fastquant 0.1.2.5

The easiest way to access and analyze Philippine stock data

fastquant :nerd_face:

Easiest way to access and analyze Philippine stock data

fastquant allows you easily access stock data from PSE Edge with as few as 2 lines of python code. Its goal is to promote data driven investments in the Philippines by making relevant information accessible to everyone.

Features

1. Easy access to Philippine stock data
2. Templates for backtesting trading strategies on Philippine stocks

Installation

pip install fastquant

Get stock data from PSE

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

#             open   high    low  close        value
#dt                                                 
#2018-01-03  253.4  256.8  253.0  255.4  190253754.0
#2018-01-04  255.4  255.4  253.0  255.0  157152856.0
#2018-01-05  255.6  257.4  255.0  255.0  242201952.0
#2018-01-08  257.4  259.0  253.4  256.0  216069242.0
#2018-01-09  256.0  258.0  255.0  255.8  250188588.0

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