Skip to main content

An algorithmic trading platform

Project description

hyperdrive: Robinhood analytics and algorithmic trading

Build Pipeline Dev Pipeline New Release

hyperdrive is a project to obtain stock data, create trading strategies, test against historical data (backtesting), and deploy strategies for algorithmic trading.

Getting Started

Prerequisites

You will need Python 3.8+ and a Robinhood account.

Place your credentials in a file named .env in the project root directory. Follow this structure:

RH_USERNAME=...
RH_PASSWORD=...
RH_2FA=...
IEXCLOUD=...

Installation

To install the necessary packages, run

pip install -r requirements.txt

Testing

python -m pytest -s -v test/test_filename -k function_name

Use

Making Scripts

To make a script, create a new .py file in the scripts/ dir with the following code:

import sys
sys.path.append('hyperdrive')
from Algotrader import HyperDrive  # noqa autopep8

drive = HyperDrive()

Features:

  • Broker authentication
  • Automated data storage
  • Backtesting engine
  • Monte Carlo simulations
  • Plotting and technical analysis
  • Model training
  • Strategy definition (start with buy and hold)
  • Buy and sell functionality
  • Live trading
  • Documentation

Check out the Roadmap for progress ...

Auth

Using Robinhood 2FA, we can simply provide our MFA one-time password in the .env file to login to Robinhood (via pyotp).

Data

  • Price and Volume
    • Symbols
    • OHLC
    • Intraday
  • Actions
    • Dividends
    • Splits
  • Sentiment
  • Company / Micro
    • Profile (Sector, # of Employees)
    • Earnings
    • Cash Flow
    • CEO Compensation
  • Government / Macro
    • Unemployment
    • Real GDP
    • US Recession Probabilities
  • Market
    • General Volatility (VIX)
    • Sector Performance

Strategy

  • Buy and Hold
  • Indicator/TA based
  • Portfolio Optimization

Trading

  • Buy and Sell

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

interstellar-1.0.3.tar.gz (26.2 kB view details)

Uploaded Source

Built Distribution

interstellar-1.0.3-py3-none-any.whl (20.4 kB view details)

Uploaded Python 3

File details

Details for the file interstellar-1.0.3.tar.gz.

File metadata

  • Download URL: interstellar-1.0.3.tar.gz
  • Upload date:
  • Size: 26.2 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.6.0 importlib_metadata/4.8.1 pkginfo/1.7.1 requests/2.22.0 requests-toolbelt/0.9.1 tqdm/4.60.0 CPython/3.8.5

File hashes

Hashes for interstellar-1.0.3.tar.gz
Algorithm Hash digest
SHA256 119131fa6f70596d879f41eac6e5919d1857c0c20a59bfd670149072d0e64bec
MD5 8c1830a509dc876a32c44644fc55f6a9
BLAKE2b-256 6a125eb80578ecd511dc3f1a66ff415ecacee91351e39f13372c1ff5f8fd02d5

See more details on using hashes here.

File details

Details for the file interstellar-1.0.3-py3-none-any.whl.

File metadata

  • Download URL: interstellar-1.0.3-py3-none-any.whl
  • Upload date:
  • Size: 20.4 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.6.0 importlib_metadata/4.8.1 pkginfo/1.7.1 requests/2.22.0 requests-toolbelt/0.9.1 tqdm/4.60.0 CPython/3.8.5

File hashes

Hashes for interstellar-1.0.3-py3-none-any.whl
Algorithm Hash digest
SHA256 4e334bc2913fa0ba6cf0ed1004197c928022afba2fb82dd7483bc122d31ee6aa
MD5 38510397fa1b5f6cbcc3966dcbaa1d57
BLAKE2b-256 0e8d4296c52511f6e0714c827b72167eb077e02388ae03926750154ca43e2d4f

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page