Python RSI KER Algo Trading Strategy That PRINTS Money

[BurtonM]

Python RSI KER Algo Trading Strategy That PRINTS Money​

This S&P 500 trading strategy comes from a video by Ali Casey at StatOasis on YouTube,

Thank you Ali for sharing all your data analysis, and backtesting.

RSI Trading Strategy That PRINTS Money (Tested & Proven)

It is important you watch the video link above to understand the what I have coded.

IMPORTANT: This Strategy is developed and backtested on the S&P500 Daily timeframe.

KER refers to the Kaufman Efficiency Ratio.

Entry/Exit conditions are:​

• Enter when RSI < entry_level & KER > threshold
• Exit when RSI > exit_level or max bars held
• StopLoss added to code

The Python code file is attached below this post for easy downloading.

I have coded 4 versions: MT4 version is here. MT5 version is here. TradingView version is here.

Here is a detailed guide on how to use the RSI Trading Bot Python code.​

1. Initial Setup

python
# Install required packages
pip install pandas numpy matplotlib yfinance

1. Save the Code

• Save the complete bot code in a file named rsi_ker_trading_bot.py
• Make sure to include all the imports and the complete class definition

1. Basic Usage Example

python
from rsi_ker_trading_bot import RSIKERTradingBot
from datetime import datetime, timedelta

# Create bot instance
bot = RSIKERTradingBot(
symbol="EURUSD=X", # Trading symbol
rsi_period=2, # RSI calculation period
rsi_entry_level=25, # RSI level to enter trades
rsi_exit_level=65, # RSI level to exit trades
ker_period=21, # KER calculation period
ker_threshold=0.2331, # KRT level to enter trades
max_bars_hold=5, # Maximum bars to hold position
lot_size=0.01, # Trading lot size
stop_loss=50 # Stop loss in points
)

# Define backtest period
start_date = datetime.now() - timedelta(days=365) # 1 year of data
end_date = datetime.now()

# Run backtest
results = bot.backtest(start_date, end_date)

# Display results
bot.plot_results(results)

1. Available Parameters

python
# You can customize these parameters when creating the bot
bot = RSIKERTradingBot(
symbol="EURUSD=X", # Available symbols: any Yahoo Finance symbol
# Forex pairs need =X suffix (e.g., "EURUSD=X")
# Stocks use regular ticker (e.g., "AAPL")

# Strategy Parameters
rsi_period=2, # Shorter period = more signals
# Typical ranges: 2-14

rsi_entry_level=25, # Lower value = fewer but stronger signals
# Typical ranges: 20-30

rsi_exit_level=65, # Higher value = longer holds
# Typical ranges: 60-80

ker_period=21,

ker_threshold=0.2331,

max_bars_hold=5, # Maximum holding period
# Prevents getting stuck in trades

# Trading Parameters
lot_size=0.01, # Trading size
# 0.01 = 1 micro lot
# 0.1 = 1 mini lot
# 1.0 = 1 standard lot

stop_loss=50 # Stop loss in points
# 50 = 50 pips for forex
)

1. Accessing Results

python
# After running backtest, you can access various data:
results = bot.backtest(start_date, end_date)

# Get all signals
signals = results[results['Signal'] != 0]

# Get entry points
entries = results[results['Signal'] == 1]

# Get exit points
exits = results[results['Signal'] == -1]

# Get RSI values
rsi_values = results['RSI']

# Get equity curve
equity = results['Equity_Curve']

# Calculate key metrics
total_return = results['Equity_Curve'].iloc[-1] / results['Equity_Curve'].iloc[0] - 1
sharpe_ratio = np.sqrt(252) * results['Strategy'].mean() / results['Strategy'].std()
max_drawdown = (results['Equity_Curve'] / results['Equity_Curve'].cummax() - 1).min()
number_of_trades = len(results[results['Signal'] != 0])

1. Testing Different Timeframes

python
# Test different periods
# Short period (1 month)
start_date = datetime.now() - timedelta(days=30)

# Medium period (6 months)
start_date = datetime.now() - timedelta(days=180)

# Long period (2 years)
start_date = datetime.now() - timedelta(days=730)

# Different data frequency
# When calling yf.download, you can use different intervals:
# '1m' - 1 minute
# '5m' - 5 minutes
# '15m' - 15 minutes
# '30m' - 30 minutes
# '1h' - 1 hour
# '1d' - 1 day

1. Error Handling

python
try:
bot = RSIKERTradingBot(symbol="EURUSD=X")
results = bot.backtest(start_date, end_date)
except Exception as e:
print(f"An error occurred: {e}")

1. For Live Trading

python
# While the bot is designed for backtesting, you can use process_bar()
# For live trading. Here's a basic example:

import time

while True:
# Get latest data
end = datetime.now()
start = end - timedelta(days=1) # Get last day of data
data = yf.download(bot.symbol, start=start, end=end, interval='1h')

# Process the latest bar
bot.process_bar(data)

# Wait for next bar
time.sleep(3600) # Wait 1 hour for next bar

Remember:​

• Always test with small amounts first
• Monitor the bot's performance regularly
• Adjust parameters based on your trading goals
• Keep track of all trades and performance metrics
• Consider market conditions when interpreting results