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https://github.com/linsanity03/algorithmic_trading
Trading strategy based on the intersection of short-term and long-term moving averages.
https://github.com/linsanity03/algorithmic_trading
jupyter-notebook matplotlib-pyplot numpy pandas
Last synced: 30 days ago
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Trading strategy based on the intersection of short-term and long-term moving averages.
- Host: GitHub
- URL: https://github.com/linsanity03/algorithmic_trading
- Owner: LINSANITY03
- Created: 2024-11-25T12:36:37.000Z (about 1 month ago)
- Default Branch: main
- Last Pushed: 2024-12-03T23:08:30.000Z (about 1 month ago)
- Last Synced: 2024-12-04T00:19:43.176Z (about 1 month ago)
- Topics: jupyter-notebook, matplotlib-pyplot, numpy, pandas
- Language: Jupyter Notebook
- Homepage:
- Size: 54.3 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Moving Average Crossover Strategy
Create a trading strategy based on the intersection of short-term and long-term moving averages.
## 1. Fetch Historical Data
We are using the price data from a popular stock [yfinance](https://github.com/ranaroussi/yfinance).
The dataset typically includes:
Ticker: Represents the stock symbol (e.g., "AAPL" for Apple Inc.).
Open: Price of the stock when the market opened for the day.
High: Highest price of the stock traded during the day.
Low: Lowest price of the stock traded during the day.
Close: Price of the stock when the market closed for the day.
Adj Close: A version of closing price accounting other factor affecting the stock value.
Volume: The number of shares traded during the time period.
## 2. Calculate short-long moving average.
Test various short/long moving average window combinations for better output. For this instance,
we use 5 and 17 days respectively. (To change the value check **config.yaml** file)
```
data["Short_MA"] = data["Close"].rolling(window=short_window).mean()
data["Long_MA"] = data["Close"].rolling(window=long_window).mean()
```
## 3. Implement trading strategy.
Implement a moving average crossover strategy for a single stock.
```
# Generate buy/sell signals
data['Signal'] = 0
data.loc[data['Short_MA'] > data['Long_MA'], 'Signal'] = 1 # Buy signal
data.loc[data['Short_MA'] <= data['Long_MA'], 'Signal'] = -1 # Sell signal
# Identify when trades occur
data['Trade'] = data['Signal'].diff().fillna(0).abs()
```
## 4. Simulate Trades
Backtest the strategy by simulating trades based on signals.
```
# Backtest the strategy
data['Daily_Return'] = data['Close'].pct_change()
data['Strategy_Return'] = data['Signal'].shift(1) * data['Daily_Return'] # Lag signal by 1 day
# Deduct transaction costs
data['Strategy_Return_with_Costs'] = data['Strategy_Return'] - (data['Trade'] * transaction_cost)
# Cumulative returns
data['Cumulative_Market_Return'] = (1 + data['Daily_Return']).cumprod()
data['Cumulative_Strategy_Return'] = (1 + data['Strategy_Return']).cumprod()
data['Cumulative_Strategy_Return_with_Costs'] = (1 + data['Strategy_Return_with_Costs']).cumprod()
```
## 5. Evaluate Performance
Calculate key metrics to assess the strategy.
```
sharpe_ratio_with_costs = data['Strategy_Return_with_Costs'].mean() / data['Strategy_Return_with_Costs'].std() * (252**0.5)
print(f"Sharpe Ratio (With Costs): {sharpe_ratio_with_costs:.2f}")
```
## 6. Visualize Results
We use matplotlib to visualize stock price with short and long moving average.
## Collaboration
Feel free to use this codebase for your projects. If you want to talk more about this project or have found bugs, create a pull request or contact me on **[email protected]**.