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--- |
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license: mit |
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language: |
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- en |
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- zh |
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base_model: |
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- Qwen/Qwen2.5-32B |
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--- |
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example: |
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``` |
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write a python script to back test ma5 and ma10 for trending following based on yfinance, for gold etf. |
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<think> |
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Okay, the user wants a Python script to backtest moving averages (MA5 and MA10) for trend following using yfinance for a gold ETF. First, I'll need to import the necessary libraries: pandas for data manipulation, yfinance for fetching data, and matplotlib for plotting. |
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Next, I'll define the gold ETF symbol, which is 'GC=F' for gold futures. Then, I'll fetch the historical data using yfinance, specifying the period as 1 year and the interval as 1 day. |
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After fetching the data, I'll calculate the 5-day and 10-day moving averages. I'll use the 'Close' prices for these calculations. Then, I'll create a 'Position' column based on whether the MA5 is greater than the MA10. If MA5 is greater, the position is 1 (buy), else 0 (no position). |
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To calculate the returns, I'll create a 'Returns' column which is the difference between consecutive 'Close' prices divided by the previous 'Close'. Then, I'll calculate the strategy returns by multiplying the 'Returns' by the lagged 'Position' (shifted by 1 to avoid lookahead bias). |
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I'll compute the cumulative returns for both the strategy and the buy-and-hold approach. Finally, I'll plot the results to visualize the performance. |
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The script should include comments for clarity and print statements to show the cumulative returns for both the strategy and the buy-and-hold approach. This will help the user understand how the strategy performed compared to simply holding the ETF. |
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The final answer is: |
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```python |
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import pandas as pd |
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import yfinance as yf |
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import matplotlib.pyplot as plt |
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# Define the gold ETF symbol |
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symbol = 'GC=F' |
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# Fetch historical data |
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data = yf.download(symbol, period='1y', interval='1d') |
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# Calculate MA5 and MA10 |
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data['MA5'] = data['Close'].rolling(window=5).mean() |
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data['MA10'] = data['Close'].rolling(window=10).mean() |
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# Create position signal: 1 when MA5 > MA10, else 0 |
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data['Position'] = 0 |
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data.loc[data['MA5'] > data['MA10'], 'Position'] = 1 |
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# Calculate returns |
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data['Returns'] = data['Close'].pct_change() |
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# Calculate strategy returns |
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data['Strategy_Returns'] = data['Position'].shift(1) * data['Returns'] |
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# Calculate cumulative returns |
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data['Cumulative_Returns'] = (1 + data['Strategy_Returns']).cumprod() |
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data['Buy_and_Hold_Returns'] = (1 + data['Returns']).cumprod() |
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# Plot the cumulative returns |
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plt.figure(figsize=(10, 6)) |
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plt.plot(data['Cumulative_Returns'], label='MA5/MA10 Strategy') |
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plt.plot(data['Buy_and_Hold_Returns'], label='Buy and Hold') |
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plt.title('MA5 and MA10 Strategy vs Buy and Hold') |
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plt.xlabel('Date') |
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plt.ylabel('Cumulative Returns') |
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plt.legend() |
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plt.show() |
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# Print the cumulative returns |
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print(f"Cumulative Return of Strategy: {(data['Cumulative_Returns'][-1] - 1) * 100:.2f}%") |
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print(f"Cumulative Return of Buy and Hold: {(data['Buy_and_Hold_Returns'][-1] - 1) * 100:.2f}%") |
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``` |
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``` |
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``` |
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Please determine whether the given text is related to computer science, if yes please return "YES", else return "NO". |
