Update projects/forex_algo_trading.md

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.