From bc0716cc537a93fe6c745899d36be4184b3fed66 Mon Sep 17 00:00:00 2001 From: medusa Date: Sun, 18 Feb 2024 14:02:59 +0000 Subject: [PATCH] Update projects/forex_algo_trading.md --- projects/forex_algo_trading.md | 84 +++++++++++++++++++++++++++------- 1 file changed, 67 insertions(+), 17 deletions(-) diff --git a/projects/forex_algo_trading.md b/projects/forex_algo_trading.md index f2ceb22..2374f08 100644 --- a/projects/forex_algo_trading.md +++ b/projects/forex_algo_trading.md @@ -34,24 +34,74 @@ * **Market sentiment:** News analysis, social media data. * Utilize feature selection methods like PCA or LASSO. -* **Step 2: Model Building and Training (Scikit-learn)** - * Choose appropriate algorithms based on the target variable (e.g., price prediction, trend classification). - * Experiment with models like: - * **Regression:** Linear Regression, Random Forest, Support Vector Regression. - * **Classification:** Logistic Regression, Decision Trees, Neural Networks (with caution). - * Train models on the prepared data, considering hyperparameter tuning. - * Evaluate model performance using metrics like accuracy, precision, and recall. +## Step 2: Model Building and Training (Scikit-learn) -* **Step 3: Strategy Implementation and Backtesting (Backtrader)** - * Translate model predictions into trading signals (e.g., buy/sell thresholds). - * Implement your strategy in Backtrader using a Python class. - * Define entry, exit, and position management rules. - * Account for: - * **Risk management:** Stop-loss, take-profit orders. - * **Transaction costs:** Commissions, slippage. - * Backtest the strategy on historical data, analyzing: - * **Performance metrics:** Profit, loss, Sharpe ratio, drawdown. - * **Robustness:** Walk-forward testing for unseen data. +**Example 1: Predicting Future Closing Price** + +* **Target variable:** Continuous future closing price of a specific asset. +* **Candidate models:** + * **Linear Regression:** Simple baseline for linear relationships, but may struggle with non-linearities. + * **Random Forest Regression:** Handles complex relationships well, but prone to overfitting. + * **Support Vector Regression (SVR):** Identifies support and resistance levels, but sensitive to outliers. + * **Long Short-Term Memory (LSTM):** Deep learning model capturing temporal dependencies, but requires more data and computational resources. +* **Features:** + * **Technical indicators:** Moving averages, RSI, MACD, Bollinger Bands (consider normalization). + * **Lagged features:** Past closing prices, volume, volatility (e.g., ATR). + * **Market data:** Sector performance, interest rates, economic indicators (if relevant). +* **Feature engineering:** + * Create new features like momentum indicators, price ratios, or technical indicator derivatives. + * Consider dimensionality reduction techniques (e.g., PCA) to avoid overfitting. +* **Hyperparameter tuning:** + * Tune regularization parameters for SVR, number of trees and max depth for Random Forest, and LSTM hyperparameters carefully. +* **Evaluation metrics:** + * **Mean Squared Error (MSE):** Sensitive to outliers, use for interpretability. + * **Mean Absolute Error (MAE):** Less sensitive to outliers, good for general performance. + * **R-squared:** Proportion of variance explained, but can be misleading for non-linear models. + * **Consider additional metrics:** Sharpe ratio (risk-adjusted return), MAPE (percentage error). + +**Example 2: Trend Classification (Upward/Downward)** + +* **Target variable:** Binary classification of price movement (e.g., next day). +* **Candidate models:** + * **Logistic Regression:** Simple and interpretable, but may not capture complex trends. + * **Decision Trees:** Handles non-linearities well, but prone to overfitting. + * **Support Vector Machines (SVM):** Identifies clear trend boundaries, but sensitive to noise. + * **Random Forest:** More robust than single Decision Trees, but requires careful tuning. +* **Features:** Similar to price prediction, but consider momentum indicators, volume changes, and market sentiment analysis (e.g., news sentiment). +* **Feature engineering:** Explore features specifically related to trend identification (e.g., rate of change, moving average convergence/divergence). +* **Hyperparameter tuning:** Regularization for Logistic Regression, tree depth/number of trees for Random Forest, kernel type for SVM. +* **Evaluation metrics:** + * **Accuracy:** Overall percentage of correct predictions. + * **Precision:** Ratio of true positives to predicted positives. + * **Recall:** Ratio of true positives to all actual positives. + * **F1-score:** Balanced metric considering both precision and recall. + +**Remember:** + +* Choose models and features aligned with your goals and asset class. +* Start simple and gradually add complexity based on data and performance. +* Evaluate thoroughly using appropriate metrics and avoid overfitting. +* Consider data quality, cleaning, and potential biases. + +## Step 3: Strategy Implementation and Backtesting (Backtrader) + +**Example 1: Trend-Following Strategy (Price Prediction based)** + +* **Entry rule:** Buy when predicted price exceeds actual price by a threshold (consider volatility). +* **Exit rule:** Sell when predicted price falls below actual price by a threshold or after a holding period (set stop-loss). +* **Position sizing:** Based on predicted price movement, confidence level, and risk tolerance. +* **Risk management:** Implement stop-loss orders, consider trailing stops and position size adjustments. +* **Backtesting:** Analyze performance metrics (profit, loss, Sharpe ratio, drawdown) for different models, thresholds, and holding periods. +* **Additional considerations:** Transaction costs, slippage, commissions, walk-forward testing for robustness. + +**Example 2: Mean Reversion Strategy (Trend Classification based)** + +* **Entry rule:** Buy when classified as downtrend and reaches a support level (defined by technical indicators or historical data). +* **Exit rule:** Sell when classified as uptrend or reaches a take-profit target (set based on risk tolerance and expected return). +* **Position sizing:** Fixed percentage or dynamic based on confidence in trend classification. +* **Risk management:** Stop-loss orders, consider trailing stops and position adjustments based on trend strength. +* **Backtesting:** Analyze performance across different trend classification models, support/resistance levels, and holding periods. +* **Additional considerations:** Transaction costs * **Step 4: Continuous Improvement and Feedback Loop** * Analyze backtesting results and identify areas for improvement.