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Mean Reversion

Mean Reversion

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
2–8 days moves
Indicators Used
1
RSI
Win Rate (Backtest)
42.9%
Below 50% threshold
Avg Return / Trade
+0.21%
Per trade, after costs
Max Drawdown
-1.1%
Within typical range
Trades / Year
14
Small sample — interpret with caution
About the Mean Reversion Strategy
Mean Reversion captures the tendency of asset prices to move back toward their average after extreme moves. When NSE stocks or futures deviate sharply from their typical range, this strategy assumes they're more likely to reverse than continue, creating a tradeable edge.

NSE's high liquidity in large-cap stocks and index futures makes mean reversion particularly effective. The market experiences distinct intraday volatility patterns, especially around market open and during volatile sessions, when oversold and overbought conditions frequently reverse within the same day. This daily timeframe suits traders who want to avoid overnight gaps while capturing these mean-reverting price action patterns.

The strategy uses the Relative Strength Index to identify extremes. An RSI reading below 30 suggests oversold conditions where buying pressure typically emerges, while readings above 70 flag overbought territory where selling interest tends to return. The setup waits for price to push into these extreme zones on RSI, then enters when early reversal signals appear, betting on the stock or futures contract returning toward its equilibrium.

This approach works well in the equity and futures segments where NSE provides sufficient volume and tight spreads to execute entries and exits without slippage.
Who This Strategy Is For
This Beginner strategy suits Beginner Traders comfortable with a Daily timeframe and holding periods around 2–8 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: ASIANPAINT  ·  2024-05-13 to 2026-06-30
Total Return
+2.9%
CAGR
1.4%
Sharpe Ratio
0.82
Sortino Ratio
1.23
Calmar Ratio
1.27
Win Rate
42.9%
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 Excellent
Drawdown control Excellent
Trade frequency (sample size) Needs Caution
Sharpe ratio Good
Monthly Returns Heatmap
202420252026
Jan -0.4%
Feb
Mar 0%
Apr
May
Jun -0.1%
Jul -0.9% +0.5%
Aug +0.3% +1.4%
Sep +1.7% -0.2%
Oct -0.7%
Nov +1.3%
Dec
Positive return Negative return
Performance vs Nifty 50
Nifty 50 comparison isn't available for this backtest period yet.
Trade Distribution
14 Total
Profitable 6 (42.9%)
Losing 8 (57.1%)
↑ Avg Win +864
↓ Avg Loss -285
★ Best Trade +1,693
▼ Worst Trade -719
Returns Distribution
Recent Backtest Results
Period Symbol Capital Total Return CAGR Max Drawdown Win Rate Trades Sharpe Ratio View
2 Years (2024–2026) ASIANPAINT ₹100,000 +2.9% 1.4% -1.1% 42.9% 14 0.82 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:

Price 2+ standard deviations from 20-day mean
Index-heavy stock — institutional support likely
VIX elevated but not extreme — creates opportunity
Intraday panic sell or buy creates extreme
Technical support zone aligns with mean
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
Price 2+ standard deviations from 20-day mean
Index-heavy stock — institutional support likely
VIX elevated but not extreme — creates opportunity
Intraday panic sell or buy creates extreme
Technical support zone aligns with mean
✕ Avoid When
Momentum breakout day — price keeps moving away from mean
Bad fundamental news driving the move
Illiquid mid and small-cap stocks
Short squeeze or operator-driven move
Market in structural trend — mean keeps moving
Risk Management Rules
Risk Per Trade
1.0%
of total capital
Min Capital
₹30,000
Hold Period
2–8 days
Segment
Equity, Futures
Common Mistakes to Avoid
⚠️ Mean reversion strategies lose the most money when a stock is actually trending, not ranging — the biggest mistake is applying this strategy blindly without checking whether the broader trend is against the trade.
Full Backtest Report

Backtested on ASIANPAINT · 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
TITAN 2024-01-18 · Long
WIN
Entry ₹
₹3,482.00
Stop Loss ₹
₹3,348.00
Target ₹
₹3,748.00
Exit ₹
₹3,745.00
Titan Company traded 2.4 standard deviations below its 20-day mean (₹3,680) after broader market weakness. RSI at 30. Strong brand with no fundamental news. Entered at ₹3,482 with stop ₹3,348 (2x ATR below). Mean reversion target was the 20-day EMA at ₹3,680 — price overshot to ₹3,745 in 14 sessions.
Strategy Parameters
ParameterDefaultMinMaxTypeDescription
ema_period 20 10 100 integer EMA used as mean
std_period 20 10 50 integer Standard deviation period
entry_std 2.0 1.0 4.0 decimal Enter when price is N std devs from mean
exit_std 0.5 0.0 1.5 decimal Exit when price returns to N std devs from mean
atr_mult 2.0 0.5 4.0 decimal Stop = ATR × multiplier
Frequently Asked Questions
Mean Reversion trading bets that assets which deviate significantly from their historical average price or statistical mean will eventually return toward that average. This is supported by the statistical concept that extreme values tend to be followed by values closer to the long-term average, reflecting supply-demand rebalancing.
High-quality large-cap stocks with stable business models and no fundamental negative catalysts show the strongest mean reversion behavior after temporary price extremes. Range-bound market phases (ADX below 20) amplify mean reversion opportunities, while strong trending markets make mean reversion strategies prone to significant losses.
Calculate how many standard deviations or ATR multiples price has moved from a reference mean (20-day SMA, VWAP). Deviations of 2+ standard deviations or 2.5+ ATR multiples from the mean represent statistically extreme conditions on NSE large-caps, providing the strongest historical mean reversion setup probability.
Mean reversion strategies have theoretically unlimited risk because an asset that appears to have deviated too far from its mean can continue deviating further, particularly if fundamental reasons (news, earnings, regulatory changes) justify the extreme price. Always use hard stop losses and never assume a reversion is guaranteed.
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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.