scarlett: Robinhood analytics and algorithmic trading

scarlett 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

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('src')
from Algotrader import Scarlett  # noqa autopep8

sl = Scarlett()

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

  • 
  • EOD OHLCV
  • Intraday OHLCV 5 min ticks

• Actions

  • 
  • Splits

• Sentiment

  • News Sentiment
  • Social Sentiment
  • Analyst Recommendations

• Company / Micro

  • Profile (Sector, # of Employees)
  • Earnings
  • Cash Flow
  • CEO Compensation

• Government / Macro

  • Unemployment Rate
  • Real GDP
  • US Recession Probabilities

• Market

  • General Volatility (VIX)
  • Sector Performance

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