Backtesting

Strategy simulation often includes going through same steps : determining entry and exit moments, calculating number of shares capital and pnl. To limit the number of lines of code needed to perform a backtest, the twp library includes a simple backtesting module.

The backtest module is a very simple version of a vectorized backtester. It can be used as a stand-alone module without the rest of the tradingWithPython library.

All of the functionality is accessible through the Backtest class, which will be demonstrated here.

The backtester needs an instrument price and entry/exit signals to do its job. Let’s start by creating simple data series to test it.

In [1]: import tradingWithPython as twp

In [2]: import pandas as pd

In [3]: import numpy as np

Backtest class

class tradingWithPython.lib.backtest.Backtest(price, signal, signalType='capital', initialCash=0, roundShares=True)

Simple vectorized backtester. Works with pandas objects.

Attributes

trades

(Series) trade data

Methods

plotTrades

plotTrades()

visualise trades on the price chart long entry : green triangle up short entry : red triangle down exit : black circle

pnl

easy access to pnl data column

sharpe

return annualized sharpe ratio of the pnl

Functions

tradingWithPython.lib.backtest.tradeBracket(price, entryBar, upper=None, lower=None, timeout=None)

trade a bracket on price series, return price delta and exit bar #

Parameters:

price : np.array

array of price values

entryBar : int

entry bar number, determines entry price

upper : float

high stop

lower : float

low stop

timeout : int

max number of periods to hold

Returns:

exit price and number of bars held