -----------------------------------------------------------------------------\n",
"import pandas as pd\n",
"import numpy as np\n",
"import MetaTrader5 as mt5\n",
"import vectorbt as vbt\n",
"import plotly.graph_objects as go\n",
"\n",
"# Time-Series Libraries\n",
"import statsmodels.api as sm\n",
"from statsmodels.tsa.arima.model import ARIMA\n",
"from statsmodels.tsa.statespace.sarimax import SARIMAX\n",
"from statsmodels.tsa.vector_ar.var_model import VAR\n",
"\n",
"# Feature Engineering & Data Loader\n",
"from data.data_loader import get_data_mt5\n",
"from features.feature_engineering import add_all_ta_features\n",
"from models.model_training import walk_forward_splits\n",
"from sklearn.metrics import mean_absolute_error, mean_squared_error\n",
"\n",
"# -----------------------------------------------------------------------------\n",
"# 3) DATA LOADING & FEATURE ENGINEERING\n",
"# -----------------------------------------------------------------------------\n",
"if not mt5.initialize():\n",
" print(\"Failed to initialize MT5\")\n",
"else:\n",
" data = get_data_mt5(symbol=\"BTCUSD\", timeframe=mt5.TIMEFRAME_H1, n_bars=1000)\n",
" mt5.shutdown()\n",
"\n",
"df = add_all_ta_features(data).dropna() # Apply technical indicators\n",
"\n",
"# Select features and target (closing price prediction)\n",
"X = df.drop(columns=[\"close\"])\n",
"y = df[\"close\"]\n",
"\n",
"print(f\"✅ Features (X): {X.shape}, Target (y): {y.shape}\")\n",
"\n",
"# -----------------------------------------------------------------------------\n",
"# 4) WALK-FORWARD SPLITS\n",
"# -----------------------------------------------------------------------------\n",
"folds = walk_forward_splits(X, y, n_splits=3) # Apply walk-forward splits\n",
"print(f\"✅ {len(folds)} folds created for walk-forward validation!\")\n",
"\n",
"# Dictionary to store each fold's results and the best feature used\n",
"fold_results = {}\n",
"\n",
"# -----------------------------------------------------------------------------\n",
"# 5) TIME-SERIES MODELS TRAINING, FORECASTING & EVALUATION FOR EACH FOLD\n",
"# -----------------------------------------------------------------------------\n",
"for fold_i, (X_train_fold, y_train_fold, X_test_fold, y_test_fold) in enumerate(folds, start=1):\n",
" print(f\"\\n===== Fold {fold_i} =====\")\n",
" \n",
" # Convert train/test folds into DataFrame format\n",
" df_train = pd.DataFrame(X_train_fold, columns=X.columns)\n",
" df_train[\"target\"] = y_train_fold.values\n",
"\n",
" df_test = pd.DataFrame(X_test_fold, columns=X.columns)\n",
" df_test[\"target\"] = y_test_fold.values\n",
"\n",
" # Select Best Feature (based on correlation with target), excluding the 'target' column\n",
" feature_corr = df_train.drop(columns=[\"target\"]).corrwith(df_train[\"target\"]).abs().sort_values(ascending=False)\n",
" best_feature = feature_corr.index[0]\n",
" print(f\"🔹 Best Feature Selected: {best_feature}\")\n",
"\n",
" # ----- Model 1: ARIMA -----\n",
" arima_model = ARIMA(df_train[best_feature].dropna(), order=(1, 1, 1))\n",
" arima_result = arima_model.fit()\n",
" arima_forecast = arima_result.forecast(steps=len(df_test))\n",
" df_test[\"arima_forecast\"] = arima_forecast.values\n",
"\n",
" # ----- Model 2: SARIMA -----\n",
" sarima_model = SARIMAX(df_train[best_feature].dropna(), order=(1, 1, 1), seasonal_order=(1, 1, 1, 12))\n",
" sarima_result = sarima_model.fit()\n",
" sarima_forecast = sarima_result.forecast(steps=len(df_test))\n",
" df_test[\"sarima_forecast\"] = sarima_forecast.values\n",
"\n",
" # ----- Model 3: VAR (Multi-Feature Model) -----\n",
" df_var = df_train.drop(columns=[\"target\"]).dropna()\n",
" if df_var.empty:\n",
" print(f\"⚠️ Skipping VAR model for Fold {fold_i} due to insufficient data!\")\n",
" df_test[\"var_forecast\"] = np.nan\n",
" else:\n",
" var_model = VAR(df_var)\n",
" var_result = var_model.fit(maxlags=5)\n",
"\n",
" # Ensure df_test has the necessary features for VAR\n",
" df_var_test = df_test.drop(columns=[\"target\"]).dropna()\n",
" if df_var_test.empty:\n",
" print(f\"⚠️ Skipping VAR forecast for Fold {fold_i} due to missing features!\")\n",
" df_test[\"var_forecast\"] = np.nan\n",
" else:\n",
" # Forecast using the last 5 observations from df_var\n",
" var_forecast = var_result.forecast(df_var.values[-5:], steps=len(df_var_test))\n",
" var_forecast_df = pd.DataFrame(var_forecast, columns=df_var.columns)\n",
"\n",
" # If the best feature exists in the VAR forecast, use it\n",
" if best_feature in var_forecast_df.columns:\n",
" df_test[\"var_forecast\"] = var_forecast_df[best_feature].values\n",
" else:\n",
" print(f\"⚠️ Best feature '{best_feature}' missing from VAR forecast in Fold {fold_i}. Filling with NaNs.\")\n",
" df_test[\"var_forecast\"] = np.nan\n",
"\n",
" # -----------------------------------------------------------------------------\n",
" # PLOTTING FORECASTS FOR THE CURRENT FOLD\n",
" # -----------------------------------------------------------------------------\n",
" fig = go.Figure()\n",
" fig.add_trace(go.Scatter(x=df_train.index, y=df_train[best_feature], name=\"Train Data\"))\n",
" fig.add_trace(go.Scatter(x=df_test.index, y=df_test[best_feature], name=\"Test Data\", line=dict(color=\"gray\")))\n",
" fig.add_trace(go.Scatter(x=df_test.index, y=df_test[\"arima_forecast\"], name=\"ARIMA Forecast\", line=dict(color=\"red\")))\n",
" fig.add_trace(go.Scatter(x=df_test.index, y=df_test[\"sarima_forecast\"], name=\"SARIMA Forecast\", line=dict(color=\"green\")))\n",
" fig.add_trace(go.Scatter(x=df_test.index, y=df_test[\"var_forecast\"], name=\"VAR Forecast\", line=dict(color=\"blue\")))\n",
" fig.update_layout(title=f\"Forecasting - Fold {fold_i}\", xaxis_title=\"Time\", yaxis_title=best_feature)\n",
" fig.show()\n",
"\n",
" # -----------------------------------------------------------------------------\n",
" # Evaluate Forecasts (for each model) within the fold\n",
" # -----------------------------------------------------------------------------\n",
" def evaluate_forecasts(actual, predicted, model_name):\n",
" mae = mean_absolute_error(actual, predicted)\n",
" rmse = np.sqrt(mean_squared_error(actual, predicted))\n",
" print(f\"🔹 {model_name} - Fold {fold_i}: MAE = {mae:.4f}, RMSE = {rmse:.4f}\")\n",
"\n",
" # Fill any missing forecast values (backward fill)\n",
" df_test[\"arima_forecast\"].fillna(method=\"bfill\", inplace=True)\n",
" df_test[\"sarima_forecast\"].fillna(method=\"bfill\", inplace=True)\n",
" df_test[\"var_forecast\"].fillna(method=\"bfill\", inplace=True)\n",
"\n",
" # Store the fold's results along with the best feature used\n",
" fold_results[fold_i] = {\"df\": df_test.copy(), \"best_feature\": best_feature}\n",
"\n",
" # Optional: Evaluate immediately for the current fold (dropping NaNs)\n",
" df_test_cleaned = df_test.dropna(subset=[best_feature, \"arima_forecast\", \"sarima_forecast\", \"var_forecast\"])\n",
" if df_test_cleaned.empty:\n",
" print(f\"⚠️ Warning: No valid forecast samples for evaluation in Fold {fold_i}.\")\n",
" else:\n",
" evaluate_forecasts(df_test_cleaned[best_feature], df_test_cleaned[\"arima_forecast\"], \"ARIMA\")\n",
" evaluate_forecasts(df_test_cleaned[best_feature], df_test_cleaned[\"sarima_forecast\"], \"SARIMA\")\n",
" evaluate_forecasts(df_test_cleaned[best_feature], df_test_cleaned[\"var_forecast\"], \"VAR\")\n",
"\n",
"# -----------------------------------------------------------------------------\n",
"# 6) PRINT RESULTS SUMMARY ACROSS FOLDS\n",
"# -----------------------------------------------------------------------------\n",
"for fold_i, fold_info in fold_results.items():\n",
" df_test = fold_info[\"df\"]\n",
" best_feature = fold_info[\"best_feature\"]\n",
" print(f\"\\n=== Fold {fold_i} Results Summary ===\")\n",
"\n",
" # Drop rows with missing values in any key column\n",
" df_test_cleaned = df_test.dropna(subset=[best_feature, \"arima_forecast\", \"sarima_forecast\", \"var_forecast\"])\n",
" if df_test_cleaned.empty:\n",
" print(f\"⚠️ No valid forecast data available for evaluation in Fold {fold_i}.\")\n",
" continue\n",
"\n",
" mae_arima = mean_absolute_error(df_test_cleaned[best_feature], df_test_cleaned[\"arima_forecast\"])\n",
" rmse_arima = np.sqrt(mean_squared_error(df_test_cleaned[best_feature], df_test_cleaned[\"arima_forecast\"]))\n",
"\n",
" mae_sarima = mean_absolute_error(df_test_cleaned[best_feature], df_test_cleaned[\"sarima_forecast\"])\n",
" rmse_sarima = np.sqrt(mean_squared_error(df_test_cleaned[best_feature], df_test_cleaned[\"sarima_forecast\"]))\n",
"\n",
" mae_var = mean_absolute_error(df_test_cleaned[best_feature], df_test_cleaned[\"var_forecast\"])\n",
" rmse_var = np.sqrt(mean_squared_error(df_test_cleaned[best_feature], df_test_cleaned[\"var_forecast\"]))\n",
"\n",
" print(f\"📌 ARIMA - MAE: {mae_arima:.4f}, RMSE: {rmse_arima:.4f}\")\n",
" print(f\"📌 SARIMA - MAE: {mae_sarima:.4f}, RMSE: {rmse_sarima:.4f}\")\n",
" print(f\"📌 VAR - MAE: {mae_var:.4f}, RMSE: {rmse_var:.4f}\")\n"
]
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"Columns after TA features: ['open', 'high', 'low', 'close', 'tick_volume', 'spread', 'real_volume', 'volume_adi', 'volume_obv', 'volume_cmf', 'volume_fi', 'volume_em', 'volume_sma_em', 'volume_vpt', 'volume_vwap', 'volume_mfi', 'volume_nvi', 'volatility_bbm', 'volatility_bbh', 'volatility_bbl', 'volatility_bbw', 'volatility_bbp', 'volatility_bbhi', 'volatility_bbli', 'volatility_kcc', 'volatility_kch', 'volatility_kcl', 'volatility_kcw', 'volatility_kcp', 'volatility_kchi', 'volatility_kcli', 'volatility_dcl', 'volatility_dch', 'volatility_dcm', 'volatility_dcw', 'volatility_dcp', 'volatility_atr', 'volatility_ui', 'trend_macd', 'trend_macd_signal', 'trend_macd_diff', 'trend_sma_fast', 'trend_sma_slow', 'trend_ema_fast', 'trend_ema_slow', 'trend_vortex_ind_pos', 'trend_vortex_ind_neg', 'trend_vortex_ind_diff', 'trend_trix', 'trend_mass_index', 'trend_dpo', 'trend_kst', 'trend_kst_sig', 'trend_kst_diff', 'trend_ichimoku_conv', 'trend_ichimoku_base', 'trend_ichimoku_a', 'trend_ichimoku_b', 'trend_stc', 'trend_adx', 'trend_adx_pos', 'trend_adx_neg', 'trend_cci', 'trend_visual_ichimoku_a', 'trend_visual_ichimoku_b', 'trend_aroon_up', 'trend_aroon_down', 'trend_aroon_ind', 'trend_psar_up', 'trend_psar_down', 'trend_psar_up_indicator', 'trend_psar_down_indicator', 'momentum_rsi', 'momentum_stoch_rsi', 'momentum_stoch_rsi_k', 'momentum_stoch_rsi_d', 'momentum_tsi', 'momentum_uo', 'momentum_stoch', 'momentum_stoch_signal', 'momentum_wr', 'momentum_ao', 'momentum_roc', 'momentum_ppo', 'momentum_ppo_signal', 'momentum_ppo_hist', 'momentum_pvo', 'momentum_pvo_signal', 'momentum_pvo_hist', 'momentum_kama', 'others_dr', 'others_dlr', 'others_cr']\n",
"df_selected shape: (10000, 1)\n",
"df_selected head:\n",
" close\n",
"time \n",
"2020-05-15 00:00:00 9771.74\n",
"2020-05-15 04:00:00 9436.59\n",
"2020-05-15 08:00:00 9609.99\n",
"2020-05-15 12:00:00 9508.49\n",
"2020-05-15 16:00:00 9463.89\n",
"Available columns: Index(['open', 'high', 'low', 'close', 'tick_volume', 'spread', 'real_volume',\n",
" 'volume_adi', 'volume_obv', 'volume_cmf', 'volume_fi', 'volume_em',\n",
" 'volume_sma_em', 'volume_vpt', 'volume_vwap', 'volume_mfi',\n",
" 'volume_nvi', 'volatility_bbm', 'volatility_bbh', 'volatility_bbl',\n",
" 'volatility_bbw', 'volatility_bbp', 'volatility_bbhi',\n",
" 'volatility_bbli', 'volatility_kcc', 'volatility_kch', 'volatility_kcl',\n",
" 'volatility_kcw', 'volatility_kcp', 'volatility_kchi',\n",
" 'volatility_kcli', 'volatility_dcl', 'volatility_dch', 'volatility_dcm',\n",
" 'volatility_dcw', 'volatility_dcp', 'volatility_atr', 'volatility_ui',\n",
" 'trend_macd', 'trend_macd_signal', 'trend_macd_diff', 'trend_sma_fast',\n",
" 'trend_sma_slow', 'trend_ema_fast', 'trend_ema_slow',\n",
" 'trend_vortex_ind_pos', 'trend_vortex_ind_neg', 'trend_vortex_ind_diff',\n",
" 'trend_trix', 'trend_mass_index', 'trend_dpo', 'trend_kst',\n",
" 'trend_kst_sig', 'trend_kst_diff', 'trend_ichimoku_conv',\n",
" 'trend_ichimoku_base', 'trend_ichimoku_a', 'trend_ichimoku_b',\n",
" 'trend_stc', 'trend_adx', 'trend_adx_pos', 'trend_adx_neg', 'trend_cci',\n",
" 'trend_visual_ichimoku_a', 'trend_visual_ichimoku_b', 'trend_aroon_up',\n",
" 'trend_aroon_down', 'trend_aroon_ind', 'trend_psar_up',\n",
" 'trend_psar_down', 'trend_psar_up_indicator',\n",
" 'trend_psar_down_indicator', 'momentum_rsi', 'momentum_stoch_rsi',\n",
" 'momentum_stoch_rsi_k', 'momentum_stoch_rsi_d', 'momentum_tsi',\n",
" 'momentum_uo', 'momentum_stoch', 'momentum_stoch_signal', 'momentum_wr',\n",
" 'momentum_ao', 'momentum_roc', 'momentum_ppo', 'momentum_ppo_signal',\n",
" 'momentum_ppo_hist', 'momentum_pvo', 'momentum_pvo_signal',\n",
" 'momentum_pvo_hist', 'momentum_kama', 'others_dr', 'others_dlr',\n",
" 'others_cr'],\n",
" dtype='object')\n",
"\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 282ms/step\n",
"\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 298ms/step\n",
"WARNING:tensorflow:5 out of the last 5 calls to .one_step_on_data_distributed at 0x000001DE4DD7A020> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n",
"\u001b[1m1/2\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 266ms/stepWARNING:tensorflow:6 out of the last 6 calls to .one_step_on_data_distributed at 0x000001DE4DD7A020> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n",
"\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 307ms/step\n",
"=== Fold 1 ===\n",
"MAE = 618.4380, RMSE = 794.4144\n"
]
},
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"source": [
"\n",
"from pathlib import Path\n",
"import sys\n",
"import os\n",
"import warnings\n",
"\n",
"\n",
"# -----------------------------------------------------------------------------\n",
"# 1) SET PROJECT ROOT AND UPDATE PATH/WORKING DIRECTORY\n",
"# -----------------------------------------------------------------------------\n",
"project_root = Path.cwd().parent.parent # Assuming notebook is in notebooks/time_series\n",
"sys.path.append(str(project_root))\n",
"os.chdir(str(project_root))\n",
"warnings.filterwarnings(\"ignore\")\n",
"\n",
"\n",
"import numpy as np\n",
"import pandas as pd\n",
"import MetaTrader5 as mt5\n",
"\n",
"# Plotting\n",
"import plotly.graph_objects as go\n",
"\n",
"# TensorFlow / Keras\n",
"from tensorflow.keras.models import Sequential\n",
"from tensorflow.keras.layers import LSTM, Dense, Dropout\n",
"from sklearn.preprocessing import MinMaxScaler\n",
"from sklearn.metrics import mean_absolute_error, mean_squared_error\n",
"\n",
"# Custom Modules\n",
"from data.data_loader import get_data_mt5\n",
"from features.feature_engineering import add_all_ta_features\n",
"\n",
"\n",
"# -------------------------------------------------------------------------\n",
"# 3) DATA LOADING & FEATURE ENGINEERING\n",
"# -------------------------------------------------------------------------\n",
"if not mt5.initialize():\n",
" print(\"Failed to initialize MT5\")\n",
"else:\n",
" data = get_data_mt5(symbol=\"BTCUSD\", timeframe=mt5.TIMEFRAME_H4, n_bars=10000)\n",
" mt5.shutdown()\n",
"\n",
"# data is assumed to have columns like ['time', 'open', 'high', 'low', 'close', 'tick_volume', ...]\n",
"df = add_all_ta_features(data).dropna() # This adds many technical indicators\n",
"print(\"Columns after TA features:\", df.columns.tolist())\n",
"\n",
"# For the LSTM, we need at least 'close' in the DataFrame\n",
"# Optionally, if you want only the 'close' column:\n",
"# df_selected = df[['close']]\n",
"# Or if you want multiple features, e.g. 'close', 'volume', 'rsi', 'macd', etc.:\n",
"# Make sure your create_sequences() function handles multi-feature input if you do so.\n",
"df_selected = df[['close']].copy()\n",
"\n",
"print(\"df_selected shape:\", df_selected.shape)\n",
"print(\"df_selected head:\\n\", df_selected.head())\n",
"\n",
"\n",
"###############################################################################\n",
"# 1) LSTM HELPER FUNCTIONS\n",
"###############################################################################\n",
"def create_sequences(data, lookback=10):\n",
" \"\"\"\n",
" Convert a 1D or 2D numpy array (data) into LSTM sequences.\n",
" data shape: (num_samples, 1) or (num_samples, num_features)\n",
" Returns:\n",
" X: shape (num_sequences, lookback, num_features)\n",
" y: shape (num_sequences, num_features) or (num_sequences, 1)\n",
" \"\"\"\n",
" X, y = [], []\n",
" for i in range(len(data) - lookback):\n",
" X.append(data[i : i + lookback])\n",
" y.append(data[i + lookback])\n",
" return np.array(X), np.array(y)\n",
"\n",
"def lstm_walk_forward(df, n_splits=3, lookback=10, horizon=50, epochs=20, batch_size=16):\n",
" \"\"\"\n",
" Perform walk-forward validation with an LSTM model.\n",
"\n",
" Parameters:\n",
" -----------\n",
" df : DataFrame with a 'close' column (and optionally other columns).\n",
" n_splits : Number of walk-forward folds.\n",
" lookback : Number of timesteps in each LSTM input sequence.\n",
" horizon : Number of test points in each fold.\n",
" epochs : Training epochs for each fold's LSTM.\n",
" batch_size: Batch size for LSTM training.\n",
"\n",
" Returns:\n",
" --------\n",
" fold_results : list of dicts, each containing:\n",
" - 'fold' : Fold index (1-based).\n",
" - 'mae' : MAE on the fold test set.\n",
" - 'rmse' : RMSE on the fold test set.\n",
" - 'preds' : Inverse-scaled predictions.\n",
" - 'y_test' : Inverse-scaled actuals.\n",
" - 'df_train': Training DataFrame slice for the fold.\n",
" - 'df_test' : Test DataFrame slice for the fold.\n",
" \"\"\"\n",
" if 'close' not in df.columns:\n",
" raise ValueError(\"DataFrame must have a 'close' column.\")\n",
"\n",
" total_length = len(df)\n",
" # The maximum training length per fold is everything minus the final horizon\n",
" fold_size = (total_length - horizon) // n_splits\n",
"\n",
" fold_results = []\n",
" start_idx = 0\n",
"\n",
" for fold_idx in range(n_splits):\n",
" # End of training region for this fold\n",
" train_end = start_idx + fold_size\n",
" if fold_idx == n_splits - 1:\n",
" # Last fold takes everything up to the final horizon\n",
" train_end = total_length - horizon\n",
" \n",
" test_end = train_end + horizon\n",
"\n",
" # Slice the DataFrame for training and testing\n",
" df_train = df.iloc[:train_end]\n",
" df_test = df.iloc[train_end:test_end]\n",
"\n",
" # Scale each fold separately based on the training set\n",
" scaler = MinMaxScaler()\n",
" train_scaled = scaler.fit_transform(df_train[['close']])\n",
" test_scaled = scaler.transform(df_test[['close']])\n",
"\n",
" # Create sequences for training\n",
" X_train, y_train = create_sequences(train_scaled, lookback)\n",
"\n",
" # Combine last 'lookback' points of training + test set for test sequences\n",
" last_train_part = train_scaled[-lookback:]\n",
" test_input = np.concatenate([last_train_part, test_scaled], axis=0)\n",
" X_test, y_test = create_sequences(test_input, lookback)\n",
"\n",
" # Define LSTM model\n",
" model = Sequential([\n",
" LSTM(50, return_sequences=True, input_shape=(lookback, 1)),\n",
" Dropout(0.2),\n",
" LSTM(50),\n",
" Dense(1)\n",
" ])\n",
" model.compile(loss=\"mse\", optimizer=\"adam\")\n",
"\n",
" # Train\n",
" model.fit(X_train, y_train, epochs=epochs, batch_size=batch_size, verbose=0)\n",
"\n",
" # Predict\n",
" preds = model.predict(X_test)\n",
" preds_inverted = scaler.inverse_transform(preds)\n",
" y_test_inverted = scaler.inverse_transform(y_test.reshape(-1, 1))\n",
"\n",
" # Evaluate\n",
" mae = mean_absolute_error(y_test_inverted, preds_inverted)\n",
" rmse = np.sqrt(mean_squared_error(y_test_inverted, preds_inverted))\n",
"\n",
" fold_results.append({\n",
" 'fold': fold_idx + 1,\n",
" 'mae': mae,\n",
" 'rmse': rmse,\n",
" 'preds': preds_inverted.flatten(),\n",
" 'y_test': y_test_inverted.flatten(),\n",
" 'df_train': df_train,\n",
" 'df_test': df_test\n",
" })\n",
"\n",
" # Move start index for next fold\n",
" start_idx = train_end\n",
"\n",
" return fold_results\n",
"\n",
"###############################################################################\n",
"# 2) DATA LOADING & PREPARATION\n",
"###############################################################################\n",
"if not mt5.initialize():\n",
" print(\"Failed to initialize MT5\")\n",
"else:\n",
" data = get_data_mt5(symbol=\"BTCUSD\", timeframe=mt5.TIMEFRAME_H1, n_bars=1000)\n",
" mt5.shutdown()\n",
"\n",
"df = add_all_ta_features(data).dropna() # Apply technical indicators\n",
"\n",
"print(\"Available columns:\", df.columns)\n",
"\n",
"# Here, we define df_selected so that we have a DataFrame with at least 'close'.\n",
"# If you want multiple features, e.g. RSI, MACD, etc., add them here.\n",
"df_selected = df[['close']].copy()\n",
"\n",
"###############################################################################\n",
"# 3) RUN THE WALK-FORWARD LSTM\n",
"###############################################################################\n",
"n_splits = 3\n",
"lookback = 10\n",
"horizon = 50\n",
"epochs = 10\n",
"batch_size = 16\n",
"\n",
"fold_results = lstm_walk_forward(\n",
" df_selected, \n",
" n_splits=n_splits, \n",
" lookback=lookback, \n",
" horizon=horizon, \n",
" epochs=epochs, \n",
" batch_size=batch_size\n",
")\n",
"\n",
"###############################################################################\n",
"# 4) EVALUATE & PLOT RESULTS\n",
"###############################################################################\n",
"for result in fold_results:\n",
" fold_num = result['fold']\n",
" mae = result['mae']\n",
" rmse = result['rmse']\n",
" preds = result['preds']\n",
" actual = result['y_test']\n",
" df_train = result['df_train']\n",
" df_test = result['df_test']\n",
"\n",
" print(f\"=== Fold {fold_num} ===\")\n",
" print(f\"MAE = {mae:.4f}, RMSE = {rmse:.4f}\")\n",
"\n",
" # For plotting, note that the test sequences start after 'lookback'\n",
" test_index = df_test.index[lookback:]\n",
"\n",
" fig = go.Figure()\n",
" fig.add_trace(go.Scatter(\n",
" x=df_train.index,\n",
" y=df_train['close'],\n",
" name=\"Train Data\",\n",
" line=dict(color=\"blue\")\n",
" ))\n",
" fig.add_trace(go.Scatter(\n",
" x=df_test.index,\n",
" y=df_test['close'],\n",
" name=\"Test Data\",\n",
" line=dict(color=\"gray\")\n",
" ))\n",
" fig.add_trace(go.Scatter(\n",
" x=test_index,\n",
" y=preds,\n",
" name=\"LSTM Forecast\",\n",
" line=dict(color=\"orange\")\n",
" ))\n",
" fig.update_layout(\n",
" title=f\"LSTM Forecast - Fold {fold_num}\",\n",
" xaxis_title=\"Time\",\n",
" yaxis_title=\"Price\"\n",
" )\n",
" fig.show()\n"
]
},
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"source": [
"## Part 2: Model Training & Hyperparameter Tuning\n",
"\n",
"**Objective:** \n",
"Train an ML model (e.g., RandomForest, XGBoost) on the engineered features to predict the chosen labels.\n",
"\n",
"**Tasks:**\n",
"- Perform time-based or walk-forward splits \n",
"- Select top features if desired (e.g., using RandomForest feature importance) \n",
"- Use `RandomizedSearchCV` or `GridSearchCV` to find optimal hyperparameters \n",
"- Save the best model pipeline (e.g., `best_rf_pipeline.pkl`) "
]
},
{
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"id": "652ed8ac",
"metadata": {},
"source": [
"1) Imports & Basic Setup"
]
},
{
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"execution_count": null,
"id": "a75e974f",
"metadata": {},
"outputs": [
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"name": "stderr",
"output_type": "stream",
"text": [
"[I 2025-02-28 11:47:37,989] A new study created in memory with name: no-name-bbe792a4-92d5-475a-b71d-82eec3e015d2\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Columns after TA features: ['open', 'high', 'low', 'close', 'tick_volume', 'spread', 'real_volume', 'volume_adi', 'volume_obv', 'volume_cmf', 'volume_fi', 'volume_em', 'volume_sma_em', 'volume_vpt', 'volume_vwap', 'volume_mfi', 'volume_nvi', 'volatility_bbm', 'volatility_bbh', 'volatility_bbl', 'volatility_bbw', 'volatility_bbp', 'volatility_bbhi', 'volatility_bbli', 'volatility_kcc', 'volatility_kch', 'volatility_kcl', 'volatility_kcw', 'volatility_kcp', 'volatility_kchi', 'volatility_kcli', 'volatility_dcl', 'volatility_dch', 'volatility_dcm', 'volatility_dcw', 'volatility_dcp', 'volatility_atr', 'volatility_ui', 'trend_macd', 'trend_macd_signal', 'trend_macd_diff', 'trend_sma_fast', 'trend_sma_slow', 'trend_ema_fast', 'trend_ema_slow', 'trend_vortex_ind_pos', 'trend_vortex_ind_neg', 'trend_vortex_ind_diff', 'trend_trix', 'trend_mass_index', 'trend_dpo', 'trend_kst', 'trend_kst_sig', 'trend_kst_diff', 'trend_ichimoku_conv', 'trend_ichimoku_base', 'trend_ichimoku_a', 'trend_ichimoku_b', 'trend_stc', 'trend_adx', 'trend_adx_pos', 'trend_adx_neg', 'trend_cci', 'trend_visual_ichimoku_a', 'trend_visual_ichimoku_b', 'trend_aroon_up', 'trend_aroon_down', 'trend_aroon_ind', 'trend_psar_up', 'trend_psar_down', 'trend_psar_up_indicator', 'trend_psar_down_indicator', 'momentum_rsi', 'momentum_stoch_rsi', 'momentum_stoch_rsi_k', 'momentum_stoch_rsi_d', 'momentum_tsi', 'momentum_uo', 'momentum_stoch', 'momentum_stoch_signal', 'momentum_wr', 'momentum_ao', 'momentum_roc', 'momentum_ppo', 'momentum_ppo_signal', 'momentum_ppo_hist', 'momentum_pvo', 'momentum_pvo_signal', 'momentum_pvo_hist', 'momentum_kama', 'others_dr', 'others_dlr', 'others_cr']\n",
"df_selected shape: (5000, 1)\n",
"WARNING:tensorflow:5 out of the last 5 calls to .one_step_on_data_distributed at 0x000001BED5910AE0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n",
"WARNING:tensorflow:6 out of the last 6 calls to .one_step_on_data_distributed at 0x000001BED5910AE0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[I 2025-02-28 11:50:00,691] Trial 0 finished with value: 3732335.848029452 and parameters: {'n_units': 128, 'dropout_rate': 0.4, 'learning_rate': 0.00048188398102667944, 'epochs': 30, 'batch_size': 64, 'lookback': 10}. Best is trial 0 with value: 3732335.848029452.\n",
"[I 2025-02-28 11:52:45,758] Trial 1 finished with value: 1182089.3237289798 and parameters: {'n_units': 128, 'dropout_rate': 0.2, 'learning_rate': 0.0011847001103633932, 'epochs': 40, 'batch_size': 48, 'lookback': 5}. Best is trial 1 with value: 1182089.3237289798.\n",
"[I 2025-02-28 12:00:06,491] Trial 2 finished with value: 2140429.6150380843 and parameters: {'n_units': 96, 'dropout_rate': 0.5, 'learning_rate': 0.0015224751735208528, 'epochs': 30, 'batch_size': 16, 'lookback': 15}. Best is trial 1 with value: 1182089.3237289798.\n",
"[I 2025-02-28 12:04:01,453] Trial 3 finished with value: 2478006.355463989 and parameters: {'n_units': 32, 'dropout_rate': 0.4, 'learning_rate': 0.005093204429337122, 'epochs': 40, 'batch_size': 48, 'lookback': 25}. Best is trial 1 with value: 1182089.3237289798.\n",
"[I 2025-02-28 12:06:41,118] Trial 4 finished with value: 1419328.4148156133 and parameters: {'n_units': 128, 'dropout_rate': 0.1, 'learning_rate': 0.002512372832711921, 'epochs': 20, 'batch_size': 16, 'lookback': 5}. Best is trial 1 with value: 1182089.3237289798.\n",
"[I 2025-02-28 12:07:29,158] Trial 5 finished with value: 3124320.3358980156 and parameters: {'n_units': 64, 'dropout_rate': 0.4, 'learning_rate': 0.00037970512274111717, 'epochs': 10, 'batch_size': 48, 'lookback': 15}. Best is trial 1 with value: 1182089.3237289798.\n",
"[I 2025-02-28 12:10:13,491] Trial 6 finished with value: 932777.854093264 and parameters: {'n_units': 128, 'dropout_rate': 0.1, 'learning_rate': 0.0013997406651546764, 'epochs': 20, 'batch_size': 16, 'lookback': 10}. Best is trial 6 with value: 932777.854093264.\n",
"[I 2025-02-28 12:11:41,886] Trial 7 finished with value: 2369984.3410976976 and parameters: {'n_units': 64, 'dropout_rate': 0.1, 'learning_rate': 0.004873065651776751, 'epochs': 10, 'batch_size': 32, 'lookback': 30}. Best is trial 6 with value: 932777.854093264.\n",
"[I 2025-02-28 12:17:48,683] Trial 8 finished with value: 3631521.0792305246 and parameters: {'n_units': 96, 'dropout_rate': 0.1, 'learning_rate': 0.00029465621059972415, 'epochs': 50, 'batch_size': 64, 'lookback': 30}. Best is trial 6 with value: 932777.854093264.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Best Trial:\n",
" Value (MSE): 932777.854093264\n",
" Params: {'n_units': 128, 'dropout_rate': 0.1, 'learning_rate': 0.0013997406651546764, 'epochs': 20, 'batch_size': 16, 'lookback': 10}\n",
"\n",
"Walk-Forward Performance with Best Hyperparams: Avg RMSE = 1161.4837\n",
"\n",
"=== Fold 1 ===\n",
"MAE = 165.3904, RMSE = 201.7328\n"
]
},
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"showbackground": true,
"ticks": "",
"zerolinecolor": "white"
},
"zaxis": {
"backgroundcolor": "#E5ECF6",
"gridcolor": "white",
"gridwidth": 2,
"linecolor": "white",
"showbackground": true,
"ticks": "",
"zerolinecolor": "white"
}
},
"shapedefaults": {
"line": {
"color": "#2a3f5f"
}
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"ternary": {
"aaxis": {
"gridcolor": "white",
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"ticks": ""
},
"baxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
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"bgcolor": "#E5ECF6",
"caxis": {
"gridcolor": "white",
"linecolor": "white",
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},
"title": {
"x": 0.05
},
"xaxis": {
"automargin": true,
"gridcolor": "white",
"linecolor": "white",
"ticks": "",
"title": {
"standoff": 15
},
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}
},
"title": {
"text": "LSTM Forecast - Fold 3"
},
"xaxis": {
"title": {
"text": "Time"
}
},
"yaxis": {
"title": {
"text": "Price"
}
}
}
}
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/20\n",
"\u001b[1m312/312\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 13ms/step - loss: 0.0069\n",
"Epoch 2/20\n",
"\u001b[1m312/312\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 15ms/step - loss: 3.0551e-04\n",
"Epoch 3/20\n",
"\u001b[1m312/312\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 17ms/step - loss: 2.7914e-04\n",
"Epoch 4/20\n",
"\u001b[1m312/312\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 15ms/step - loss: 3.1685e-04\n",
"Epoch 5/20\n",
"\u001b[1m312/312\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 16ms/step - loss: 2.3898e-04\n",
"Epoch 6/20\n",
"\u001b[1m312/312\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 14ms/step - loss: 1.9749e-04\n",
"Epoch 7/20\n",
"\u001b[1m312/312\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 14ms/step - loss: 1.9419e-04\n",
"Epoch 8/20\n",
"\u001b[1m312/312\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 13ms/step - loss: 2.2309e-04\n",
"Epoch 9/20\n",
"\u001b[1m312/312\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 13ms/step - loss: 2.3012e-04\n",
"Epoch 10/20\n",
"\u001b[1m312/312\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 14ms/step - loss: 2.0354e-04\n",
"Epoch 11/20\n",
"\u001b[1m312/312\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 14ms/step - loss: 2.4281e-04\n",
"Epoch 12/20\n",
"\u001b[1m312/312\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 14ms/step - loss: 1.6599e-04\n",
"Epoch 13/20\n",
"\u001b[1m312/312\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 14ms/step - loss: 2.1034e-04\n",
"Epoch 14/20\n",
"\u001b[1m312/312\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 13ms/step - loss: 1.3689e-04\n",
"Epoch 15/20\n",
"\u001b[1m312/312\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 14ms/step - loss: 1.7485e-04\n",
"Epoch 16/20\n",
"\u001b[1m312/312\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 14ms/step - loss: 1.2682e-04\n",
"Epoch 17/20\n",
"\u001b[1m312/312\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 14ms/step - loss: 1.4354e-04\n",
"Epoch 18/20\n",
"\u001b[1m312/312\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 14ms/step - loss: 1.2179e-04\n",
"Epoch 19/20\n",
"\u001b[1m312/312\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 14ms/step - loss: 1.3459e-04\n",
"Epoch 20/20\n",
"\u001b[1m312/312\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 14ms/step - loss: 1.2250e-04\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Saved best LSTM model to 'models/saved_models/best_timeseries_lstm_model.h5'\n",
"Saved MinMaxScaler to 'models/saved_models/minmax_scaler.pkl'\n"
]
}
],
"source": [
"import sys\n",
"import os\n",
"import warnings\n",
"from pathlib import Path\n",
"\n",
"# ---------------------------------------------------------------------------\n",
"# 1) SET PROJECT ROOT AND UPDATE PATH/WORKING DIRECTORY\n",
"# ---------------------------------------------------------------------------\n",
"project_root = Path.cwd().parent.parent # Adjust if your notebook is in notebooks/time_series\n",
"sys.path.append(str(project_root))\n",
"os.chdir(str(project_root))\n",
"warnings.filterwarnings(\"ignore\")\n",
"\n",
"import numpy as np\n",
"import pandas as pd\n",
"import MetaTrader5 as mt5\n",
"import optuna\n",
"import joblib\n",
"\n",
"# Plotly for charting (optional)\n",
"import plotly.graph_objects as go\n",
"\n",
"# TensorFlow / Keras\n",
"from tensorflow.keras.models import Sequential, load_model\n",
"from tensorflow.keras.layers import LSTM, Dense, Dropout\n",
"from tensorflow.keras.optimizers import Adam\n",
"\n",
"# Sklearn\n",
"from sklearn.preprocessing import MinMaxScaler\n",
"from sklearn.metrics import mean_absolute_error, mean_squared_error\n",
"\n",
"# Custom Modules\n",
"from data.data_loader import get_data_mt5\n",
"from features.feature_engineering import add_all_ta_features\n",
"\n",
"###############################################################################\n",
"# 2) DATA LOADING & FEATURE ENGINEERING\n",
"###############################################################################\n",
"if not mt5.initialize():\n",
" print(\"Failed to initialize MT5\")\n",
"else:\n",
" data = get_data_mt5(symbol=\"BTCUSD\", timeframe=mt5.TIMEFRAME_H4, n_bars=2000, start_pos=2000)\n",
" mt5.shutdown()\n",
"\n",
"df = add_all_ta_features(data).dropna()\n",
"print(\"Columns after TA features:\", df.columns.tolist())\n",
"\n",
"# Keep just 'close' for demonstration (add more columns if desired)\n",
"df_selected = df[['close']].copy()\n",
"print(\"df_selected shape:\", df_selected.shape)\n",
"\n",
"###############################################################################\n",
"# 3) HELPER FUNCTIONS\n",
"###############################################################################\n",
"def create_sequences(data, lookback=10):\n",
" \"\"\"\n",
" Convert 1D or 2D numpy array into LSTM sequences of shape:\n",
" (num_sequences, lookback, num_features)\n",
" \"\"\"\n",
" X, y = [], []\n",
" for i in range(len(data) - lookback):\n",
" X.append(data[i : i + lookback])\n",
" y.append(data[i + lookback])\n",
" return np.array(X), np.array(y)\n",
"\n",
"def lstm_walk_forward(\n",
" df, \n",
" n_splits=3, \n",
" lookback=10, \n",
" horizon=50, \n",
" epochs=20, \n",
" batch_size=16,\n",
" n_units=50,\n",
" dropout_rate=0.2,\n",
" learning_rate=1e-3\n",
"):\n",
" \"\"\"\n",
" Perform walk-forward validation with an LSTM model.\n",
" Returns a list of dicts, each containing fold results (MAE, RMSE, etc.).\n",
" \"\"\"\n",
" total_length = len(df)\n",
" fold_size = (total_length - horizon) // n_splits\n",
" fold_results = []\n",
" start_idx = 0\n",
"\n",
" for fold_idx in range(n_splits):\n",
" # Slice data for this fold\n",
" train_end = start_idx + fold_size\n",
" if fold_idx == n_splits - 1:\n",
" train_end = total_length - horizon\n",
" test_end = train_end + horizon\n",
"\n",
" df_train = df.iloc[:train_end]\n",
" df_test = df.iloc[train_end:test_end]\n",
"\n",
" # Scale train/test separately\n",
" scaler = MinMaxScaler()\n",
" train_scaled = scaler.fit_transform(df_train[['close']])\n",
" test_scaled = scaler.transform(df_test[['close']])\n",
"\n",
" # Create sequences for training\n",
" X_train, y_train = create_sequences(train_scaled, lookback)\n",
"\n",
" # Create sequences for testing\n",
" last_train_part = train_scaled[-lookback:]\n",
" test_input = np.concatenate([last_train_part, test_scaled], axis=0)\n",
" X_test, y_test = create_sequences(test_input, lookback)\n",
"\n",
" # Build the LSTM model\n",
" model = Sequential([\n",
" LSTM(n_units, return_sequences=True, input_shape=(lookback, 1)),\n",
" Dropout(dropout_rate),\n",
" LSTM(n_units),\n",
" Dense(1)\n",
" ])\n",
" optimizer = Adam(learning_rate=learning_rate)\n",
" model.compile(loss=\"mse\", optimizer=optimizer)\n",
"\n",
" # Train\n",
" model.fit(X_train, y_train, epochs=epochs, batch_size=batch_size, verbose=0)\n",
"\n",
" # Predict\n",
" preds = model.predict(X_test, verbose=0)\n",
" preds_inverted = scaler.inverse_transform(preds)\n",
" y_test_inverted = scaler.inverse_transform(y_test.reshape(-1, 1))\n",
"\n",
" # Evaluate\n",
" mae = mean_absolute_error(y_test_inverted, preds_inverted)\n",
" rmse = np.sqrt(mean_squared_error(y_test_inverted, preds_inverted))\n",
"\n",
" fold_results.append({\n",
" 'fold': fold_idx + 1,\n",
" 'mae': mae,\n",
" 'rmse': rmse,\n",
" 'preds': preds_inverted.flatten(),\n",
" 'y_test': y_test_inverted.flatten(),\n",
" 'df_train': df_train,\n",
" 'df_test': df_test\n",
" })\n",
"\n",
" start_idx = train_end\n",
"\n",
" return fold_results\n",
"\n",
"def build_lstm_model(n_units, dropout_rate, learning_rate, lookback):\n",
" \"\"\"\n",
" Build a final LSTM model for training on the entire dataset\n",
" after finding best hyperparams.\n",
" \"\"\"\n",
" model = Sequential([\n",
" LSTM(n_units, return_sequences=True, input_shape=(lookback, 1)),\n",
" Dropout(dropout_rate),\n",
" LSTM(n_units),\n",
" Dense(1)\n",
" ])\n",
" optimizer = Adam(learning_rate=learning_rate)\n",
" model.compile(loss=\"mse\", optimizer=optimizer)\n",
" return model\n",
"\n",
"###############################################################################\n",
"# 4) OPTUNA OBJECTIVE FUNCTION\n",
"###############################################################################\n",
"def objective(trial):\n",
" \"\"\"\n",
" Optuna objective: sample hyperparams, run walk-forward, return average MSE.\n",
" \"\"\"\n",
" n_units = trial.suggest_int(\"n_units\", 32, 128, step=32)\n",
" dropout_rate = trial.suggest_float(\"dropout_rate\", 0.0, 0.5, step=0.1)\n",
" learning_rate = trial.suggest_float(\"learning_rate\", 1e-4, 1e-2, log=True)\n",
" epochs = trial.suggest_int(\"epochs\", 10, 50, step=10)\n",
" batch_size = trial.suggest_int(\"batch_size\", 16, 64, step=16)\n",
" lookback = trial.suggest_int(\"lookback\", 5, 30, step=5)\n",
"\n",
" fold_results = lstm_walk_forward(\n",
" df_selected,\n",
" n_splits=3,\n",
" lookback=lookback,\n",
" horizon=50,\n",
" epochs=epochs,\n",
" batch_size=batch_size,\n",
" n_units=n_units,\n",
" dropout_rate=dropout_rate,\n",
" learning_rate=learning_rate\n",
" )\n",
"\n",
" # Average MSE across folds\n",
" mse_list = []\n",
" for fr in fold_results:\n",
" rmse = fr['rmse']\n",
" mse_list.append(rmse**2) # RMSE^2 = MSE\n",
" avg_mse = np.mean(mse_list)\n",
"\n",
" return avg_mse # Optuna will minimize this\n",
"\n",
"###############################################################################\n",
"# 5) RUN OPTUNA STUDY\n",
"###############################################################################\n",
"study = optuna.create_study(direction=\"minimize\")\n",
"study.optimize(objective, n_trials=10, timeout=1800) # e.g., 10 trials or 30 min\n",
"\n",
"print(\"\\nBest Trial:\")\n",
"print(\" Value (MSE):\", study.best_trial.value)\n",
"print(\" Params:\", study.best_trial.params)\n",
"\n",
"best_params = study.best_trial.params\n",
"\n",
"###############################################################################\n",
"# 6) EVALUATE BEST PARAMS WITH WALK-FORWARD & PRINT/SHOW PLOTS\n",
"###############################################################################\n",
"best_fold_results = lstm_walk_forward(\n",
" df_selected,\n",
" n_splits=3,\n",
" lookback=best_params['lookback'],\n",
" horizon=50,\n",
" epochs=best_params['epochs'],\n",
" batch_size=best_params['batch_size'],\n",
" n_units=best_params['n_units'],\n",
" dropout_rate=best_params['dropout_rate'],\n",
" learning_rate=best_params['learning_rate']\n",
")\n",
"\n",
"# Print final performance across folds\n",
"rmse_list = [fr['rmse'] for fr in best_fold_results]\n",
"avg_rmse = np.mean(rmse_list)\n",
"print(f\"\\nWalk-Forward Performance with Best Hyperparams: Avg RMSE = {avg_rmse:.4f}\")\n",
"\n",
"# (Optional) Plot each fold's predictions\n",
"for result in best_fold_results:\n",
" fold_num = result['fold']\n",
" mae = result['mae']\n",
" rmse = result['rmse']\n",
" preds = result['preds']\n",
" actual = result['y_test']\n",
" df_train = result['df_train']\n",
" df_test = result['df_test']\n",
"\n",
" print(f\"\\n=== Fold {fold_num} ===\")\n",
" print(f\"MAE = {mae:.4f}, RMSE = {rmse:.4f}\")\n",
"\n",
" # Indices for plotting\n",
" test_index = df_test.index[best_params['lookback']:] # shift by lookback\n",
"\n",
" fig = go.Figure()\n",
" fig.add_trace(go.Scatter(\n",
" x=df_train.index,\n",
" y=df_train['close'],\n",
" name=\"Train Data\",\n",
" line=dict(color=\"blue\")\n",
" ))\n",
" fig.add_trace(go.Scatter(\n",
" x=df_test.index,\n",
" y=df_test['close'],\n",
" name=\"Test Data\",\n",
" line=dict(color=\"gray\")\n",
" ))\n",
" fig.add_trace(go.Scatter(\n",
" x=test_index,\n",
" y=preds,\n",
" name=\"LSTM Forecast\",\n",
" line=dict(color=\"orange\")\n",
" ))\n",
" fig.update_layout(\n",
" title=f\"LSTM Forecast - Fold {fold_num}\",\n",
" xaxis_title=\"Time\",\n",
" yaxis_title=\"Price\"\n",
" )\n",
" fig.show()\n",
"\n",
"###############################################################################\n",
"# 7) RETRAIN FINAL MODEL ON ENTIRE DATA & SAVE\n",
"###############################################################################\n",
"# 1) Scale entire dataset with a fresh scaler\n",
"scaler = MinMaxScaler()\n",
"df_scaled = scaler.fit_transform(df_selected[['close']])\n",
"\n",
"# 2) Create sequences for the entire dataset\n",
"X_full, y_full = create_sequences(df_scaled, lookback=best_params['lookback'])\n",
"\n",
"# 3) Build the final LSTM with best hyperparams\n",
"final_model = build_lstm_model(\n",
" n_units=best_params['n_units'],\n",
" dropout_rate=best_params['dropout_rate'],\n",
" learning_rate=best_params['learning_rate'],\n",
" lookback=best_params['lookback']\n",
")\n",
"\n",
"# 4) Train on entire dataset\n",
"final_model.fit(X_full, y_full, epochs=best_params['epochs'],\n",
" batch_size=best_params['batch_size'], verbose=1)\n",
"\n",
"# 5) Save model and scaler\n",
"model_path = \"models/saved_models/best_timeseries_lstm_model.h5\"\n",
"final_model.save(model_path)\n",
"print(f\"Saved best LSTM model to '{model_path}'\")\n",
"\n",
"scaler_path = \"models/saved_models/minmax_scaler.pkl\"\n",
"joblib.dump(scaler, scaler_path)\n",
"print(f\"Saved MinMaxScaler to '{scaler_path}'\")\n"
]
},
{
"cell_type": "markdown",
"id": "083aed71",
"metadata": {},
"source": [
"## Part 3: Backtesting & Performance Evaluation\n",
"\n",
"**Objective:** \n",
"Evaluate how well the trained model performs on unseen data, simulating real trades.\n",
"\n",
"**Tasks:**\n",
"- Use walk-forward or expanding splits to mimic “live” conditions \n",
"- Convert model predictions to signals ([-1, 0, +1] or buy/sell/hold) \n",
"- Run a simple backtest script or VectorBT for performance metrics \n",
"- Calculate returns, Sharpe ratio, drawdowns, confusion matrix, etc. \n",
"- Visualize results (equity curve, trades, etc.) to judge strategy viability "
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b0ab0198",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"WARNING:absl:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"DataFrame shape: (2000, 1)\n",
" close\n",
"time \n",
"2024-04-03 12:00:00 65929.59\n",
"2024-04-03 16:00:00 66195.94\n",
"2024-04-03 20:00:00 65715.27\n",
"2024-04-04 00:00:00 66304.94\n",
"2024-04-04 04:00:00 65476.41\n",
"Loaded model from 'models/saved_models/best_timeseries_lstm_model.h5'\n",
"Loaded scaler from 'models/saved_models/minmax_scaler.pkl'\n",
"\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step\n",
"\n",
"=== Running Full Backtest on Entire Dataset ===\n",
"\n",
"Full Backtest RMSE=1438.10, Return=8.85%, Sharpe=0.44\n",
"\n",
"Vectorbt Full Dataset Stats:\n",
"Start 2024-04-05 04:00:00\n",
"End 2025-03-02 16:00:00\n",
"Period 331 days 16:00:00\n",
"Start Value 10000.0\n",
"End Value 10885.263431\n",
"Total Return [%] 8.852634\n",
"Benchmark Return [%] 25.862688\n",
"Max Gross Exposure [%] 100.0\n",
"Total Fees Paid 361.693673\n",
"Max Drawdown [%] 35.698673\n",
"Max Drawdown Duration 218 days 20:00:00\n",
"Total Trades 90\n",
"Total Closed Trades 89\n",
"Total Open Trades 1\n",
"Open Trade PnL -25.144702\n",
"Win Rate [%] 66.292135\n",
"Best Trade [%] 6.728806\n",
"Worst Trade [%] -19.44662\n",
"Avg Winning Trade [%] 2.426562\n",
"Avg Losing Trade [%] -4.205857\n",
"Avg Winning Trade Duration 2 days 01:21:21.355932203\n",
"Avg Losing Trade Duration 6 days 02:08:00\n",
"Profit Factor 1.069433\n",
"Expectancy 10.229305\n",
"Sharpe Ratio 0.436169\n",
"Calmar Ratio 0.274088\n",
"Omega Ratio 1.029857\n",
"Sortino Ratio 0.599174\n",
"dtype: object\n"
]
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"import warnings\n",
"warnings.filterwarnings(\"ignore\")\n",
"\n",
"import sys\n",
"import os\n",
"from pathlib import Path\n",
"\n",
"import numpy as np\n",
"import pandas as pd\n",
"import MetaTrader5 as mt5\n",
"import vectorbt as vbt\n",
"import joblib\n",
"\n",
"import tensorflow as tf\n",
"from tensorflow.keras.models import load_model\n",
"from tensorflow.keras.optimizers import Adam\n",
"from sklearn.metrics import mean_squared_error\n",
"\n",
"# If your saved model used \"mse\" as a loss, map it to MeanSquaredError:\n",
"custom_objs = {\"mse\": tf.keras.losses.MeanSquaredError()}\n",
"\n",
"###############################################################################\n",
"# 1) PROJECT SETUP\n",
"###############################################################################\n",
"project_root = Path.cwd().parent.parent # Adjust if needed\n",
"sys.path.append(str(project_root))\n",
"os.chdir(str(project_root))\n",
"warnings.filterwarnings(\"ignore\")\n",
"\n",
"# Custom modules\n",
"from data.data_loader import get_data_mt5\n",
"from features.feature_engineering import add_all_ta_features\n",
"\n",
"###############################################################################\n",
"# 2) DATA LOADING & PREPARATION\n",
"###############################################################################\n",
"if not mt5.initialize():\n",
" print(\"Failed to initialize MT5\")\n",
"else:\n",
" data = get_data_mt5(symbol=\"BTCUSD\", timeframe=mt5.TIMEFRAME_H4, n_bars=2000, start_pos=0)\n",
" mt5.shutdown()\n",
"\n",
"df = add_all_ta_features(data).dropna()\n",
"df.sort_index(inplace=True) # Ensure chronological order\n",
"\n",
"# We'll forecast price, so we keep just 'close' as the target\n",
"df_selected = df[['close']].copy()\n",
"\n",
"print(\"DataFrame shape:\", df_selected.shape)\n",
"print(df_selected.head())\n",
"\n",
"###############################################################################\n",
"# 3) LOAD MODEL & SCALER (NO RE-FIT)\n",
"###############################################################################\n",
"model_path = \"models/saved_models/best_timeseries_lstm_model.h5\"\n",
"scaler_path = \"models/saved_models/minmax_scaler.pkl\"\n",
"\n",
"# Load the trained LSTM model\n",
"best_model = load_model(model_path, custom_objects=custom_objs)\n",
"print(f\"Loaded model from '{model_path}'\")\n",
"\n",
"# (Re-)compile if needed\n",
"best_model.compile(optimizer=Adam(learning_rate=0.001), loss=\"mean_squared_error\")\n",
"\n",
"# Load the MinMaxScaler that was previously fitted on training data\n",
"scaler = joblib.load(scaler_path)\n",
"print(f\"Loaded scaler from '{scaler_path}'\")\n",
"\n",
"###############################################################################\n",
"# 4) BACKTEST PARAMETERS\n",
"###############################################################################\n",
"lookback = 10 # Must match what was used during training\n",
"threshold = 0.002 # e.g. 0.2% implied return threshold\n",
"fees = 0.0002 # e.g. 0.02% transaction cost per trade\n",
"execution_lag = 1 # trade on the NEXT bar to avoid look-ahead (set 0 for same-bar)\n",
"\n",
"###############################################################################\n",
"# 5) HELPER: CREATE SEQUENCES FOR INFERENCE\n",
"###############################################################################\n",
"def create_sequences(arr, lookback):\n",
" \"\"\"Convert data into overlapping lookback sequences for LSTM.\"\"\"\n",
" X_seq = []\n",
" for i in range(lookback, len(arr)):\n",
" X_seq.append(arr[i - lookback : i])\n",
" return np.array(X_seq)\n",
"\n",
"###############################################################################\n",
"# 6) PREPARE FULL DATASET FOR BACKTESTING (TRANSFORM ONLY)\n",
"###############################################################################\n",
"# IMPORTANT: Use scaler.transform() instead of scaler.fit_transform()\n",
"df_scaled = scaler.transform(df_selected[['close']])\n",
"\n",
"# Create sequences from the full dataset\n",
"X_full_seq = create_sequences(df_scaled, lookback)\n",
"\n",
"# Predict using the entire dataset\n",
"preds_scaled = best_model.predict(X_full_seq, verbose=0)\n",
"\n",
"# Convert scaled predictions back to original price domain\n",
"preds_inverted = scaler.inverse_transform(preds_scaled)\n",
"\n",
"# Index that corresponds to the predictions\n",
"test_index = df_selected.index[lookback:]\n",
"\n",
"# Ensure alignment (trim if any slight length mismatch)\n",
"n = min(len(test_index), len(preds_inverted))\n",
"test_index = test_index[:n]\n",
"preds_inverted = preds_inverted[:n].reshape(-1)\n",
"\n",
"# Actual prices aligned to prediction rows\n",
"actual_prices = df_selected['close'].reindex(test_index).values\n",
"\n",
"# Compute implied returns from predicted price vs actual price\n",
"implied_returns = (preds_inverted - actual_prices) / actual_prices\n",
"\n",
"# Compute RMSE of price forecasts\n",
"rmse = np.sqrt(mean_squared_error(actual_prices, preds_inverted))\n",
"\n",
"###############################################################################\n",
"# 7) BACKTEST WITH VECTORBT (TARGET EXPOSURE: -1, 0, +1)\n",
"###############################################################################\n",
"print(\"\\n=== Running Full Backtest on Entire Dataset ===\")\n",
"\n",
"# Convert returns to exposure: Long (+1), Short (-1), Flat (0)\n",
"exposure = np.where(implied_returns > threshold, 1.0,\n",
" np.where(implied_returns < -threshold, -1.0, 0.0)).astype(float)\n",
"\n",
"# Align exposure with price series; optional next-bar execution to avoid look-ahead\n",
"close = df_selected['close'].reindex(test_index)\n",
"exposure_s = pd.Series(exposure, index=test_index)\n",
"if execution_lag > 0:\n",
" exposure_s = exposure_s.shift(execution_lag).fillna(0.0)\n",
"\n",
"pf = vbt.Portfolio.from_orders(\n",
" close=close,\n",
" size=exposure_s, # -1 short, 0 flat, +1 long\n",
" size_type='targetpercent',\n",
" init_cash=10000,\n",
" freq='4H',\n",
" fees=fees\n",
")\n",
"\n",
"###############################################################################\n",
"# 8) PRINT PERFORMANCE METRICS\n",
"###############################################################################\n",
"total_return = pf.total_return()\n",
"sharpe_ratio = pf.sharpe_ratio()\n",
"\n",
"print(f\"\\nFull Backtest RMSE={rmse:.4f}, Return={total_return:.2%}, Sharpe={sharpe_ratio:.2f}\")\n",
"print(\"\\nVectorbt Full Dataset Stats:\")\n",
"print(pf.stats())\n",
"\n",
"# Plot\n",
"fig = pf.plot()\n",
"fig.update_layout(title=\"Full Dataset Backtest (LSTM Price Forecasting)\")\n",
"fig.show()\n"
]
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