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 (backtesting, paper trading, dashboard).

# Examples
python run_backtest.py --strategy <strategy_name> --data <data_file>
python run_paper_trading.py --strategy <strategy_name> --config <config_file>
streamlit run dashboard.py
python train_model.py --model <model_type> --config <config_file> --data <data_source/file>

Project Structure

A well-organized project structure is vital for maintainability and scalability. While the specific layout can vary, a common and recommended structure for a Python trading bot looks like this:

my_trading_bot/
├── data/
├── strategies/
├── core/
├── config/
├── tests/
├── README.md
├── requirements.txt
├── run_backtest.py
├── run_paper_trading.py
├── dashboard.py
└── ...