RSI (Relative Strength Index)
Momentum oscillator that measures the speed and change of price movements, helping traders identify overbought, oversold, and reversal conditions
Technical Overview
The Relative Strength Index (RSI) is a momentum oscillator developed by J. Welles Wilder Jr. that measures the speed and magnitude of recent price changes. RSI oscillates between 0 and 100, providing a normalized value that helps traders identify overbought, oversold, and reversal conditions.
Key Insight: RSI values above 70 typically indicate overbought conditions, while values below 30 suggest oversold conditions. RSI is widely used to spot potential reversal points, confirm trend strength, and filter out false signals in both trending and ranging markets.
How RSI Works
What RSI Actually Measures
Think of RSI as a “momentum thermometer” that answers: “Is the market overbought, oversold, or balanced?”
Step 1: Measure Gains & Losses
RSI looks at average price gains and losses over the last 14 periods
Step 2: Calculate Relative Strength
It compares the magnitude of recent gains to recent losses
Step 3: Output RSI Value
Outputs a value from 0-100 showing momentum and overbought/oversold status
Reading RSI Values
Market may be due for a bounce or reversal upward
Balanced momentum, no strong trend
Market may be overextended, possible reversal or pullback
Key RSI Components
Average Gain
The mean of all positive price changes over the lookback period (usually 14 days), used to measure upward momentum.
Average Loss
The mean of all negative price changes over the lookback period, used to measure downward momentum.
Relative Strength (RS)
The ratio of average gain to average loss, forming the core of the RSI calculation.
RSI Line
The final oscillator value, ranging from 0 to 100, that signals overbought, oversold, or neutral market conditions.
Strategy Integration
5-Day Predictions
How RSI Data Powers Machine Learning:
- Feature Input: RSI values are used as features for the RandomForest model alongside ADX, MACD, and others
- Overbought/Oversold Detection: Model learns to recognize reversal zones and momentum extremes
- Divergence Analysis: RSI divergences help the model spot early trend changes
- Breakout Confirmation: RSI confirms the strength of price breakouts
- Noise Filtering: Neutral RSI helps the model avoid choppy, directionless markets
Real Impact: RSI helps the model time entries and exits based on momentum shifts and reversal signals
1-Year Predictions
How RSI Enhances Long-Term Forecasting:
- Cycle Analysis: Model uses RSI to identify major market cycles and regime shifts
- Persistence Modeling: Extended RSI extremes help spot long-lasting bull/bear phases
- Volatility Context: RSI trends help predict periods of high/low volatility
- Sector Rotation: Model learns when strong RSI favors growth vs defensive sectors
- Macro Confirmation: RSI combined with economic indicators improves macro trend calls
Real Impact: RSI helps the long-term model time major allocation changes and sector rotations
RSI Level Interpretation
Why Use RSI in Trading?
- Overbought and oversold condition identification
- Momentum divergence detection for trend reversal signals
- Entry and exit timing optimization
- Risk management through extreme level awareness
- Confirmation of price breakouts and reversals
- Market regime identification for strategy selection
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