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.
- 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!
pip install fastquant
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")
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
# 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
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
#> ...
The data is pulled from Binance, and all the available tickers are found here.
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
get_crypto_data("BTCUSDT", "2019-01-01", "2019-03-01")
#> # 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
Note: Support for backtesting in R is pending
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
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).
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
| 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 |
backtest('rsi', df, rsi_period=14, rsi_upper=70, rsi_lower=30)
# Starting Portfolio Value: 100000.00
# Final Portfolio Value: 132967.87
backtest('smac', df, fast_period=10, slow_period=30)
# Starting Portfolio Value: 100000.00
# Final Portfolio Value: 95902.74
backtest('emac', df, fast_period=10, slow_period=30)
# Starting Portfolio Value: 100000.00
# Final Portfolio Value: 90976.00
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
backtest('bbands', df, period=20, devfactor=2.0)
# Starting Portfolio Value: 100000.00
# Final Portfolio Value: 97060.30
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
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)
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.