Hyperdrive

| <img src="https://raw.githubusercontent.com/suchak1/hyperdrive/master/img/nasa_5mb_cropped.gif" width="250" /> | hyperdrive: an algorithmic trading library | | -------------------------------------------------------------------------------------------------------------- | ------------------------------------------------ |

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hyperdrive is an algorithmic trading library that powers quant research firm  <img src="https://raw.githubusercontent.com/suchak1/hyperdrive/master/img/logo.png" width="16" /> Algotrade.io.

Unlike other backtesting libraries, hyperdrive specializes in data collection and quantitative research.

In the examples below, we explore how to:

1. store market data
2. create trading strategies
3. test strategies against historical data (backtesting)
4. execute orders.

Getting Started

Prerequisites

You will need Python 3.8+

Installation

To install the necessary packages, run

pythom -m pip install hyperdrive -U

Examples

Most secrets must be passed as environment variables. Future updates will allow secrets to be passed directly into class object (see example on order execution).

<!-- ### 1. Getting data Pre-requisites: - a Polygon API key - an AWS account and an S3 bucket Environment Variables: - `POLYGON` - `AWS_ACCESS_KEY_ID` - `AWS_SECRET_ACCESS_KEY` - `AWS_DEFAULT_REGION` - `S3_BUCKET` ``` from hyperdrive import DataSource from DataSource import Polygon # Your Polygon API token must be an environment variable (accessible in os.environ['POLYGON']) poly = Polygon() df = poly.get_ohlc(symbol='TSLA', timeframe='7d') print(df) ``` Output: ``` Time Open High Low Close Vol 2863 2021-11-10 1010.41 1078.1000 987.31 1067.95 42802722 2864 2021-11-11 1102.77 1104.9700 1054.68 1063.51 22396568 2865 2021-11-12 1047.50 1054.5000 1019.20 1033.42 25573148 2866 2021-11-15 1017.63 1031.9800 978.60 1013.39 34775649 2867 2021-11-16 1003.31 1057.1999 1002.18 1054.73 26542359 ``` Although this function won't save data to the S3 bucket, hyperdrive checks the S3 bucket with key `data/ohlc/polygon/TSLA.csv` to see if any cached data exists to correct for inconsistencies in values and column names. -->

1. Storing data

Pre-requisites:

• a Polygon API key
• an AWS account and an S3 bucket

Environment Variables:

• POLYGON
• AWS_ACCESS_KEY_ID
• AWS_SECRET_ACCESS_KEY
• AWS_DEFAULT_REGION
• S3_BUCKET

from hyperdrive import DataSource
from DataSource import Polygon, MarketData

# Polygon API token loaded as an environment variable (os.environ['POLYGON'])

symbol = 'TSLA'
timeframe = '7d'

md = MarketData()
poly = Polygon()

poly.save_ohlc(symbol=symbol, timeframe=timeframe)
df = md.get_ohlc(symbol=symbol, timeframe=timeframe)

print(df)

Output:

           Time     Open       High      Low    Close       Vol
2863 2021-11-10  1010.41  1078.1000   987.31  1067.95  42802722
2864 2021-11-11  1102.77  1104.9700  1054.68  1063.51  22396568
2865 2021-11-12  1047.50  1054.5000  1019.20  1033.42  25573148
2866 2021-11-15  1017.63  1031.9800   978.60  1013.39  34775649
2867 2021-11-16  1003.31  1057.1999  1002.18  1054.73  26542359

2. Creating a model

Much of this code is still closed-source, but you can take a look at the Historian class in the History module for some ideas.

3. Backtesting a strategy

We use vectorbt to backtest strategies.

from hyperdrive import History, DataSource, Constants as C
from History import Historian
from DataSource import MarketData

hist = Historian()
md = MarketData()

symbol = 'TSLA'
timeframe = '1y'

df = md.get_ohlc(symbol=symbol, timeframe=timeframe)

holding = hist.from_holding(df[C.CLOSE])
signals = hist.get_optimal_signals(df[C.CLOSE])
my_strat = hist.from_signals(df[C.CLOSE], signals)

metrics = [
    'Total Return [%]', 'Benchmark Return [%]',
    'Max Drawdown [%]', 'Max Drawdown Duration',
    'Total Trades', 'Win Rate [%]', 'Avg Winning Trade [%]',
    'Avg Losing Trade [%]', 'Profit Factor',
    'Expectancy', 'Sharpe Ratio', 'Calmar Ratio',
    'Omega Ratio', 'Sortino Ratio'
]

holding_stats = holding.stats()[metrics]
my_strat_stats = my_strat.stats()[metrics]

print(f'Buy and Hold Strat\n{"-"*42}')
print(holding_stats)

print(f'My Strategy\n{"-"*42}')
print(my_strat_stats)

# holding.plot()
my_strat.plot()

Output:

Buy and Hold Strat
------------------------------------------
Total Return [%]                138.837436
Benchmark Return [%]            138.837436
Max Drawdown [%]                 36.246589
Max Drawdown Duration    186 days 00:00:00
Total Trades                             1
Win Rate [%]                           NaN
Avg Winning Trade [%]                  NaN
Avg Losing Trade [%]                   NaN
Profit Factor                          NaN
Expectancy                             NaN
Sharpe Ratio                      2.206485
Calmar Ratio                      6.977133
Omega Ratio                       1.381816
Sortino Ratio                     3.623509
Name: Close, dtype: object

My Strategy
------------------------------------------
Total Return [%]                364.275727
Benchmark Return [%]            138.837436
Max Drawdown [%]                  35.49422
Max Drawdown Duration    122 days 00:00:00
Total Trades                             6
Win Rate [%]                          80.0
Avg Winning Trade [%]            52.235227
Avg Losing Trade [%]             -3.933059
Profit Factor                     45.00258
Expectancy                      692.157004
Sharpe Ratio                      4.078172
Calmar Ratio                     23.220732
Omega Ratio                       2.098986
Sortino Ratio                     7.727806
Name: Close, dtype: object

<img src="https://raw.githubusercontent.com/suchak1/hyperdrive/master/img/my_strat.png">

4. Executing an order

Pre-requisites:

• a Binance.US API key

Environment Variables:

• BINANCE

from pprint import pprint
from hyperdrive import Exchange
from Exchange import Binance

# Binance API token loaded as an environment variable (os.environ['BINANCE'])

bn = Binance()

# use 45% of your USD account balance to buy BTC
order = bn.order('BTC', 'USD', 'BUY', 0.45)

pprint(order)

Output:

{'clientOrderId': '3cfyrJOSXqq6Zl1RJdeRRC',
 'cummulativeQuoteQty': 46.8315,
 'executedQty': 0.000757,
 'fills': [{'commission': '0.0500',
            'commissionAsset': 'USD',
            'price': '61864.6400',
            'qty': '0.00075700',
            'tradeId': 25803914}],
 'orderId': 714855908,
 'orderListId': -1,
 'origQty': 0.000757,
 'price': 0.0,
 'side': 'SELL',
 'status': 'FILLED',
 'symbol': 'BTCUSD',
 'timeInForce': 'GTC',
 'transactTime': 1637030680121,
 'type': 'MARKET'}

Use

Use the scripts provided in the scripts/ directory as a reference since they are actually used in production daily.

Available data collection functions:

• [x] (from Robinhood)
• [x] (from Polygon)
• [x] (from Polygon)
• [x] (from Polygon)
• [x] (from Polygon)

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• [x] (from the Bureau of Labor Statistics)

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