Trading Bot Project - Iteration 1

Project Overview

This project aims to build an automated trading bot with the following core components:

Data Collection: Gathers historical and live market data from free sources (yfinance, Alpaca API, Binance API).
Signal Generator: Analyzes data using technical indicators, statistical methods, or AI/ML models to produce buy/sell/hold signals.
Risk Management: Applies rules for position sizing, stop-loss orders, take-profit orders, and overall portfolio risk control.
Execution Engine: Communicates with broker APIs (Alpaca, Binance) to place, modify, and cancel orders.
Logging / UI: Records events and provides a user interface (e.g., Streamlit dashboard) for monitoring.

Integrating AI Without Paying

AI/ML is a core aspect, exploring free techniques:

Goal	AI Technique (Free)	Tools/Libraries	Considerations
Predict Market Direction	Time Series Forecasting (LSTM), Classification (XGBoost, Random Forest)	TensorFlow/Keras, PyTorch, scikit-learn, XGBoost	Requires feature engineering, risk of overfitting.
Detect Trade Setups	Pattern Recognition (CNN on charts), Rule-based Logic	TensorFlow/Keras, PyTorch, OpenCV	Can be complex, data labeling is key.
Adapt to Market Regimes	Reinforcement Learning (RL)	Stable-Baselines3, Gymnasium	Requires defining environment, reward function design is critical.
Optimize Strategy Parameters	Genetic Algorithms, Grid Search, Bayesian Optimization	DEAP, scikit-optimize, Custom implementations	Can be computationally intensive, prone to local optima (GA).

Getting Started

To build and run the bot, follow these general steps:

Prerequisites

Python 3.7+
A code editor
Command-line access

Installation

Clone the repository, create a virtual environment, and install dependencies from requirements.txt.

git clone <repository_url>
cd <repository_folder>
python -m venv venv
source venv/bin/activate  # On Windows use `venv\Scripts\activate`
pip install -r requirements.txt

Configuration

Obtain API keys (start with paper trading!) and configure strategy parameters. Store keys securely.

Running the Bot

Specific commands depend on implementation and the mode you want to run in. Ensure your configuration is set up correctly before running.

# Backtesting
python backtester/run_backtest.py --strategy <strategy_name> --config config/config.yaml --data data/historical_data.csv

# Paper Trading
python live_trading/run_paper_trading.py --strategy <strategy_name> --config config/config.yaml

# Live Trading (Use with extreme caution!)
python live_trading/run_live_trading.py --strategy <strategy_name> --config config/config.yaml

# Running Dashboard
streamlit run dashboard/dashboard.py

# Training an AI Model
python training/train_model.py --model <model_type> --config config/config.yaml --data data/training_data.csv

Project Structure

A well-organized project structure is vital for maintainability and scalability. The proposed structure aims for modularity and clear separation of concerns.

my_trading_bot/
├── backtester/
│   ├── __init__.py
│   └── run_backtest.py
├── config/
│   ├── __init__.py
│   └── config.yaml # Example configuration file
├── data/
│   ├── __init__.py
│   └── historical_data.csv # Example data file
├── core/
│   ├── __init__.py
│   ├── data_handler.py
│   ├── execution_handler.py
│   ├── risk_manager.py
│   └── signal_generator.py
├── live_trading/
│   ├── __init__.py
│   ├── run_paper_trading.py
│   └── run_live_trading.py
├── strategies/
│   ├── __init__.py
│   ├── base_strategy.py # Base class for strategies
│   ├── rsi_strategy.py # Example strategy
│   └── ml_strategy.py # Example ML-based strategy
├── tests/
│   ├── __init__.py
│   ├── test_data_handler.py
│   └── test_strategies.py
├── training/
│   ├── __init__.py
│   ├── train_model.py
│   └── models/ # Directory for trained models
├── dashboard/
│   ├── __init__.py
│   └── dashboard.py # Streamlit dashboard
├── .gitignore
├── README.md
├── requirements.txt
└── main.py # Optional entry point or orchestrator