Creating Custom Strategies and Reporting Pipelines via quantstats

Quantitative trading has become increasingly popular with the rise in computational power and access to financial data. As a trader or a data scientist involved in the realm of finance, quantstats is a powerful library that can help you create custom strategies and reporting pipelines. This Python library provides tools for calculating financial statistics, visualizing performance metrics, and producing customized reports to refine strategies.

Getting Started with quantstats

Before diving into creating custom strategies and reports, it's essential to set up your environment. You need to have Python installed on your system along with packages such as pandas and quantstats.

pip install pandas quantstats

Loading and Preparing Data

To create a custom strategy, you need historical stock data. You can fetch this data using data providers like Yahoo Finance, or pre-download it into a CSV file to facilitate multiple tests.

import pandas as pd

def load_data(file_path):
    df = pd.read_csv(file_path)
    df['Date'] = pd.to_datetime(df['Date'])
    df.set_index('Date', inplace=True)
    return df

# Example loading data
file_path = "/path/to/your/stock_data.csv"
data = load_data(file_path)

Once you have your data, you can focus on strategy creation.

Developing a Custom Strategy

Any trading strategy involves signals generation based on certain conditions. We'll focus on a basic Moving Average Crossover strategy.

def moving_average_crossover(df, short_window=40, long_window=100):
    signals = pd.DataFrame(index=df.index)
    signals['signal'] = 0.0
    
    # Generate short and long moving averages
    signals['short_mavg'] = df['Close'].rolling(window=short_window, min_periods=1, center=False).mean()
    signals['long_mavg'] = df['Close'].rolling(window=long_window, min_periods=1, center=False).mean()

    # Create signals
    signals['signal'][short_window:] = \
        np.where(signals['short_mavg'][short_window:] 
                 > signals['long_mavg'][short_window:], 1.0, 0.0)   

    # Generate trading orders
    signals['positions'] = signals['signal'].diff()
    
    return signals

# Running the strategy on your data
signals = moving_average_crossover(data)

Implementing a Reporting Pipeline with quantstats

Once your strategy is defined and implemented, quantstats allows you to generate performance reports quickly. You first need to create a simple backtest to gather the equity curve, or you can simulate a strategy using historical data to derive portfolio returns.

import quantstats as qs

# Assume data has a "Close" column
strategy_returns = data['Close'].pct_change().fillna(0)

# Calculate portfolio returns
portfolio_returns = signals['positions'] * strategy_returns

# Using quantstats to analyze strategy performance
qs.reports.full(portfolio_returns, "Benchmark"s_stock")

Customizing the Report

Quantstats generates a rich HTML report and also helps plot various visuals to analyze the strategy's returns, drawdowns, and risk metrics. Suppose you want internal analytics on settings or report in another format (e.g., simple statistics output).

# Generate a detailed HTML report
qs.reports.html(signals['positions'], benchmark='Benchmark_stock', 
                output='custom_report.html')

# Viewing basic metrics
print(qs.stats.sharpe(portfolio_returns))
print(qs.stats.max_drawdown(portfolio_returns))

Conclusion

By leveraging the flexibility and ease of use of quantstats, you can implement sophisticated strategies, backtest their effectiveness, and produce robust reports to help in decision-making. Whether you are creating simple strategies or handling complex quantitative analyses, having a well-structured pipeline from data loading to detailed output will significantly enhance your trading methodologies.

quantstats acts as a bridge between raw data and comprehensible, actionable output that both developers and traders employ in frequently optimizing their models and strategies.