Stock Trading Bot with Machine Learning
In this blog post, we will walk through the process of building a stock trading bot using machine learning techniques. We will cover the following topics:
- Fetching historical stock data
- Feature engineering using technical indicators
- Training and evaluating machine learning models
- Generating buy/sell signals
- Executing trades
Fetching Historical Stock Data
We use the yfinance library to fetch historical stock data. The fetch_data function downloads the historical data for a single stock, while the fetch_data_multiple function downloads the data for multiple stocks.
```python import yfinance as yf
def fetch_data(symbol, start, end): # …
def fetch_data_multiple(symbols, start, end): # … ```
Feature Engineering with Technical Indicators
We use the talib library to calculate technical indicators for our dataset. The preprocess_data function calculates several indicators like moving averages, Bollinger Bands, RSI, MACD, and others, which are used as features for our machine learning model.
```python import talib
def preprocess_data(df): # … ```
Training and Evaluating Machine Learning Models
We use the sklearn library to train and evaluate Gradient Boosting Regressor models. We perform hyperparameter tuning using GridSearchCV. The train_model function trains a single model, while the train_models function trains multiple models for multiple stocks.
```python from sklearn.ensemble import GradientBoostingRegressor from sklearn.model_selection import GridSearchCV
def train_model(df): # …
def train_models(data): # … ```
Generating Buy/Sell Signals
We generate buy/sell signals for each stock using the trained models. The generate_signals function predicts the next day’s closing price and compares it to the current closing price to determine whether to buy or sell the stock.
```python def generate_signals(models, feature_names, data): # … ```
Executing Trades
We use the Alpaca API to execute trades
```python def execute_trades(signals): # … ```