hyperdrive 1.10.11

An algorithmic trading platform

	hyperdrive: an algorithmic trading library

hyperdrive is an algorithmic trading library that powers quant research firm   FORCEPU.SH.

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. Storing data

Pre-requisites:

• an IEXCloud or Polygon API key
• an AWS account and an S3 bucket

Environment Variables:

• IEXCLOUD or POLYGON
• AWS_ACCESS_KEY_ID
• AWS_SECRET_ACCESS_KEY
• AWS_DEFAULT_REGION
• S3_BUCKET

from hyperdrive import DataSource
from DataSource import IEXCloud, MarketData

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

symbol = 'TSLA'
timeframe = '7d'

md = MarketData()
iex = IEXCloud()

iex.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.buy_and_hold(df[C.CLOSE])
signals = hist.get_optimal_signals(df[C.CLOSE])
my_strat = hist.create_portfolio(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

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:

• (from Robinhood)
• (from IEXCloud and Polygon)
• (from IEXCloud and Polygon)
• (from IEXCloud and Polygon)
• (from IEXCloud and Polygon)
• (from StockTwits)
• (from the Bureau of Labor Statistics)

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