1. Introduction

The Magic 100 strategy is a trading strategy published on the Forex Factory forum thred#989445. This strategy features extremely simple rules. It only uses the EMA 100 indicator to identify trend direction and relies on marked swing high / swing low of each candle to determine entry signals.

In this article, I quantified the strategy logic from the original thread into a machine-executable Expert Advisor (EA) and ran historical backtest on it.

Every backtest uses identical Exness market data and execution settings. I tested five common assets: EURUSD, GBPUSD, USDJPY, USTEC and XAUUSD.

2. Original Strategy Rules

Core Indicators

  • EMA 100: Used for trend identification. Only take long orders when price moves above EMA 100; only take short orders when price moves below EMA 100.

Basic Trading Rules

  • This strategy is designed for the H1 timeframe or higher timeframes.

Entry Rules

Long entry:

  1. Price moves above EMA 100
  2. Price pulls back and closes as a bearish candle; place a pending buy order above the high of this bearish candle

Short entry:

  1. Price moves below EMA 100
  2. Price pulls back and closes as a bullish candle; place a pending sell order below the low of this bullish candle

Stop Loss & Take Profit Setup

  1. For long orders, set SL below the low of the bearish candle; target TP is 2R
  2. For short orders, set SL above the high of the bullish candle; target TP is 2R

Examples

Long Example

Short Example

Ambiguities in the Original Strategy Rules

The original strategy does not specify whether existing unfilled pending orders should be canceled once a new candle closes.

My Quantified Execution Rules

To maintain consistent trade execution during strategy quantification, I defined the following rules:

  1. After a new candle forms, cancel all prior pending orders that have not been triggered
  2. Shift pending entry and SL prices an extra 2 pips above swing highs or below swing lows to filter false breakout signals

3. Standard Backtest Configuration

Every historical test runs on raw Exness market data. Spread, slippage and tick execution stay identical across all assets so we can fairly compare performance.

  • Timeframe: H1, H4, D1
  • Test window: Aug 1, 2021 — Aug 1, 2026
  • Starting account balance: $10,000
  • Leverage: 1:100
  • trade size: Fixed risk of 100 USD per trade
  • Execution mode: Every tick simulation + minor random server delay + real Exness spread and slippage values
  • Assets tested: EURUSD, GBPUSD, USDJPY, USTEC, XAUUSD

4. Full Backtest Performance Results

4.1 H1 Performance Summary

H1 Equity Curve

Asset Total Trades Win Rate Profit Factor Max Drawdown Net Result
EURUSD 322 24.22% 0.61 100.02% -$10001.84
GBPUSD 324 24.69% 0.62 99.15% -$9913.57
USDJPY 1579 32.30% 0.91 99.11% -$9898.29
XAUUSD 391 26.60% 0.67 100.31% -$10021.71
USTEC 1137 31.66% 0.88 100.13% -$10013.98

4.2 H4 Performance Summary

H4 Equity Curve

Asset Total Trades Win Rate Profit Factor Max Drawdown Net Result
EURUSD 694 29.11% 0.80 99.30% -$9874.51
GBPUSD 747 29.99% 0.82 99.27% -$9921.87
USDJPY 1149 32.03% 0.90 92.18% -$8515.98
XAUUSD 1187 35.47% 1.07 51.14% $5763.11
USTEC 1257 35.00% 1.03 41.65% $2912.21

4.3 D1 Performance Summary

D1 Equity Curve

Asset Total Trades Win Rate Profit Factor Max Drawdown Net Result
EURUSD 269 29.74% 0.82 43.99% -$3377.23
GBPUSD 231 32.03% 0.93 21.14% -$1116.11
USDJPY 215 38.60% 1.22 12.56% $2940.40
XAUUSD 251 43.03% 1.41 17.65% $6147.99
USTEC 263 34.60% 0.97 24.16% -$627.89

4.4 Analysis

EURUSD and GBPUSD generate net losses across all three timeframes. While loss magnitude and maximum drawdown show mild improvements as timeframe rises, profit factor stays consistently below 1 with no positive mathematical expectancy. This confirms the strategy is unsuitable for these two assets.

USDJPY records net losses on H1 and H4, yet delivers positive mathematical expectancy on the D1 timeframe, with a steadily oscillating upward equity curve and no severe deep drawdowns.

XAUUSD on D1 is the optimal configuration of this full strategy suite; it is the only asset that balances consistent positive returns, low maximum drawdown and stable positive mathematical expectancy.

USTEC displays inverse timeframe performance characteristics: H4 acts as its best performing timeframe, while D1 yields net losses. Traders need to exercise caution when deploying this strategy on USTEC.

The original author claims this strategy works best on H1, yet our validation shows continuous equity curve declines for all assets on the H1 timeframe. The core design of this strategy targets clean, clear pullback reversals. Lower timeframes contain excessive market noise that frequently triggers false entry signals. Across most assets in this backtest, profit factor, win rate and maximum drawdown metrics gradually improve as timeframe increases. However, low-volatility major pairs such as EURUSD and GBPUSD feature mild price movement. Even when running on D1, price range fails to generate enough price differential to offset accumulated trading losses.

5. Strategy Strengths & Limitations

✅ What Works Well

  • Simple rule set, beginner-friendly for new forex traders
  • On XAUUSD D1, it generates consistent profits over 5 years with low maximum drawdown

❌ Weak Spots

  • Win rate consistently sits below 50%; all profitability relies on high reward-to-risk ratios, consecutive losing trades are unavoidable
  • Returns on profitable assets heavily depend on trending market conditions; prolonged range-bound market phases trigger periodic account drawdowns
  • Poor performance on low-volatility major currency pairs regardless of timeframe adjustment

6. Conclusion

This strategy carries strong timeframe sensitivity; overall performance improves as timeframe expands, yet each asset has its own optimal timeframe: XAUUSD and USDJPY perform best on D1, while USTEC performs best on H4. Additionally, the strategy is not compatible with all trading assets. Low-volatility major currency pairs show zero alignment with the strategy logic, and acceptable performance only appears on high-volatility instruments.

For future optimization, traders can add range-bound market filters to only open trades during strong one-sided trending moves. This adjustment can further lift win rates and reduce loss magnitude. Traders may also set higher reward-to-risk ratios to boost overall equity curve growth. A consecutive loss lockout rule can be implemented to pause new trade entries during drawdown streaks and prevent severe account drawdowns.

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Disclaimer: This EA is only for strategy backtest & educational purposes, strictly prohibited for live trading.

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Disclaimer: Not financial advice. Forex trading involves substantial risk. All results shown are historical backtest outputs and do not guarantee future performance.