Trade on EMA Crossover #586
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| name: Trade on EMA Crossover | |
| on: | |
| schedule: | |
| - cron: '*/5 * * * *' # Run this workflow every 5 minutes | |
| env: | |
| PYTHON_VERSION: "3.10" | |
| MARKETWATCH_USERNAME: ${{ secrets.MARKETWATCH_USERNAME }} | |
| MARKETWATCH_PASSWORD: ${{ secrets.MARKETWATCH_PASSWORD }} | |
| GAME_NAME: "marketwatchapistrategieema" | |
| jobs: | |
| trade: | |
| runs-on: ubuntu-latest | |
| steps: | |
| - name: Checkout code | |
| uses: actions/checkout@v4 | |
| - name: Set up Python | |
| uses: actions/setup-python@v5 | |
| with: | |
| python-version: ${{ env.PYTHON_VERSION }} | |
| - name: Install dependencies | |
| run: | | |
| python -m pip install --upgrade pip | |
| pip install pandas | |
| pip install marketwatch | |
| pip install yfinance | |
| - name: Run trading script | |
| env: | |
| USERNAME: ${{ env.MARKETWATCH_USERNAME }} | |
| PASSWORD: ${{ env.MARKETWATCH_PASSWORD }} | |
| GAME_NAME: ${{ env.GAME_NAME }} | |
| run: | | |
| python - <<EOF | |
| import time | |
| import os | |
| import pandas as pd | |
| from datetime import datetime | |
| from marketwatch import MarketWatch | |
| username = os.environ.get('MARKETWATCH_USERNAME') | |
| password = os.environ.get('MARKETWATCH_PASSWORD') | |
| game_name = os.environ.get('GAME_NAME') | |
| marketwatch = MarketWatch(username, password) | |
| def calculate_ema(price_series, periods): | |
| return price_series.ewm(span=periods, adjust=False).mean() | |
| def trade_on_crossover(df, game_name, stock_symbol): | |
| short_term = df['EMA_20'].iloc[-1] | |
| long_term = df['EMA_80'].iloc[-1] | |
| prev_short_term = df['EMA_20'].iloc[-2] | |
| prev_long_term = df['EMA_80'].iloc[-2] | |
| if short_term > long_term and prev_short_term <= prev_long_term: | |
| marketwatch.buy(game_name, stock_symbol, 100) | |
| elif short_term < long_term and prev_short_term >= prev_long_term: | |
| marketwatch.sell(game_name, stock_symbol, 100) | |
| def trade_multiple_stocks(stock_symbols): | |
| price_data = {symbol: [] for symbol in stock_symbols} | |
| for _ in range(160): | |
| for symbol in stock_symbols: | |
| ticker = yf.Ticker(symbol) # <-- Use yfinance here | |
| price_data_point = ticker.history(period="1d")["Close"].iloc[-1] # <-- Fetch the last closing price | |
| price_data[symbol].append(price_data_point) | |
| if len(price_data[symbol]) > 150: | |
| price_data[symbol].pop(0) | |
| for symbol in stock_symbols: | |
| df = pd.DataFrame(price_data[symbol], columns=['Close']) | |
| for period in [20, 80]: | |
| ema_column_name = f'EMA_{period}' | |
| df[ema_column_name] = calculate_ema(df['Close'], period) | |
| if len(df) >= 80: | |
| trade_on_crossover(df, game_name, symbol) | |
| time.sleep(60) | |
| today = datetime.today().weekday() | |
| if today >= 5: | |
| exit(0) | |
| tech_stocks = ['AAPL', 'GOOGL', 'MSFT'] | |
| health_stocks = ['JNJ', 'MRK', 'PFE'] | |
| utility_stocks = ['NEE', 'DUK', 'D'] | |
| all_stocks = tech_stocks + health_stocks + utility_stocks | |
| trade_multiple_stocks(all_stocks) | |
| EOF |