fastquant 0.1.3.11

Bringing data driven investments to the mainstream

fastquant :nerd_face:

Bringing backtesting to the mainstream

fastquant allows you to easily backtest investment strategies with as few as 3 lines of python code. Its goal is to promote data driven investments by making quantitative analysis in finance accessible to everyone.

Features

1. Easily access historical stock data
2. Backtest trading strategies with only 3 lines of code

^{* - Both Yahoo Finance and Philippine stock data data are accessible straight from fastquant}

Installation

pip install fastquant

Get stock data

All symbols from Yahoo Finance and Philippine Stock Exchange (PSE) are accessible via get_stock_data.

from fastquant import get_stock_data
df = get_stock_data("JFC", "2018-01-01", "2019-01-01")
print(df.head())

#           dt  close   volume
#   2019-01-01  293.0   181410
#   2019-01-02  292.0  1665440
#   2019-01-03  309.0  1622480
#   2019-01-06  323.0  1004160
#   2019-01-07  321.0   623090

Note: Symbols from Yahoo Finance will return closing prices in USD, while symbols from PSE will return closing prices in PHP

Backtest trading strategies

Simple Moving Average Crossover (15 day MA vs 40 day MA)

Daily Jollibee prices from 2018-01-01 to 2019-01-01

from fastquant import backtest
backtest('smac', df, fast_period=15, slow_period=40)

# Starting Portfolio Value: 100000.00
# Final Portfolio Value: 102272.90

Library of trading strategies

Strategy	Alias	Parameters
Relative Strength Index (RSI)	rsi	rsi_period, rsi_upper, rsi_lower
Simple moving average crossover (SMAC)	smac	fast_period, slow_period
Exponential moving average crossover (EMAC)	macd	fast_period, slow_period
Moving Average Convergence Divergence (MACD)	emac	fast_perod, slow_upper, signal_period, sma_period, sma_dir_period
Bollinger Bands	bbands	period, devfactor

Relative Strength Index (RSI) Strategy

backtest('rsi', df, rsi_period=14, rsi_upper=70, rsi_lower=30)

# Starting Portfolio Value: 100000.00
# Final Portfolio Value: 132967.87

Simple moving average crossover (SMAC) Strategy

backtest('smac', df, fast_period=10, slow_period=30)

# Starting Portfolio Value: 100000.00
# Final Portfolio Value: 95902.74

Exponential moving average crossover (EMAC) Strategy

backtest('emac', df, fast_period=10, slow_period=30)

# Starting Portfolio Value: 100000.00
# Final Portfolio Value: 90976.00

Moving Average Convergence Divergence (MACD) Strategy

backtest('macd', df, fast_period=12, slow_period=26, signal_period=9, sma_period=30, dir_period=10)

# Starting Portfolio Value: 100000.00
# Final Portfolio Value: 96229.58

Bollinger Bands Strategy

backtest('bbands', df, period=20, devfactor=2.0)

# Starting Portfolio Value: 100000.00
# Final Portfolio Value: 97060.30

See more examples here.