From c75a640153af7be7a485ba5d82d4a9c537e94afd Mon Sep 17 00:00:00 2001 From: medusa Date: Sun, 2 Jun 2024 00:12:30 +0000 Subject: [PATCH] Update financial_docs/ml_trading.md --- financial_docs/ml_trading.md | 324 +++++++++++++++++++++++++++++++++-- 1 file changed, 308 insertions(+), 16 deletions(-) diff --git a/financial_docs/ml_trading.md b/financial_docs/ml_trading.md index 3d2f974..e8532ea 100644 --- a/financial_docs/ml_trading.md +++ b/financial_docs/ml_trading.md @@ -95,33 +95,325 @@ Model evaluation is crucial to assess the accuracy and reliability of prediction - **Formula**: \( \text{RMSE} = \sqrt{ \frac{1}{n} \sum_{i=1}^n (Y_i - \hat{Y_i})^2 } \) - **Interpretation**: Lower RMSE indicates better model performance. -### Workflow Summary (Pseudocode) +Here's the updated Workflow Summary with the same level of detail as the Model Training section: + +### Workflow Summary #### Data Preparation -1. Ingest data from OANDA. -2. Preprocess data: handle missing values, outliers. -3. Store preprocessed data in TimescaleDB. +1. Ingest data from OANDA: + - Utilize OANDA API to retrieve historical and real-time Forex data. + - Handle authentication and API rate limits. + - Implement error handling and retry mechanisms for reliable data retrieval. +2. Preprocess data: handle missing values, outliers: + - Identify and fill missing values using appropriate techniques (e.g., forward fill, interpolation). + - Detect and handle outliers using statistical methods (e.g., z-score, Tukey's fences). + - Normalize or standardize the data to ensure consistent scaling. +3. Store preprocessed data in TimescaleDB: + - Design an efficient database schema for storing time series data. + - Utilize TimescaleDB's hypertable feature for optimal performance and scalability. + - Implement data insertion and retrieval queries optimized for time series analysis. #### Feature Engineering -1. Create lag features and rolling statistics. -2. Store engineered features in TimescaleDB. +1. Create lag features and rolling statistics: + - Generate lag features by shifting the time series data by specified time steps. + - Calculate rolling statistics (e.g., mean, variance, standard deviation) using sliding windows. + - Implement efficient algorithms for feature generation (e.g., vectorized operations, caching). +2. Store engineered features in TimescaleDB: + - Extend the database schema to accommodate engineered features. + - Optimize data insertion and retrieval queries for efficient storage and access. + - Implement data partitioning and indexing strategies for improved query performance. #### Correlation Analysis and Storage -1. Calculate correlation matrix. -2. Store correlation results in TimescaleDB. +1. Calculate correlation matrix: + - Compute the Pearson correlation coefficient between different Forex pairs. + - Handle missing values and ensure proper alignment of time series data. + - Implement efficient algorithms for correlation calculation (e.g., vectorized operations, parallelization). +2. Store correlation results in TimescaleDB: + - Design a suitable database schema for storing correlation matrices. + - Optimize data insertion and retrieval queries for efficient storage and access. + - Implement data compression techniques to reduce storage requirements. #### Trend Identification and Storage -1. Calculate moving averages and trend indicators. -2. Store trend data in TimescaleDB. +1. Calculate moving averages and trend indicators: + - Implement various moving average techniques (e.g., SMA, EMA) with configurable window sizes. + - Calculate trend indicators (e.g., MACD, RSI) to identify market trends and momentum. + - Optimize calculations using efficient algorithms and vectorized operations. +2. Store trend data in TimescaleDB: + - Extend the database schema to incorporate trend indicators and moving averages. + - Optimize data insertion and retrieval queries for efficient storage and access. + - Implement data retention policies to manage historical trend data effectively. #### Model Training (ARIMA, LSTM, Transformers) -1. Retrieve feature-engineered data from TimescaleDB. -2. Train ARIMA, LSTM, and Transformer models. -3. Store trained models and scalers. +1. Retrieve feature-engineered data from TimescaleDB: + - Design efficient queries to fetch relevant features and target variables. + - Implement data batching and caching mechanisms to optimize data loading. + - Handle data preprocessing steps (e.g., normalization, encoding) specific to each model. +2. Train ARIMA, LSTM, and Transformer models: + - ARIMA: + - Determine optimal p, d, and q parameters using techniques like ACF/PACF plots, AIC/BIC criteria, and grid search. + - Train the ARIMA model using the selected parameters and evaluate its performance. + - LSTM: + - Design the LSTM network architecture, including the number of layers, hidden units, and dropout regularization. + - Select appropriate hyperparameters (e.g., learning rate, batch size, number of epochs) using techniques like grid search or Bayesian optimization. + - Implement the LSTM model using deep learning frameworks (e.g., TensorFlow, PyTorch) and train it on the Forex data. + - Transformers: + - Understand the self-attention mechanism and its components (e.g., scaled dot-product attention, multi-head attention). + - Build the Transformer model architecture, including positional encodings, encoder-decoder structure, and masking. + - Train the Transformer model using techniques like teacher forcing and optimize hyperparameters. +3. Store trained models and scalers: + - Serialize and store the trained models (ARIMA, LSTM, Transformers) for future use. + - Store the associated preprocessing scalers (e.g., normalization parameters) to ensure consistent data preprocessing during inference. + - Implement versioning and metadata management for tracking model iterations and configurations. #### Model Evaluation and Storage -1. Evaluate models using RMSE. -2. Store evaluation results in TimescaleDB. +1. Evaluate models using RMSE: + - Calculate the Root Mean Squared Error (RMSE) metric for each trained model. + - Implement cross-validation techniques (e.g., rolling window, time series split) to assess model performance on unseen data. + - Compare RMSE values across different models and hyperparameter configurations to select the best-performing models. +2. Store evaluation results in TimescaleDB: + - Design a database schema to store model evaluation metrics and configurations. + - Implement data insertion and retrieval queries for efficient storage and access of evaluation results. + - Utilize TimescaleDB's time-based aggregation and analysis capabilities for model performance tracking over time. ### Conclusion -This guide provides a detailed, technical overview of the methodologies used in forex time series analysis, leveraging advanced AI/ML models like ARIMA, LSTM, and Transformers. Each step is designed to ensure robustness, scalability, and accuracy in forecasting and trend identification, making it suitable for high-frequency trading environments and financial analytics. \ No newline at end of file +This guide provides a detailed, technical overview of the methodologies used in forex time series analysis, leveraging advanced AI/ML models like ARIMA, LSTM, and Transformers. Each step is designed to ensure robustness, scalability, and accuracy in forecasting and trend identification, making it suitable for high-frequency trading environments and financial analytics. + +--- + +### Technical Guide for Forex Time Series Analysis Using AI/ML Models + +#### Objective +This guide provides a comprehensive overview of the methodologies and machine learning models used in analyzing forex time series data, focusing on EUR/USD and other major and minor pairs. The goal is to understand the underlying technical principles, implement feature engineering, perform correlation analysis, identify trends, train AI/ML models, and evaluate their performance using RMSE. + +### Key Components + +1. **Data Preparation** +2. **Feature Engineering** +3. **Correlation Analysis** +4. **Trend Identification** +5. **Model Training** +6. **Model Evaluation** + +### 1. Data Preparation + +#### Context +Forex data is high-frequency time series data that requires careful preprocessing to handle missing values, outliers, and ensure consistency. TimescaleDB is used for efficient storage and retrieval due to its scalability and time-series optimizations. + +**Technical Details:** +- **Data Sourcing**: Forex data is typically retrieved from APIs such as OANDA, which provide real-time and historical data. + - Utilize OANDA API to retrieve historical and real-time Forex data. + - Handle authentication and API rate limits. + - Implement error handling and retry mechanisms for reliable data retrieval. +- **Preprocessing**: This includes filling missing values using forward fill or interpolation methods, handling outliers through techniques like z-score normalization, and converting timestamps to a uniform format. + - Identify and fill missing values using appropriate techniques (e.g., forward fill, interpolation). + - Detect and handle outliers using statistical methods (e.g., z-score, Tukey's fences). + - Normalize or standardize the data to ensure consistent scaling. +- **Data Storage**: Store preprocessed data in TimescaleDB for efficient storage and retrieval. + - Design an efficient database schema for storing time series data. + - Utilize TimescaleDB's hypertable feature for optimal performance and scalability. + - Implement data insertion and retrieval queries optimized for time series analysis. + +### 2. Feature Engineering + +#### Context +Feature engineering transforms raw data into meaningful features that enhance the model's predictive capabilities. This process is critical for time series analysis as it captures temporal dependencies and seasonality. + +**Technical Details:** +- **Lag Features**: Introducing past values (lags) as predictors helps capture temporal dependencies. + - **Mathematical Formulation**: \( \text{Lag}(k) = X_{t-k} \) + - Generate lag features by shifting the time series data by specified time steps. +- **Rolling Statistics**: Calculating rolling mean, variance, and standard deviation captures local trends and volatility. + - **Mathematical Formulation**: \( \text{Rolling Mean}(w) = \frac{1}{w} \sum_{i=t-w+1}^{t} X_i \) + - Calculate rolling statistics using sliding windows. + - Implement efficient algorithms for feature generation (e.g., vectorized operations, caching). +- **Scaling**: Normalization or standardization ensures that features are on a similar scale, which is essential for models like LSTM and Transformers. +- **Feature Storage**: Store engineered features in TimescaleDB for efficient storage and access. + - Extend the database schema to accommodate engineered features. + - Optimize data insertion and retrieval queries for efficient storage and access. + - Implement data partitioning and indexing strategies for improved query performance. + +### 3. Correlation Analysis + +#### Context +Correlation analysis identifies relationships between different forex pairs, which can inform trading strategies and portfolio management. + +**Technical Details:** +- **Pearson Correlation**: Measures linear correlation between pairs. + - **Formula**: \( \rho_{X,Y} = \frac{\text{Cov}(X,Y)}{\sigma_X \sigma_Y} \) + - **Properties**: Symmetric, bounded between -1 and 1. + - Compute the Pearson correlation coefficient between different Forex pairs. + - Handle missing values and ensure proper alignment of time series data. + - Implement efficient algorithms for correlation calculation (e.g., vectorized operations, parallelization). +- **Visualization**: Heatmaps are used to visualize the correlation matrix, highlighting highly correlated pairs. +- **Correlation Storage**: Store correlation results in TimescaleDB for efficient storage and access. + - Design a suitable database schema for storing correlation matrices. + - Optimize data insertion and retrieval queries for efficient storage and access. + - Implement data compression techniques to reduce storage requirements. + +### 4. Trend Identification + +#### Context +Identifying trends helps in understanding the market direction and making informed trading decisions. Techniques like moving averages smooth out short-term fluctuations and highlight longer-term trends. + +**Technical Details:** +- **Moving Averages**: Simple and exponential moving averages (SMA, EMA) are used. + - **SMA Formula**: \( \text{SMA}(n) = \frac{1}{n} \sum_{i=0}^{n-1} X_{t-i} \) + - **EMA Formula**: \( \text{EMA}(t) = \alpha \cdot X_t + (1-\alpha) \cdot \text{EMA}(t-1) \) + - Implement various moving average techniques with configurable window sizes. + - Optimize calculations using efficient algorithms and vectorized operations. +- **Trend Indicators**: Calculate trend indicators (e.g., MACD, RSI) to identify market trends and momentum. +- **Trend Lines**: Connecting significant highs or lows in price data to form resistance and support lines. +- **Trend Storage**: Store trend data in TimescaleDB for efficient storage and access. + - Extend the database schema to incorporate trend indicators and moving averages. + - Optimize data insertion and retrieval queries for efficient storage and access. + - Implement data retention policies to manage historical trend data effectively. + +### 5. Model Training + +#### Context +Different machine learning models have different strengths in time series forecasting. This project uses ARIMA, LSTM, and Transformer models. + +**Technical Details:** + +**Data Preparation for Model Training:** +- Retrieve feature-engineered data from TimescaleDB. + - Design efficient queries to fetch relevant features and target variables. + - Implement data batching and caching mechanisms to optimize data loading. + - Handle data preprocessing steps (e.g., normalization, encoding) specific to each model. + +**ARIMA (AutoRegressive Integrated Moving Average):** +- **Components**: AR (p) - AutoRegression, I (d) - Integration, MA (q) - Moving Average. + - **AR**: \( X_t = \phi_1 X_{t-1} + \phi_2 X_{t-2} + \dots + \phi_p X_{t-p} + \epsilon_t \) + - **I**: \( Y_t = X_t - X_{t-1} \) (d times differencing) + - **MA**: \( X_t = \epsilon_t + \theta_1 \epsilon_{t-1} + \theta_2 \epsilon_{t-2} + \dots + \theta_q \epsilon_{t-q} \) +- **Use Case**: Effective for univariate time series with trends and seasonality. +- **Parameter Selection**: Determine optimal p, d, and q parameters using techniques like ACF/PACF plots, AIC/BIC criteria, and grid search. +- **Model Training**: Train the ARIMA model using the selected parameters and evaluate its performance. + +**LSTM (Long Short-Term Memory):** +- **Architecture**: Special type of RNN capable of learning long-term dependencies. + - **Gates**: Input, forget, and output gates control the cell state. + - **Equations**: + - Forget Gate: \( f_t = \sigma(W_f \cdot [h_{t-1}, X_t] + b_f) \) + - Input Gate: \( i_t = \sigma(W_i \cdot [h_{t-1}, X_t] + b_i) \) + - Output Gate: \( o_t = \sigma(W_o \cdot [h_{t-1}, X_t] + b_o) \) + - Cell State: \( C_t = f_t * C_{t-1} + i_t * \tilde{C_t} \) +- **Use Case**: Suitable for capturing long-term dependencies in time series data. +- **Model Design**: Design the LSTM network architecture, including the number of layers, hidden units, and dropout regularization. +- **Hyperparameter Tuning**: Select appropriate hyperparameters (e.g., learning rate, batch size, number of epochs) using techniques like grid search or Bayesian optimization. +- **Model Implementation**: Implement the LSTM model using deep learning frameworks (e.g., TensorFlow, PyTorch) and train it on the Forex data. + +**Transformers:** +- **Architecture**: Self-attention mechanism allows the model to weigh the importance of different parts of the input sequence. + - **Attention Mechanism**: \( \text{Attention}(Q, K, V) = \text{softmax}\left( \frac{QK^T}{\sqrt{d_k}} \right) V \) + - **Components**: Multi-head attention, feed-forward networks, and positional encodings. +- **Use Case**: Powerful for sequence modeling tasks, especially when capturing global dependencies. +- **Model Building**: Build the Transformer model architecture, including positional encodings, encoder-decoder structure, and masking. +- **Model Training**: Train the Transformer model using techniques like teacher forcing and optimize hyperparameters. + +**Model Storage:** +- Serialize and store the trained models (ARIMA, LSTM, Transformers) for future use. +- Store the associated preprocessing scalers (e.g., normalization parameters) to ensure consistent data preprocessing during inference. +- Implement versioning and metadata management for tracking model iterations and configurations. + +### 6. Model Evaluation + +#### Context +Model evaluation is crucial to assess the accuracy and reliability of predictions. RMSE (Root Mean Squared Error) is a standard metric for this purpose. + +**Technical Details:** +- **RMSE**: Measures the average magnitude of the error. + - **Formula**: \( \text{RMSE} = \sqrt{ \frac{1}{n} \sum_{i=1}^n (Y_i - \hat{Y_i})^2 } \) + - **Interpretation**: Lower RMSE indicates better model performance. + - Calculate the RMSE metric for each trained model. + - Implement cross-validation techniques (e.g., rolling window, time series split) to assess model performance on unseen data. + - Compare RMSE values across different models and hyperparameter configurations to select the best-performing models. +- **Evaluation Storage**: Store evaluation results in TimescaleDB for efficient storage and access. + - Design a database schema to store model evaluation metrics and configurations. + - Implement data insertion and retrieval queries for efficient storage and access of evaluation results. + - Utilize TimescaleDB's time-based aggregation and analysis capabilities for model performance tracking over time. + +### Conclusion +This guide provides a detailed, technical overview of the methodologies used in forex time series analysis, leveraging advanced AI/ML models like ARIMA, LSTM, and Transformers. Each step is designed to ensure robustness, scalability, and accuracy in forecasting and trend identification, making it suitable for high-frequency trading environments and financial analytics. By aligning the level of detail across all sections, this guide offers a comprehensive resource for implementing and optimizing forex time series analysis using cutting-edge AI/ML techniques. + +--- + +Here's the updated Workflow Summary with the same level of detail as the Model Training section: + +### Workflow Summary + +#### Data Preparation +1. Ingest data from OANDA: + - Utilize OANDA API to retrieve historical and real-time Forex data. + - Handle authentication and API rate limits. + - Implement error handling and retry mechanisms for reliable data retrieval. +2. Preprocess data: handle missing values, outliers: + - Identify and fill missing values using appropriate techniques (e.g., forward fill, interpolation). + - Detect and handle outliers using statistical methods (e.g., z-score, Tukey's fences). + - Normalize or standardize the data to ensure consistent scaling. +3. Store preprocessed data in TimescaleDB: + - Design an efficient database schema for storing time series data. + - Utilize TimescaleDB's hypertable feature for optimal performance and scalability. + - Implement data insertion and retrieval queries optimized for time series analysis. + +#### Feature Engineering +1. Create lag features and rolling statistics: + - Generate lag features by shifting the time series data by specified time steps. + - Calculate rolling statistics (e.g., mean, variance, standard deviation) using sliding windows. + - Implement efficient algorithms for feature generation (e.g., vectorized operations, caching). +2. Store engineered features in TimescaleDB: + - Extend the database schema to accommodate engineered features. + - Optimize data insertion and retrieval queries for efficient storage and access. + - Implement data partitioning and indexing strategies for improved query performance. + +#### Correlation Analysis and Storage +1. Calculate correlation matrix: + - Compute the Pearson correlation coefficient between different Forex pairs. + - Handle missing values and ensure proper alignment of time series data. + - Implement efficient algorithms for correlation calculation (e.g., vectorized operations, parallelization). +2. Store correlation results in TimescaleDB: + - Design a suitable database schema for storing correlation matrices. + - Optimize data insertion and retrieval queries for efficient storage and access. + - Implement data compression techniques to reduce storage requirements. + +#### Trend Identification and Storage +1. Calculate moving averages and trend indicators: + - Implement various moving average techniques (e.g., SMA, EMA) with configurable window sizes. + - Calculate trend indicators (e.g., MACD, RSI) to identify market trends and momentum. + - Optimize calculations using efficient algorithms and vectorized operations. +2. Store trend data in TimescaleDB: + - Extend the database schema to incorporate trend indicators and moving averages. + - Optimize data insertion and retrieval queries for efficient storage and access. + - Implement data retention policies to manage historical trend data effectively. + +#### Model Training (ARIMA, LSTM, Transformers) +1. Retrieve feature-engineered data from TimescaleDB: + - Design efficient queries to fetch relevant features and target variables. + - Implement data batching and caching mechanisms to optimize data loading. + - Handle data preprocessing steps (e.g., normalization, encoding) specific to each model. +2. Train ARIMA, LSTM, and Transformer models: + - ARIMA: + - Determine optimal p, d, and q parameters using techniques like ACF/PACF plots, AIC/BIC criteria, and grid search. + - Train the ARIMA model using the selected parameters and evaluate its performance. + - LSTM: + - Design the LSTM network architecture, including the number of layers, hidden units, and dropout regularization. + - Select appropriate hyperparameters (e.g., learning rate, batch size, number of epochs) using techniques like grid search or Bayesian optimization. + - Implement the LSTM model using deep learning frameworks (e.g., TensorFlow, PyTorch) and train it on the Forex data. + - Transformers: + - Understand the self-attention mechanism and its components (e.g., scaled dot-product attention, multi-head attention). + - Build the Transformer model architecture, including positional encodings, encoder-decoder structure, and masking. + - Train the Transformer model using techniques like teacher forcing and optimize hyperparameters. +3. Store trained models and scalers: + - Serialize and store the trained models (ARIMA, LSTM, Transformers) for future use. + - Store the associated preprocessing scalers (e.g., normalization parameters) to ensure consistent data preprocessing during inference. + - Implement versioning and metadata management for tracking model iterations and configurations. + +#### Model Evaluation and Storage +1. Evaluate models using RMSE: + - Calculate the Root Mean Squared Error (RMSE) metric for each trained model. + - Implement cross-validation techniques (e.g., rolling window, time series split) to assess model performance on unseen data. + - Compare RMSE values across different models and hyperparameter configurations to select the best-performing models. +2. Store evaluation results in TimescaleDB: + - Design a database schema to store model evaluation metrics and configurations. + - Implement data insertion and retrieval queries for efficient storage and access of evaluation results. + - Utilize TimescaleDB's time-based aggregation and analysis capabilities for model performance tracking over time. \ No newline at end of file