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RobinhoodBot

Trading bot for Robinhood accounts

For more info: https://medium.com/@kev.guo123/building-a-robinhood-stock-trading-bot-8ee1b040ec6a

5/1/19: Since Robinhood has updated it's API, you now have to enter a 2 factor authentication code whenever you run the script. To do this, go to the Robinhood mobile app and enable two factor authentication in your settings. You will now receive an SMS code when you run the script, which you have to enter into the script.

This project supports Python 3.7+

🚀 Quick Start

# 1. Clone the repository
git clone https://github.com/txavier/RobinhoodBot.git
cd RobinhoodBot

# 2. Create and activate virtual environment
python3 -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# 3. Install dependencies
cd robinhoodbot
pip install -r requirements.txt

# 4. Configure your credentials
cp config.py.sample config.py
# Edit config.py with your Robinhood credentials:
#   - rh_username: Your Robinhood email
#   - RH_DEVICE_TOKEN: Your 16-character device token
#   - rh_email/rh_mail_password: For SMS notifications (optional)

# 5. Setup Robinhood
# - Enable 2FA in Robinhood app (Settings > Security > Two-Factor Authentication)
# - Create a watchlist named "Default" with stocks to monitor
# - Create a watchlist named "Exclusion" for stocks to ignore

# 6. Run the bot
python main.py          # Single run
./run.sh               # Continuous loop (runs every 7 minutes)

Installation (Detailed)

git clone https://github.com/txavier/RobinhoodBot.git
cd RobinhoodBot/
pip install -r requirements.txt
cp config.py.sample config.py # add auth info and watchlist name to monitor after copying

Running the Bot

In RobinHood create a watchlist named "Exclusion". This will be the watchlist that you will use to tell the bot to ignore the stock tickers contained within.

cd RobinhoodBot/robinhoodbot
python3 main.py

To loop: 1 once an hour

cd RobinboodBot/robinhoodbot (If outside of root directory)
./run.sh # uses bash

Changes to robin_stocks library

  • Added @cache to def get_name_by_symbol(symbol):
  • Added @cache to def get_name_by_url(url):
  • Added @cache to def get_symbol_by_url(url):
  • Added None Parameter to order Afterwards, be sure to run /> pip install .

VENV

In the event you want to use Venv instead of anaconda, activate it with the follwing command.

venv: /home/theo/dev/.venv/bin/activate ~/> source dev/.venv/bin/activate

Backtesting & Parameter Optimization

The bot includes intraday backtesting and genetic optimization modules that accurately simulate day trading using hourly data - matching the actual main.py trading logic.

Intraday Backtesting (Day Trading Simulation)

The backtest_intraday.py module provides accurate day trading simulation that matches the actual main.py trading logic. Unlike the basic backtest which uses daily data, this module simulates hourly trading with all the same conditions the real bot uses.

Why Intraday Backtesting?

The main bot (main.py) is a day trading application that:

  • Uses hourly SMA crossovers (not daily)
  • Makes multiple trades per day
  • Has aggressive take-profit targets (1.88% optimized)
  • Includes many conditional filters before buying/selling

The intraday backtester generates hourly data and applies all the same filters as main.py, providing accurate day trading simulation.

Quick Start

cd robinhoodbot/

# Basic intraday backtest (60 days, 5 symbols)
python backtest_intraday.py --symbols AAPL,MSFT,GOOGL,NVDA,TSLA --days 60

# Reproducible test with seed
python backtest_intraday.py --symbols AAPL,MSFT --days 30 --seed 42

# Custom parameters
python backtest_intraday.py --symbols AAPL --days 90 --capital 25000 --take-profit 1.0 --stop-loss 3

Main.py Matching Features

The intraday backtester accurately simulates these main.py conditions:

Buy Filters (all must pass)

Filter Description Config Flag
Market Uptrend 2 of 3 indices (SPY, DIA, NDAQ) must be up today --no-market-filter
No Major Downtrend Not in weekly market downtrend --no-market-filter
EOD Filter No buying after 1:30pm (inflection protection) --no-eod-filter
Price > 5hr Ago Price must be higher than 5 hours ago --no-price-5hr-check
Slope Ordering Prioritize stocks with positive momentum (slope > 0.0008) --no-slope-ordering
Price Cap Stock price must be under $2,100 --no-price-cap
Day Trade Limit Respect pattern day trader limits Built-in

Sell Triggers (in priority order)

Trigger Description
Stop Loss Sell if loss exceeds stop_loss_percent (default 5%)
Take Profit Sell if gain exceeds take_profit_percent (default 0.70%)
Sudden Drop Sell if 10% drop in 2hr OR 15% drop in 1hr
Profit Before EOD Sell profitable positions after 1:30pm
Death Cross Sell when short SMA crosses below long SMA

Dynamic SMA Adjustment

The backtest simulates main.py's dynamic SMA periods:

  • Default: n1=20, n2=50
  • In Downtrend: n1=14 (more conservative)
  • After Take Profit + Traded Today: n1=5, n2=7 (more aggressive re-entry)

Command Line Options

# Full options example
python backtest_intraday.py \
    --symbols AAPL,MSFT,GOOGL,NVDA,TSLA \
    --days 60 \
    --capital 10000 \
    --short-sma 20 \
    --long-sma 50 \
    --take-profit 0.7 \
    --stop-loss 5 \
    --max-positions 5 \
    --seed 42 \
    --output intraday_results.json

# Simple mode (disable ALL filters - like old backtest)
python backtest_intraday.py --symbols AAPL,MSFT --days 30 --simple-mode

# Disable specific filters
python backtest_intraday.py --symbols AAPL --no-market-filter --no-eod-filter

Options Reference

Option Description Default
--symbols Comma-separated stock symbols AAPL,MSFT,GOOGL
--days Trading days to simulate 30
--capital Initial capital 10000
--short-sma Short SMA period (hours) 20
--long-sma Long SMA period (hours) 50
--take-profit Take profit % 0.70 (from config)
--stop-loss Stop loss % 5 (from config)
--golden-cross-hours Hours to look back for cross 24
--max-positions Max concurrent positions 5
--close-eod Close all positions at end of day False
--seed Random seed for reproducibility None
--output Output JSON file None
--quiet Suppress trade-by-trade output False

Filter Toggle Options

Option Description
--simple-mode Disable ALL main.py filters
--no-market-filter Disable market trend filtering
--no-eod-filter Disable no-buy-after-1:30pm rule
--no-profit-before-eod Disable profit_before_eod sell
--no-price-5hr-check Disable price > 5hr ago check
--no-dynamic-sma Disable dynamic SMA adjustment
--no-slope-ordering Disable slope-based ordering
--no-price-cap Disable price cap filter
--price-cap Set custom price cap (default: 2100)

Example Output

======================================================================
INTRADAY BACKTESTING - ACCURATE MAIN.PY SIMULATION
======================================================================
Symbols: AAPL, MSFT, GOOGL, NVDA, TSLA
Trading Days: 60
Initial Capital: $10,000.00
Strategy: Hourly SMA(20/50)
Take Profit: 0.7%
Stop Loss: 5%

⚙️  MAIN.PY MATCHING FEATURES:
   Market Filter: ON
   EOD Filter (no buy after 1:30pm): ON
   Profit Before EOD Sell: ON
   Price > 5hr Ago Check: ON
   Dynamic SMA: ON
   Slope Ordering: ON
   Price Cap ($2100): ON
======================================================================

  🟢 BUY  AAPL: 20sh @ $99.79 [golden_cross, slope=0.2561] Cost: $1995.80
  ✅ SELL AAPL: 20sh @ $100.82 [take_profit] P/L: $+20.60 (+1.03%)
  📊 Day 2025-10-10 [uptrend]: 6 trades, P/L: $+6.53, Portfolio: $10,006.53
  ...

======================================================================
BACKTEST RESULTS (ACCURATE MAIN.PY SIMULATION)
======================================================================

📅 Period: 2025-10-07 to 2026-02-25
📈 Symbols: AAPL, MSFT, GOOGL, NVDA, TSLA

💰 FINANCIAL SUMMARY
   Initial Capital:  $    10,000.00
   Final Capital:    $    10,262.91
   Total Return:     $      +262.91 (+2.63%)

📊 PERFORMANCE METRICS
   Max Drawdown:     $        87.46 (0.87%)
   Sharpe Ratio:             3.0699

🎯 TRADING STATISTICS
   Trading Days:                102
   Total Trades:                142
   Trades/Day:                 0.70
   Winning Trades:               48
   Losing Trades:                23
   Win Rate:                 67.61%
   Avg Win:          $        11.31
   Avg Loss:         $        12.17
   Profit Factor:              1.94

🚫 REJECTED BUY SIGNALS (main.py filters)
   Total Rejected:             2972
   Market Downtrend:            772
   Major Downtrend:              55
   After 1:30pm EOD:            414
   Price < 5hr Ago:               8
   Low Slope:                  1642
   Day Trade Limit:              81

🔧 MAIN.PY MATCHING FEATURES
   market_filter: ✅ ON
   eod_filter: ✅ ON
   profit_before_eod: ✅ ON
   price_5hr_check: ✅ ON
   dynamic_sma: ✅ ON
   slope_ordering: ✅ ON
   price_cap: ✅ ON
======================================================================

Comparing Accurate vs Simple Mode

Run both modes to see the impact of main.py's filters:

# Accurate mode (all filters ON)
python backtest_intraday.py --symbols AAPL,MSFT,GOOGL,NVDA,TSLA --days 60 --seed 42

# Simple mode (all filters OFF)
python backtest_intraday.py --symbols AAPL,MSFT,GOOGL,NVDA,TSLA --days 60 --seed 42 --simple-mode

Typical Comparison Results

Metric Accurate (filters ON) Simple (filters OFF)
Total Return +2.63% +3.69%
Max Drawdown 0.87% 2.72%
Sharpe Ratio 3.07 2.00
Profit Factor 1.94 1.33
Avg Loss $12.17 $35.44
Rejected Buys 2,972 254

Key Insight: The filters produce lower raw returns but much better risk-adjusted returns with 70% less drawdown and 50% better profit factor.

Intraday Genetic Optimizer (Day Trading Parameter Evolution)

The genetic_optimizer_intraday.py module uses genetic algorithms to evolve trading parameters using hourly data - matching actual day trading performance.

Quick Start

cd robinhoodbot/

# Basic intraday optimization (recommended)
python genetic_optimizer_intraday.py --symbols AAPL,MSFT,GOOGL --generations 15 --population 20

# Full optimization with reproducibility
python genetic_optimizer_intraday.py \
    --symbols AAPL,MSFT,GOOGL,NVDA,TSLA \
    --days 60 \
    --capital 10000 \
    --population 25 \
    --generations 20 \
    --seed 42 \
    --output my_intraday_optimization.json

# Quick test run
python genetic_optimizer_intraday.py --symbols AAPL --generations 5 --population 10 --seed 42

# Use parallel processing for faster optimization (auto-detects CPU cores)
python genetic_optimizer_intraday.py --symbols AAPL,MSFT,GOOGL,NVDA,TSLA,META,AMZN -w 0

Parallel Processing

The optimizer supports multiprocessing to speed up fitness evaluation:

Option Description
-w 0 or --workers 0 Auto-detect (uses cpu_count - 1 workers)
-w 1 or --workers 1 Sequential execution (no parallelism, useful for debugging)
-w N or --workers N Use exactly N parallel workers

Example with 8 workers:

python genetic_optimizer_intraday.py --symbols AAPL,MSFT,GOOGL -w 8

Parameters Being Optimized

The intraday optimizer evolves these parameters (all designed for hourly trading):

Parameter Range Description
short_sma 5-50 hours Short-term SMA (~1-7 trading days)
long_sma 20-100 hours Long-term SMA (~3-14 trading days)
golden_cross_hours 7-72 hours Lookback for golden cross signals
stop_loss_pct 1-15% Stop loss percentage
take_profit_pct 0.3-3.0% Take profit percentage (tighter for day trading)
position_size_pct 5-30% Maximum position size
slope_threshold 0.0001-0.002 Slope filter for order_symbols_by_slope
price_cap_value $500-$5000 Maximum stock price to consider

Advanced: Filter Optimization

Use --optimize-filters to also evolve which main.py filters to enable/disable:

python genetic_optimizer_intraday.py --symbols AAPL,MSFT --optimize-filters --generations 20

This will evolve whether each filter (market trend, EOD, slope ordering, etc.) should be ON or OFF for optimal performance.

Fitness Function

The intraday fitness function is tuned for day trading (emphasizes risk-adjusted returns):

Metric Weight Description
Total Return 25% Overall profit/loss percentage
Sharpe Ratio 30% Risk-adjusted return (higher weight for day trading)
Win Rate 15% Percentage of winning trades
Profit Factor 15% Gross profits / gross losses
Max Drawdown 10% Penalty for large drawdowns
Trades/Day 5% Bonus for ~1 trade/day activity level

Additional penalties:

  • No trades: -2.0 fitness
  • Fewer than 5 trades: -0.5 fitness
  • Bonus for good win/loss ratio

Command Line Options

# Full options reference
python genetic_optimizer_intraday.py \
    --symbols AAPL,MSFT,GOOGL \
    --days 60 \
    --capital 10000 \
    --population 25 \
    --generations 20 \
    --mutation-rate 0.15 \
    --crossover-rate 0.7 \
    --optimize-filters \
    --output intraday_optimization.json \
    --seed 42

Options Reference

Option Short Description Default
--symbols -s Comma-separated stock symbols AAPL,MSFT,GOOGL
--days -d Trading days to simulate 60
--capital -c Initial capital 10000
--population -p Population size 20
--generations -g Number of generations 15
--mutation-rate -m Mutation probability (0.0-1.0) 0.15
--crossover-rate Crossover probability (0.0-1.0) 0.7
--optimize-filters Also optimize filter on/off settings False
--max-positions Maximum concurrent positions 5
--validate-real Validate results against tradehistory-real.json False
--output -o Output JSON file genetic_optimization_intraday_result.json
--seed Random seed for reproducibility None
--quiet -q Suppress verbose output False

Example Output

======================================================================
INTRADAY GENETIC ALGORITHM OPTIMIZER
======================================================================
Symbols: AAPL, MSFT, GOOGL
Trading Days: 60
Initial Capital: $10,000.00
Population: 20
Generations: 15
Mutation Rate: 0.15
Crossover Rate: 0.7
Optimize Filters: False
======================================================================

--- Generation 1/15 ---
  Evaluating 1/20: SMA(20/50) GC:24h SL:5.0% TP:0.70% Slope:0.0008 | Fit:0.3521
  ...

--- Generation 15/15 ---
  Best:  SMA(15/40) GC:18h SL:3.0% TP:0.85% Slope:0.0012 | Fit:0.6234

======================================================================
EVOLUTION SUMMARY
======================================================================
 Gen |   Best Fit |    Avg Fit |  Return % |   Sharpe | Win % | Tr/Day
----------------------------------------------------------------------------
   1 |     0.3521 |     0.1823 |     +1.24 |     1.52 |  62.0 |   0.85
   ...
  15 |     0.6234 |     0.4521 |     +3.85 |     2.41 |  71.2 |   0.92
----------------------------------------------------------------------------
Improvement: Fitness +0.2713 | Return +2.61%

======================================================================
BEST INTRADAY CONFIGURATION FOUND
======================================================================

# Add these to your config.py:
# Note: SMA values are in HOURS (for hourly data)

# SMA Settings (Hourly)
# short_sma_hours = 15  # ~2.1 trading days
# long_sma_hours = 40   # ~5.7 trading days
golden_cross_buy_days = 2  # 18 hours

# Risk Management
stop_loss_percent = 3.0
take_profit_percent = 0.85

# Position Sizing
purchase_limit_percentage = 18.0

# Main.py Specific Settings
# slope_threshold = 0.0012  # For order_symbols_by_slope
price_cap = 1800

# Performance Metrics:
# Total Return: +3.85%
# Win Rate: 71.2%
# Sharpe Ratio: 2.4100
# Max Drawdown: 1.52%
# Profit Factor: 2.31
# Total Trades: 98
# Trades/Day: 0.92
# Avg Win: $14.21
# Avg Loss: $11.82
# Fitness Score: 0.6234
======================================================================

Tips for Best Results

  1. Use 60+ Trading Days: More data captures various market conditions
  2. Population 20-30: Good balance of exploration vs computation time
  3. Generations 15-25: Allows proper convergence
  4. Multiple Runs: Run with different seeds and compare results
  5. Diverse Symbols: Include different sectors (tech, finance, energy)
  6. Start Without Filter Optimization: Get good base parameters first, then try --optimize-filters
  7. Validate Results: Run backtest_intraday.py with the optimized parameters to confirm

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Trading bot for Robinhood accounts

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