Challenge: Backtest a Moving Average Crossover Strategy
You have already explored how to plot moving averages and visualize trading signals, as well as the basics of backtesting and evaluating strategy performance. Now, you will put these ideas into practice by coding and backtesting a simple moving average crossover strategy. This challenge will help you connect theoretical concepts to practical strategy development using pandas and matplotlib.
To begin, you will work with a hardcoded DataFrame of closing prices. Your goal is to implement a strategy that generates a buy signal when the 3-day simple moving average (SMA) crosses above the 7-day SMA, and a sell signal when the 3-day SMA crosses below the 7-day SMA. You will simulate following these signals by entering and exiting trades, calculate the total return, and plot the resulting equity curve to visualize your strategy's performance over time.
Swipe to start coding
You will implement a moving average crossover trading strategy using a hardcoded DataFrame of daily closing prices.
- Define a DataFrame with a column
Closecontaining at least 30 daily closing prices (use any realistic values). - Compute the 3-day and 7-day simple moving averages (SMA) of the closing prices and add them as new columns.
- Generate trading signals:
- Signal is
1(buy) when the 3-day SMA crosses above the 7-day SMA. - Signal is
-1(sell) when the 3-day SMA crosses below the 7-day SMA. - Otherwise, signal is
0.
- Signal is
- Simulate the strategy:
- Assume you start with $10,000 in cash and can only hold one position at a time (either long or out of the market).
- Buy at the next day's close after a buy signal, and sell at the next day's close after a sell signal.
- Calculate the daily equity (portfolio value) over time.
- Plot:
- The closing prices, 3-day SMA, and 7-day SMA on one chart.
- The equity curve (portfolio value over time) on a separate chart.
- Print the total return (percentage gain or loss) at the end of the simulation.
Lösning
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Can you show me how to create the hardcoded DataFrame of closing prices?
How do I implement the moving average crossover logic in code?
Can you explain how to calculate and plot the equity curve for this strategy?
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You have already explored how to plot moving averages and visualize trading signals, as well as the basics of backtesting and evaluating strategy performance. Now, you will put these ideas into practice by coding and backtesting a simple moving average crossover strategy. This challenge will help you connect theoretical concepts to practical strategy development using pandas and matplotlib.
To begin, you will work with a hardcoded DataFrame of closing prices. Your goal is to implement a strategy that generates a buy signal when the 3-day simple moving average (SMA) crosses above the 7-day SMA, and a sell signal when the 3-day SMA crosses below the 7-day SMA. You will simulate following these signals by entering and exiting trades, calculate the total return, and plot the resulting equity curve to visualize your strategy's performance over time.
Swipe to start coding
You will implement a moving average crossover trading strategy using a hardcoded DataFrame of daily closing prices.
- Define a DataFrame with a column
Closecontaining at least 30 daily closing prices (use any realistic values). - Compute the 3-day and 7-day simple moving averages (SMA) of the closing prices and add them as new columns.
- Generate trading signals:
- Signal is
1(buy) when the 3-day SMA crosses above the 7-day SMA. - Signal is
-1(sell) when the 3-day SMA crosses below the 7-day SMA. - Otherwise, signal is
0.
- Signal is
- Simulate the strategy:
- Assume you start with $10,000 in cash and can only hold one position at a time (either long or out of the market).
- Buy at the next day's close after a buy signal, and sell at the next day's close after a sell signal.
- Calculate the daily equity (portfolio value) over time.
- Plot:
- The closing prices, 3-day SMA, and 7-day SMA on one chart.
- The equity curve (portfolio value over time) on a separate chart.
- Print the total return (percentage gain or loss) at the end of the simulation.
Lösning
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