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Anti Martingale

Anti Martingale

Beginner Daily

A beginner trading strategy well-suited for NSE markets. Uses systematic, rule-based logic to identify high-probability entry and exit points with defined risk on every trade.

Complexity
Beginner
Easy to implement
NSE Suitability
High
5.2 / 10 score
Timeframe
Daily
Short to medium term
Best For
Beginner Traders
5–15 days moves
Indicators Used
2
Price Action, Volume
Win Rate (Backtest)
0%
Below 50% threshold
Avg Return / Trade
-0.54%
Per trade, after costs
Max Drawdown
-1.8%
Within typical range
Trades / Year
3
Small sample — interpret with caution
About the Anti Martingale Strategy
The Anti Martingale strategy operates on the principle of increasing position size after winning trades while decreasing after losses, capturing momentum reversals during mean reversion cycles. On the NSE, this approach leverages the market's tendency to correct after sharp moves, particularly during the high-liquidity morning and mid-session windows when institutional participation is strongest.

The strategy identifies setups where price has moved significantly from a recent support or resistance level, accompanied by declining volume. This divergence between price extension and volume weakness often signals exhaustion. Traders enter on the anticipated reversal and scale into profitable positions using predetermined increments, allowing winning trades to compound while limiting exposure during losing streaks.

NSE's equity segment exhibits distinct volatility patterns driven by opening gaps and intra-day consolidations, making daily timeframes ideal for capturing these reversals. The strategy works best in stocks with sufficient liquidity to accommodate position scaling without slippage. Risk management is critical, as the compounding nature of position increases requires strict stop-loss discipline to prevent catastrophic drawdowns when reversals fail to materialize.
Who This Strategy Is For
This Beginner strategy suits Beginner Traders comfortable with a Daily timeframe and holding periods around several days. It's built for the Equity 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: ADANIPORTS  ·  2024-05-13 to 2026-06-30
Total Return
-1.6%
CAGR
-0.9%
Sharpe Ratio
-0.69
Sortino Ratio
-1.04
Calmar Ratio
-0.5
Win Rate
0%
NSE Market Fit
5 OUT OF 10
Moderate 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) Needs Caution
Sharpe ratio Needs Caution
Monthly Returns Heatmap
20252026
Jan
Feb
Mar -1%
Apr
May
Jun
Jul -0.6%
Aug
Sep -0.1%
Oct
Nov
Dec
Positive return Negative return
Performance vs Nifty 50
Nifty 50 comparison isn't available for this backtest period yet.
Trade Distribution
3 Total
Profitable 0 (0%)
Losing 3 (100%)
↑ Avg Win +0
↓ Avg Loss -545
★ Best Trade +-51
▼ Worst Trade -965
Returns Distribution
Recent Backtest Results
Period Symbol Capital Total Return CAGR Max Drawdown Win Rate Trades Sharpe Ratio View
2 Years (2024–2026) ADANIPORTS ₹100,000 -1.6% -0.9% -1.8% 0% 3 -0.69 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:

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
Risk Management Rules
Risk Per Trade
1.0%
of total capital
Min Capital
₹30,000
Hold Period
5–15 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 ADANIPORTS · 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.

No sample trades added yet for this strategy.

Strategy Parameters
ParameterDefaultMinMaxTypeDescription
base_qty 1 1 10 integer Starting position size in lots
base_qty 1 1 10 integer Starting position size in lots
win_multiplier 2.0 1.5 4.0 decimal Multiply position by this after each win
win_multiplier 2.0 1.5 4.0 decimal Scale position by this factor after each win
max_scale 4 2 8 integer Maximum times to scale up position
max_scale 4 2 8 integer Maximum number of times to scale up
reset_on_loss 1 boolean Return to base qty after any loss
reset_on_loss 1 boolean Return to base size after any loss
Frequently Asked Questions
Anti-Martingale increases position size after wins and reduces it after losses — the opposite of the Martingale. This approach lets you ride winning streaks with larger size while protecting capital during losing streaks. It is mathematically sound and preferred by professional traders over the dangerous Martingale approach.
Start with 1 lot. After each winning trade, double to 2 lots. After another win, increase to 4 lots. After any loss, reset back to 1 lot. Set a maximum scale limit (e.g., 8 lots maximum) to prevent overexposure during extended winning streaks. This prevents euphoria from leading to excessive risk.
The main risk is that your largest position comes right after a winning streak — if that trade loses, you give back significant gains. To mitigate this, reset to base size after every loss and never scale above 4x your base. Also set a daily profit target — stop trading once you achieve 3-4% on your capital.
Fixed fractional (risking 1% per trade) is more consistent and mathematically optimal for most traders. Anti-Martingale is better for traders who have identified genuine skill edges and want to amplify winning streaks. For beginners, fixed fractional is safer. Anti-Martingale suits experienced NSE traders with proven track records.
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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.