fastquant 0.1.2.17

Bringing data driven investments to the mainstream

fastquant :nerd_face:

Bringing data driven investments to the mainstream

fastquant allows you 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

* - Only Philippine stock data is available so far, but more countries will be covered soon with Yahoo Finance integration

Installation

pip install fastquant

Get stock data

Accessed via the phisix API

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

Plot daily closing prices

from matplotlib import pyplot as plt

df.close.plot(figsize=(10, 6))
plt.title("Daily Closing Prices of JFC\nfrom 2018-01-01 to 2019-01-01", fontsize=20)

Analyze with a simple moving average (SMA) trading strategy

ma30 = df.close.rolling(30).mean()
close_ma30 = pd.concat([df.close, ma30], axis=1).dropna()
close_ma30.columns = ['Closing Price', 'Simple Moving Average (30 day)']

close_ma30.plot(figsize=(10, 6))
plt.title("Daily Closing Prices vs 30 day SMA of JFC\nfrom 2018-01-01 to 2019-01-01", fontsize=20)

Backtesting templates

Using the backtrader framework

Relative strength index (RSI) trading strategy (14 day window)

Daily Jollibee prices from 2017-01-01 to 2019-01-01

python examples/jfc_rsi.py

Min max support resistance trading strategy (30 day window)

Daily Jollibee prices from 2017-01-01 to 2019-01-01

python examples/jfc_support_resistance.py