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Adaptive RSI

Adaptive RSI

Beginner Daily 9/10 Popularity

Uses the Relative Strength Index to identify overbought and oversold conditions on NSE charts. A beginner approach that works well as both a standalone signal and a filter for other strategies.

Complexity
Beginner
Easy to implement
NSE Suitability
High
9.2 / 10 score
Timeframe
Daily
Short to medium term
Best For
Beginner Traders
5–20 days moves
Indicators Used
1
RSI
Win Rate (Backtest)
35.3%
Below 50% threshold
Avg Return / Trade
+0%
Per trade, after costs
Max Drawdown
-3.4%
Within typical range
Trades / Year
17
Small sample — interpret with caution
About the Adaptive RSI Strategy
Adaptive RSI is a momentum strategy designed to capture mean-reversion moves in trending markets by responding to overbought and oversold conditions. The strategy adjusts its RSI thresholds based on recent volatility, allowing it to avoid false signals during low-volatility periods while remaining responsive during market dislocations.

On the NSE, this approach is particularly useful given the market's tendency toward sharp intraday swings and the pronounced volatility clustering in equity futures around major economic announcements and market opens. Indian equities often experience rapid reversals after extended runs, making RSI-based mean reversion viable on the daily timeframe where noise is reduced compared to intraday charts.

The setup identifies when RSI crosses above an adaptive oversold level during downtrends or below an adaptive overbought level during uptrends. Rather than using fixed 30 and 70 thresholds, the strategy recalibrates boundaries based on the rolling standard deviation of RSI values, making it responsive to changing market conditions. Entry signals occur at these adaptive extremes, with exits typically placed at the opposite threshold or when price action invalidates the setup. This framework works across both equity cash and futures segments, though liquidity in larger-cap stocks and active future contracts ensures reliable execution.
Who This Strategy Is For
This Beginner strategy suits Beginner Traders comfortable with a Daily timeframe and holding periods around 5–20 days. It's built for the Equity, Futures segment on NSE, so it fits traders who can check positions without needing intraday execution speed. Because it uses a small, well-known set of indicators, it's a reasonable starting point if you're new to systematic NSE trading.
Equity Curve (Backtest) HIGH QUALITY
Tested on: UPL  ·  2024-05-13 to 2026-06-30
Total Return
+0.1%
CAGR
0%
Sharpe Ratio
0.03
Sortino Ratio
0.05
Calmar Ratio
0
Win Rate
35.3%
NSE Market Fit
9 OUT OF 10
Very High Fit
This strategy is well-suited for current NSE market conditions.
Win rate quality Needs Caution
Risk-adjusted return Needs Caution
Drawdown control Excellent
Trade frequency (sample size) Good
Sharpe ratio Needs Caution
Monthly Returns Heatmap
202420252026
Jan +0.4%
Feb -1%
Mar +0.3%
Apr
May -1%
Jun -0.6%
Jul +1.6%
Aug -0.5%
Sep +1.9% -0.3%
Oct -0.5%
Nov -1% +1.7%
Dec -0.2% -0.6%
Positive return Negative return
Performance vs Nifty 50
Nifty 50 comparison isn't available for this backtest period yet.
Trade Distribution
17 Total
Profitable 6 (35.3%)
Losing 11 (64.7%)
↑ Avg Win +984
↓ Avg Loss -530
★ Best Trade +1,883
▼ Worst Trade -1,049
Returns Distribution
Recent Backtest Results
Period Symbol Capital Total Return CAGR Max Drawdown Win Rate Trades Sharpe Ratio View
2 Years (2024–2026) UPL ₹100,000 +0.1% 0% -3.4% 35.3% 17 0.03 View
💡 Tip: Backtest on more data to increase confidence. Our users get best results with 3+ years of backtesting. Run Extended Backtest
How It Works (Quick Overview)
1
Step 1
Identify the market context — determine if conditions are trending or ranging, and confirm the higher timeframe direction
2
Step 2
Wait for the specific entry signal defined by the strategy rules — do not enter without full confirmation
3
Step 3
Execute with pre-defined stop loss and target — manage the trade according to the exit rules without discretionary override
View Detailed Rules & Setup →

Best Market Conditions

This strategy performs best in:

RSI adapts quickly in trending markets — use shorter period
RSI slows down in ranging markets — use longer period
Efficiency ratio confirms market type before signal
Trending sectors — Adaptive RSI stays above 60
Divergence signal more reliable in slow RSI mode
How This Strategy Works
1
Identify the market context — determine if conditions are trending or ranging, and confirm the higher timeframe direction
2
Wait for the specific entry signal defined by the strategy rules — do not enter without full confirmation
3
Execute with pre-defined stop loss and target — manage the trade according to the exit rules without discretionary override
Entry & Exit Rules
✓ Entry Conditions
RSI adapts quickly in trending markets — use shorter period
RSI slows down in ranging markets — use longer period
Efficiency ratio confirms market type before signal
Trending sectors — Adaptive RSI stays above 60
Divergence signal more reliable in slow RSI mode
✕ Avoid When
Market type transition — efficiency ratio changing
News events disrupting the trend pattern
Low-liquidity stocks
When both fast and slow RSI give conflicting signals
Overnight gap distorting the RSI calculation
Risk Management Rules
Risk Per Trade
1.0%
of total capital
Min Capital
₹30,000
Hold Period
5–20 days
Segment
Equity, Futures
Common Mistakes to Avoid
⚠️ Momentum strategies can give back gains quickly once momentum fades. A common error is not tightening stops as profits build, which lets a winning trade round-trip back to breakeven or a loss.
Full Backtest Report

Backtested on UPL · 2024-05-13 to 2026-06-30 · Capital ₹100,000

Equity Curve

Live tracking coming soon

We're building forward-tested, paper-trade tracking for this strategy so you can see how it performs on live NSE data — not just historical backtests. Check back soon.

Sample Trade Walkthrough
ASIANPAINT 2024-01-09 · Long
WIN
Entry ₹
₹3,312.00
Stop Loss ₹
₹3,188.00
Target ₹
₹3,560.00
Exit ₹
₹3,558.00
Asian Paints Adaptive RSI dropped to 28 (slow mode — ranging market) on Jan 9 — oversold in the context of overall uptrend. Efficiency ratio was low — confirming a range phase. A bullish pin bar formed at ₹3,188 support. Adaptive RSI then shifted to fast mode as price started moving, quickly rising above 50. Entered ₹3,312, stop ₹3,188, target ₹3,560.
Strategy Parameters
ParameterDefaultMinMaxTypeDescription
base_period 14 7 30 integer Base RSI period before adaptation
fast_period 5 3 15 integer RSI period in trending markets
slow_period 21 14 40 integer RSI period in ranging markets
oversold 30 20 40 integer Buy signal below this level
overbought 70 60 80 integer Sell signal above this level
atr_mult 2.0 0.5 4.0 decimal Stop = ATR × multiplier
rr 2.0 1.0 5.0 decimal Target RR
Frequently Asked Questions
Adaptive RSI adjusts its lookback period based on market conditions. When volatility is high and markets are trending, the period shortens to be more responsive. When markets are quiet, the period lengthens to filter noise. This prevents the standard RSI problem of being too slow in fast markets and too noisy in quiet ones.
Use Adaptive RSI crossovers of the 50 level as trend signals — crossing above 50 is bullish, below 50 is bearish. For entries, wait for Adaptive RSI to pull back toward 40-45 in an uptrend and bounce — this is your entry zone. The adaptive nature means the 50 level is more meaningful than with standard RSI.
Use 65 as overbought and 35 as oversold rather than the standard 70/30. Because the period adapts, extremes are less frequent and more meaningful. An Adaptive RSI reading above 70 indicates an unusually strong trend rather than just an overbought condition and should not be shorted immediately.
Adaptive RSI outperforms standard RSI during trend transitions — when markets shift from trending to ranging or vice versa. This happens frequently around NSE quarterly results seasons and RBI policy announcements. The adaptive period catches these transitions faster while the standard RSI is still adjusting.
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SEBI Compliance Disclaimer

MomentumIQ is an educational platform for strategy research and backtesting. We do not provide investment advice, recommendations, or tips. All backtest results are hypothetical, based on historical data, and for educational purposes only. Past performance is not indicative of future results. Backtested results may not account for brokerage, slippage, taxes, or other real-world costs. Please consult a SEBI-registered investment advisor before making any investment decisions.