hyperdrive 1.15.3
An algorithmic trading platform
Requires Python: >=3.7

| <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 &nbsp;[<img src="https://raw.githubusercontent.com/suchak1/hyperdrive/master/img/logo.png" width="16" /> **Algotrade.io**](https://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](https://github.com/suchak1/hyperdrive/blob/master/hyperdrive/History.py) for some ideas.

### 3. Backtesting a strategy

We use [_vectorbt_](https://vectorbt.dev/) 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/`](https://github.com/suchak1/hyperdrive/tree/master/scripts) directory as a reference since they are actually used in production daily.

Available data collection functions:

- [x] [![Symbols](https://github.com/suchak1/hyperdrive/workflows/Symbols/badge.svg)](https://github.com/suchak1/hyperdrive/actions?query=workflow%3ASymbols) (from Robinhood)
- [x] [![OHLC](https://github.com/suchak1/hyperdrive/workflows/OHLC/badge.svg)](https://github.com/suchak1/hyperdrive/actions?query=workflow%3AOHLC) (from Polygon)
- [x] [![Intraday](https://github.com/suchak1/hyperdrive/workflows/Intraday/badge.svg)](https://github.com/suchak1/hyperdrive/actions?query=workflow%3AIntraday) (from Polygon)
- [x] [![Dividends](https://github.com/suchak1/hyperdrive/workflows/Dividends/badge.svg)](https://github.com/suchak1/hyperdrive/actions?query=workflow%3ADividends) (from Polygon)
- [x] [![Splits](https://github.com/suchak1/hyperdrive/workflows/Splits/badge.svg)](https://github.com/suchak1/hyperdrive/actions?query=workflow%3ASplits) (from Polygon)
<!-- - [x] [![Social Sentiment](<https://github.com/suchak1/hyperdrive/workflows/Social%20Sentiment%20(1)/badge.svg>)](https://github.com/suchak1/hyperdrive/actions?query=workflow%3A%22Social+Sentiment+%281%29%22) (from StockTwits) -->
- [x] [![Unemployment](https://github.com/suchak1/hyperdrive/workflows/Unemployment/badge.svg)](https://github.com/suchak1/hyperdrive/actions?query=workflow%3AUnemployment) (from the Bureau of Labor Statistics)

---

<!-- extra -->
<!-- 3. auto update model monthly -->
<!-- abstract away undersample fx from preprocess fx, and buy and sell from order fx, make oracle class -->
<!-- 4. automate saving model and preprocessors (every 2 weeks ) -->
<!-- 5. add live results on website / model vs buying and holding like alphahub - use dash or plotly? use pca visualization, tsne for higher dimensions, roc curve, etc-->

```

```