From 4e8342b7b2a92b55c70d2a80294872a3cd30c3e9 Mon Sep 17 00:00:00 2001 From: medusa Date: Sun, 18 Feb 2024 15:22:17 +0000 Subject: [PATCH] Update projects/forex_algo_trading.md --- projects/forex_algo_trading.md | 55 ++++++++++++++++++++++++---------- 1 file changed, 39 insertions(+), 16 deletions(-) diff --git a/projects/forex_algo_trading.md b/projects/forex_algo_trading.md index b6d45db..a124941 100644 --- a/projects/forex_algo_trading.md +++ b/projects/forex_algo_trading.md @@ -11,26 +11,49 @@ This guide focuses on developing an algorithmic trading strategy for the EUR/USD - Register for an Oanda account and generate an API key. - Install `oandapyV20`: `pip install oandapyV20`. -### 1.2 Fetching Historical EUR/USD Data -- **Objective**: Download historical EUR/USD data suitable for ML analysis. -- **Steps**: - - Use `oandapyV20` to fetch historical price data. - - Decide on data granularity (e.g., H1 for hourly data) based on trading strategy needs. +## 1. Fetching Historical EUR/USD Data -## 2. Data Preparation and Analysis +### Objective +Download historical EUR/USD data optimized for mean reversion strategy development in machine learning. + +### Steps +- **API Utilization**: Employ `oandapyV20` for accessing Oanda's historical price data, focusing on capturing extensive price history to identify mean reversion opportunities. +- **Data Granularity Decision**: + - For mean reversion, select granularities that balance detail with noise reduction. **H4 (4-hour)** data is a good starting point, providing insight into intraday price movements without overwhelming short-term noise. + - Consider also fetching **D1 (daily)** data to analyze longer-term mean reversion patterns. + +## 2. Data Preparation and Analysis for Mean Reversion ### 2.1 Data Cleaning and Preprocessing -- **Objective**: Prepare the data for analysis and model training. -- **Steps**: - - Handle missing values, outliers, and duplicate entries. - - Normalize or standardize the data if necessary. -### 2.2 Exploratory Data Analysis (EDA) -- **Objective**: Gain insights into the EUR/USD price movements and volatility. -- **Tools**: Use `pandas` for data manipulation and `matplotlib`/`seaborn` for visualization. -- **Steps**: - - Plot price movements over time. - - Calculate and visualize key statistics (mean, median, standard deviation). +#### Objective +Ensure data quality for accurate mean reversion analysis and model training. + +#### Steps +- **Missing Values**: Fill or remove gaps in data to maintain consistent time series analysis. +- **Outliers**: Identify and address price spikes that may skew mean reversion analysis. +- **Normalization/Standardization**: Adjust data to a common scale, particularly important when combining features of different magnitudes or when data spans several years. + +### 2.2 Exploratory Data Analysis (EDA) for Mean Reversion + +#### Objective +Identify characteristics of EUR/USD that indicate mean reversion tendencies. + +#### Tools and Steps +- **Pandas for Data Handling**: Utilize `pandas` for managing time series data, crucial for chronological analysis and feature engineering. +- **Matplotlib/Seaborn for Visualization**: + - **Price Movement Plots**: Visualize EUR/USD price movements with time series plots to identify cyclical patterns or periods of mean reversion. + - **Volatility Analysis**: Plot volatility (e.g., using ATR or standard deviation) against price to spot mean reversion during high volatility periods. + - **Mean Reversion Indicators**: Calculate and visualize indicators like Bollinger Bands or the Z-score (price distance from the mean), which are direct signals of potential mean reversion. + +#### Advanced Analysis +- **Statistical Tests**: + - Conduct statistical tests like the Augmented Dickey-Fuller test to assess the stationarity of the EUR/USD series, a prerequisite for mean reversion. + - Use Hurst exponent analysis to differentiate between mean-reverting and trending behavior. + +## Next Steps: Strategy Formulation and Model Building +- **Indicator Selection**: Beyond visual analysis, systematically select indicators that historically signal mean reversion points. Incorporate these into the feature set for ML model training. +- **Machine Learning Models**: Experiment with models that can classify or predict mean-reverting behavior. Regression models can predict return to mean levels, while classification models can signal buy/sell opportunities based on detected mean reversion patterns. ## 3. Feature Engineering