The Python Guide: Data Collection, Filtering, and Backtesting for Statistical Trading Strategies

1.Data Collection

Step 1: Determine Data Requirements
Define the specific data elements crucial for your trading strategy. This typically includes historical price data (open, high, low, close), volume, and possibly other fundamental or macroeconomic indicators depending on your strategy’s design.

Step 2: Select Data Providers
Choose reliable sources that provide accurate and consistent data. Common providers include financial data APIs like Yahoo Finance, Alpha Vantage, Quandl, and for economic indicators, sources like the Federal Reserve Economic Data (FRED) database.

Step 3: Accessing Data
Utilize APIs or libraries in your preferred programming language to fetch data. Here’s how you can fetch historical stock prices using Python and `yfinance`:

```python
import yfinance as yf
# Define ticker symbol and timeframe
ticker = 'AAPL'
start_date = '2010–01–01'
end_date = '2020–12–31'
# Fetch historical data
data = yf.download(ticker, start=start_date, end=end_date)
```

2. Data Filtering and Preprocessing

Step 4: Cleaning Data
Data cleaning ensures data quality by handling missing values, outliers, and any inconsistencies that could skew analysis or model performance. Example using Pandas:

```python
import pandas as pd
# Drop missing values
data_cleaned = data.dropna()
# Handle outliers or anomalies
data_cleaned = data_cleaned.where((data_cleaned - data_cleaned.mean()).abs() <= 3 * data_cleaned.std())
```

Step 5: Feature Engineering
Transform raw data into features that align with your strategy’s requirements. Common techniques include calculating moving averages, relative strength index (RSI), and volatility measures like standard deviation or average true range (ATR):

```python
# Example: Compute 50-day simple moving average
data_cleaned['SMA_50'] = data_cleaned['Close'].rolling(window=50).mean()
# Example: Compute Relative Strength Index (RSI)
def compute_rsi(data, window=14):
 delta = data['Close'].diff()
 gain = (delta.where(delta > 0, 0)).rolling(window=window).mean()
 loss = (-delta.where(delta < 0, 0)).rolling(window=window).mean()
 rs = gain / loss
 rsi = 100 - (100 / (1 + rs))
 return rsi
data_cleaned['RSI_14'] = compute_rsi(data_cleaned)
```

3. Backtesting

Step 6: Strategy Implementation
Define specific rules and conditions that govern your trading strategy. This involves setting up buy, sell, and position management logic based on the data and features engineered earlier.

Step 7: Backtesting Framework
Use a backtesting framework such as `Backtrader` to simulate your strategy over historical data. Below is an expanded example using `Backtrader`:

```python
import backtrader as bt
class MyStrategy(bt.Strategy):
 params = (
 ('sma_period', 50),
 )
 
 def __init__(self):
 self.sma = bt.indicators.SimpleMovingAverage(self.data.close, period=self.params.sma_period)
 
 def next(self):
 if self.data.close[0] > self.sma[0]:
 self.buy()
 elif self.data.close[0] < self.sma[0]:
 self.sell()
# Initialize backtest
cerebro = bt.Cerebro()
# Add data feed
data = bt.feeds.PandasData(dataname=data_cleaned)
cerebro.adddata(data)
# Add strategy
cerebro.addstrategy(MyStrategy)
# Set initial cash and commission
cerebro.broker.setcash(100000.0)
cerebro.broker.setcommission(commission=0.001)
# Run backtest
cerebro.run()
# Retrieve results
strat = cerebro.run()[0]
print(f"Final Portfolio Value: {strat.broker.getvalue()}")
```

Step 8: Performance Evaluation
Evaluate the performance of your strategy using various metrics like Sharpe ratio, maximum drawdown, and cumulative returns. This step helps in understanding the robustness and profitability of your strategy over the historical data.

That’s all you need to know to back test & build a trading strategy!