fastquant 0.1.3.20

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 and optimize trading strategies with only 3 lines of code

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

Check out our blog posts in the fastquant website and this intro article on Medium!

Installation

Python

pip install fastquant

R

R support is pending development and lagging in features, but you may install the R package by typing the following:

# To install the stable version: 
install.packages("fastquant")

# To install the development version: 
# install.packages("remotes")
remotes::install_github("enzoampil/fastquant", subdir = "R")

Get stock data

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

Python

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

#           dt  close
#   2019-01-01  293.0
#   2019-01-02  292.0
#   2019-01-03  309.0
#   2019-01-06  323.0
#   2019-01-07  321.0

R

library(fastquant)

get_stock_data("JFC", "2018-01-01", "2018-02-01")

#> # A tibble: 22 x 7
#>    symbol dt         name     currency close percent_change  volume
#>    <chr>  <date>     <chr>    <chr>    <dbl>          <dbl>   <dbl>
#>  1 JFC    2018-01-03 Jollibee PHP       255.             NA  745780
#>  2 JFC    2018-01-04 Jollibee PHP       255              NA  617010
#>  3 JFC    2018-01-05 Jollibee PHP       255              NA  946040
#>  4 JFC    2018-01-08 Jollibee PHP       256              NA  840630
#> ...

Get crypto data

The data is pulled from Binance, and all the available tickers are found here.

Python

from fastquant import get_crypto_data
crypto = get_crypto_data("BTC/USDT", "2018-12-01", "2019-12-31")
crypto.head()

#             open    high     low     close    volume
# dt                                                          
# 2018-12-01  4041.27  4299.99  3963.01  4190.02  44840.073481
# 2018-12-02  4190.98  4312.99  4103.04  4161.01  38912.154790
# 2018-12-03  4160.55  4179.00  3827.00  3884.01  49094.369163
# 2018-12-04  3884.76  4085.00  3781.00  3951.64  48489.551613
# 2018-12-05  3950.98  3970.00  3745.00  3769.84  44004.799448

R

get_crypto_data("BTC/USDT", "2018-12-01", "2019-12-31")

#> # A tibble: 59 x 6
#>    dt          open  high   low close volume
#>    <date>     <dbl> <dbl> <dbl> <dbl>  <dbl>
#>  1 2019-01-01 3701. 3810. 3642  3797. 23742.
#>  2 2019-01-02 3796. 3882. 3750. 3859. 35156.
#>  3 2019-01-03 3858. 3863. 3730  3767. 29407.
#>  4 2019-01-04 3767. 3824. 3704. 3792. 29520.
#>  5 2019-01-05 3790. 3841. 3751  3771. 30491.
#> # … with 54 more rows

Note: Python has Yahoo Finance and phisix support. R has phisix support and porting to symbols from the quantmod package. Symbols from Yahoo Finance will return closing prices in USD, while symbols from PSE will return closing prices in PHP.

R does NOT have support for backtesting yet

Backtest trading strategies

Note: Support for backtesting in R is pending

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

Optimize trading strategies with automated grid search

fastquant allows you to automatically measure the performance of your trading strategy on multiple combinations of parameters. All you need to do is to input the values as iterators (like as a list or range).

Simple Moving Average Crossover (15 to 30 day MA vs 40 to 55 day MA)

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

from fastquant import backtest
res = backtest("smac", df, fast_period=range(15, 30, 3), slow_period=range(40, 55, 3), verbose=False)

# Optimal parameters: {'init_cash': 100000, 'buy_prop': 1, 'sell_prop': 1, 'execution_type': 'close', 'fast_period': 15, 'slow_period': 40}
# Optimal metrics: {'rtot': 0.022, 'ravg': 9.25e-05, 'rnorm': 0.024, 'rnorm100': 2.36, 'sharperatio': None, 'pnl': 2272.9, 'final_value': 102272.90}

print(res[['fast_period', 'slow_period', 'final_value']].head())

#	fast_period	slow_period	final_value
#0	15	        40	        102272.90
#1	21	        40	         98847.00
#2	21	        52	         98796.09
#3	24	        46	         98008.79
#4	15	        46	         97452.92

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)	emac	fast_period, slow_period
Moving Average Convergence Divergence (MACD)	macd	fast_perod, slow_upper, signal_period, sma_period, sma_dir_period
Bollinger Bands	bbands	period, devfactor
Buy and Hold	buynhold	N/A
Sentiment Strategy	sentiment	keyword , page_nums, senti
Custom Prediction Strategy	custom	upper_limit, lower_limit

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

News Sentiment Strategy

Use Tesla (TSLA) stock from yahoo finance and news articles from Business Times

from fastquant import get_yahoo_data, get_bt_news_sentiment
data = get_yahoo_data("TSLA", "2020-01-01", "2020-07-04")
sentiments = get_bt_news_sentiment(keyword="tesla", page_nums=3)
backtest("sentiment", data, sentiments=sentiments, senti=0.2)

# Starting Portfolio Value: 100000.00
# Final Portfolio Value: 313198.37

Multi Strategy

Multiple registered strategies can be utilized together in an OR fashion, where buy or sell signals are applied when at least one of the strategies trigger them.

df = get_stock_data("JFC", "2018-01-01", "2019-01-01")

# Utilize single set of parameters
strats = { 
    "smac": {"fast_period": 35, "slow_period": 50}, 
    "rsi": {"rsi_lower": 30, "rsi_upper": 70} 
} 
res = backtest("multi", df, strats=strats)
res.shape
# (1, 16)


# Utilize auto grid search
strats_opt = { 
    "smac": {"fast_period": 35, "slow_period": [40, 50]}, 
    "rsi": {"rsi_lower": [15, 30], "rsi_upper": 70} 
} 

res_opt = backtest("multi", df, strats=strats_opt)
res_opt.shape
# (4, 16)

Custom Strategy for Backtesting Machine Learning & Statistics Based Predictions

This powerful strategy allows you to backtest your own trading strategies using any type of model w/ as few as 3 lines of code after the forecast!

Predictions based on any model can be used as a custom indicator to be backtested using fastquant. You just need to add a custom column in the input dataframe, and set values for upper_limit and lower_limit.

The strategy is structured similar to RSIStrategy where you can set an upper_limit, above which the asset is sold (considered "overbought"), and a lower_limit, below which the asset is bought (considered "underbought). upper_limit is set to 95 by default, while lower_limit is set to 5 by default.

In the example below, we show how to use the custom strategy to backtest a custom indicator based on in-sample time series forecasts. The forecasts were generated using Facebook's Prophet package on Bitcoin prices.

from fastquant import get_crypto_data, backtest
from fbprophet import Prophet
from matplotlib import pyplot as plt

# Pull crypto data
df = get_crypto_data("BTC/USDT", "2019-01-01", "2020-05-31")

# Fit model on closing prices
ts = df.reset_index()[["dt", "close"]]
ts.columns = ['ds', 'y']
m = Prophet(daily_seasonality=True, yearly_seasonality=True).fit(ts)
forecast = m.make_future_dataframe(periods=0, freq='D')

# Predict and plot
pred = m.predict(forecast)
fig1 = m.plot(pred)
plt.title('BTC/USDT: Forecasted Daily Closing Price', fontsize=25)

# Convert predictions to expected 1 day returns
expected_1day_return = pred.set_index("ds").yhat.pct_change().shift(-1).multiply(100)

# Backtest the predictions, given that we buy bitcoin when the predicted next day return is > +1.5%, and sell when it's < -1.5%.
df["custom"] = expected_1day_return.multiply(-1)
backtest("custom", df.dropna(),upper_limit=1.5, lower_limit=-1.5)

See more examples here.