Performance Metrics

Performance Metrics return a float and are not part of the DataFrame Extension. They are called the Standard way. The DataFrame must have a DatetimeIndex for time-based metrics such as cagr.

import pandas_ta_classic as ta
result = ta.cagr(df.close)

Available Metrics

  • Compounded Annual Growth Rate: cagr

  • Calmar Ratio: calmar_ratio

  • Downside Deviation: downside_deviation

  • Jensen’s Alpha: jensens_alpha

  • Log Max Drawdown: log_max_drawdown

  • Max Drawdown: max_drawdown

  • Pure Profit Score: pure_profit_score

  • Sharpe Ratio: sharpe_ratio

  • Sortino Ratio: sortino_ratio

  • Volatility: volatility

Backtesting with vectorbt

Use ta.tsignals to generate trading signals for integration with vectorbt’s Portfolio.from_signals method.

For a comprehensive example, see the example Jupyter Notebook VectorBT Backtest with Pandas TA in the examples directory.

Brief Example

import pandas as pd
import pandas_ta_classic as ta
import vectorbt as vbt

# Load your data
df = pd.read_csv("your_data.csv")

# Generate trading signals using Pandas TA Classic
df['sma_short'] = df.ta.sma(length=20)
df['sma_long'] = df.ta.sma(length=50)

# Create buy/sell signals
entries = df['sma_short'] > df['sma_long']
exits = df['sma_short'] < df['sma_long']

# Use tsignals for vectorbt integration
signals = ta.tsignals(entries, asbool=True)

# Run backtest with vectorbt
portfolio = vbt.Portfolio.from_signals(
    df['close'],
    entries=signals['TS_Entries'],
    exits=signals['TS_Exits']
)

# View results
portfolio.stats()