fastquant 🤓

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.

To do this type of analysis without coding, you can also try out Hawksight, which was just recently launched! 😄

If you want to interact with us directly, you can also reach us on the Hawksight discord. Feel free to ask about fastquant in the #feedback-suggestions and #bug-report channels.

Features

Easily access historical stock data
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
or
python -m pip install fastquant

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

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

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

Want to do this without coding at all?

If you want to make this kind of analysis even more simple without having to code at all (or want to avoid the pain of doing all of the setup required), you can signup for free and try out Hawksight - this new no-code tool I’m building to democratize data driven investments.

Hoping to make these kinds of powerful analyses accessible to more people!

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`, `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`, `custom_column`
Custom Ternary Strategy	ternary	`buy_int`, `sell_int`, `custom_column`

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
# Note: Unfortunately, you can't recreate this scenario due to inconsistencies in the dates and sentiments that is scraped by get_bt_news_sentiment. In order to have a quickstart with News Sentiment Strategy you need to make the dates consistent with the sentiments that you are scraping.

from fastquant import get_yahoo_data, get_bt_news_sentiment
from datetime import datetime, timedelta

# we get the current date and delta time of 30 days
current_date = datetime.now().strftime("%Y-%m-%d")
delta_date = (datetime.now() - timedelta(30)).strftime("%Y-%m-%d")
data = get_yahoo_data("TSLA", delta_date, current_date)
sentiments = get_bt_news_sentiment(keyword="tesla", page_nums=3)
backtest("sentiment", data, sentiments=sentiments, senti=0.2)

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 out-of-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
import pandas as pd
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)

+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)">

# 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.

fastquant API

View full list of fastquan API here

Be part of the growing fastquant community

Want to discuss more about fastquant with other users, and our team of developers?

You can reach us on the Hawksight discord. Feel free to ask about fastquant in the #feedback-suggestions and #bug-report channels.

Run fastquant in a Docker Container

>> df.head()">

# Build the image
docker build -t myimage .

# Run the container
docker run -t -d -p 5000:5000 myimage

# Get the container id
docker ps

# SSH into the fastquant container
docker exec -it 
   
     /bin/bash

# Run python and use fastquant
python

>>> from fastquant import get_stock_data
>>> df = get_stock_data("TSLA", "2019-01-01", "2020-01-01")
>>> df.head()

Fastquant - Backtest and optimize your trading strategies with only 3 lines of code!

Related tags

Overview

fastquant 🤓

Bringing backtesting to the mainstream

Features

Installation

Python

Get stock data

Python

Get crypto data

Python

Backtest trading strategies

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

Want to do this without coding at all?

Optimize trading strategies with automated grid search

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

Library of trading strategies

Relative Strength Index (RSI) Strategy

Simple moving average crossover (SMAC) Strategy

Exponential moving average crossover (EMAC) Strategy

Moving Average Convergence Divergence (MACD) Strategy

Bollinger Bands Strategy

News Sentiment Strategy

Multi Strategy

Custom Strategy for Backtesting Machine Learning & Statistics Based Predictions

fastquant API

Be part of the growing fastquant community

Run fastquant in a Docker Container

Owner

Lorenzo Ampil

Paddle pit - Rethinking Spatial Dimensions of Vision Transformers

A lightweight face-recognition toolbox and pipeline based on tensorflow-lite

Human-Pose-and-Motion History

FaRL for Facial Representation Learning

Attention-guided gan for synthesizing IR images

El-Gamal on Elliptic Curve (Python)

Seg-Torch for Image Segmentation with Torch

Official repository of the paper Learning to Regress 3D Face Shape and Expression from an Image without 3D Supervision

Model Quantization Benchmark

The Submission for SIMMC 2.0 Challenge 2021

pytorch implementation of "Contrastive Multiview Coding", "Momentum Contrast for Unsupervised Visual Representation Learning", and "Unsupervised Feature Learning via Non-Parametric Instance-level Discrimination"

Face-Recognition-Attendence-System - This face recognition Attendence system using Python

People movement type classifier with YOLOv4 detection and SORT tracking.

Implementation of CoCa, Contrastive Captioners are Image-Text Foundation Models, in Pytorch

PyTorch implementation for Graph Contrastive Learning with Augmentations

A tutorial on DataFrames.jl prepared for JuliaCon2021

[NeurIPS 2021] Galerkin Transformer: a linear attention without softmax

This is the repo for the paper "Improving the Accuracy-Memory Trade-Off of Random Forests Via Leaf-Refinement".

Alpha-IoU: A Family of Power Intersection over Union Losses for Bounding Box Regression

Code and data (Incidents Dataset) for ECCV 2020 Paper "Detecting natural disasters, damage, and incidents in the wild".