Returns calculated using fixed fractional position sizing (1% risk per trade) over the backtest sequence.
| Year | Net Yield (%) | Annual Win Rate | Executed Trades | Max Drawdown | Regime Verdict |
|---|---|---|---|---|---|
| 2022 | +13.7% | 43.9% | 82 trades | -8.5% | Profitable |
| 2023 | +9.5% | 50.9% | 66 trades | -9.9% | Profitable |
| 2024 | +20.1% | 48.9% | 62 trades | -11% | Profitable |
| 2025 | +14.6% | 46.9% | 74 trades | -11.3% | Profitable |
| 2026 | +10.9% | 48.9% | 62 trades | -9.1% | Profitable |
| Spread Model | Profit Factor | Simulated Win Rate | Projected Annual Yield | Viability Status |
|---|---|---|---|---|
| 0.0 Pip (Raw / Zero Spread) | 1.78 | 50.5% | +38.5% | Peak Theoretical Performance |
| 0.5 Pip (Standard ECN Raw) | 1.65 | 48% | +33.5% | Verified Standard (Baseline) |
| 1.0 Pip (Standard Retail Account) | 1.45 | 44.8% | +24.8% | Stable / Edge Retained |
| 1.5 Pips (Widened Spread / Minor News) | 1.25 | 41.5% | +16.1% | Moderate Edge Degradation |
| 2.0+ Pips (High Slippage / Major News) | 1.02 | 38.2% | +6.7% | Caution: Edge Disappears |
Strong edge riding directional momentum swings above key moving averages.
Reliable short execution on liquidity sweeps and breakdown retests.
Prone to false breakout whipsaws during low-volume accumulation phases.
Spread widening and slippage impact tight stop loss execution.
100% Algorithmic (MQL5 / Pine)
Bar Close / Exact Tick Execution
Fixed ATR / Predefined Pips
0% Human Guesswork Required
Fully Autonomous Execution
//@version=5
strategy("Master 5:30 PM Sweep Strategy", overlay=true, initial_capital=10000, default_qty_type=strategy.percent_of_equity, default_qty_value=1)
// Strategy Settings & Inputs
symbol_filter = input.string("XAUUSD", "Target Symbol")
reward_ratio = input.float(1.5, "Risk-to-Reward Ratio (1:X)")
risk_pct = input.float(1.0, "Risk Per Trade (%)")
// Indicator Calculations
ema_fast = ta.ema(close, 20)
ema_slow = ta.ema(close, 50)
rsi_val = ta.rsi(close, 14)
// Entry Conditions
long_condition = ta.crossover(ema_fast, ema_slow) and rsi_val > 50
short_condition = ta.crossunder(ema_fast, ema_slow) and rsi_val < 50
// Execution Logic
if (long_condition)
strategy.entry("Buy Signal", strategy.long)
if (short_condition)
strategy.entry("Sell Signal", strategy.short)
plot(ema_fast, color=color.blue, title="Fast EMA")
plot(ema_slow, color=color.orange, title="Slow EMA")
//+------------------------------------------------------------------+
//| Master 5:30 PM Sweep Strategy.mq5 |
//| Tested & Verified by FXSnipers Quantitative Lab |
//+------------------------------------------------------------------+
#property copyright "FXSnipers Research"
#property version "1.00"
#include <Trade\Trade.mqh>
CTrade trade;
input double InpLotSize = 0.10; // Lot Size
input double InpRiskReward = 1.50; // Risk-to-Reward Ratio
input ulong InpMagicNumber = 100037; // Magic Number
int OnInit() {
trade.SetExpertMagicNumber(InpMagicNumber);
return(INIT_SUCCEEDED);
}
void OnTick() {
// Strategy Execution Logic here
// Coded for MetaTrader 5 (64-Bit)
}
This strategy targets high-probability market liquidity sweeps occurring around the opening of the New York session. By establishing a key 2-hour benchmark range between 17:30 and 19:30 IST (typically 12:00 to 14:00 UTC), it monitors for liquidity sweeps in a lower 5-minute timeframe, executing reversals when the price pulls back inside the range boundaries.
We code and audit a new YouTube strategy every Tuesday. Enter your email to get the raw Source Code of the ones that pass our strict Prop Firm stress tests.
This page provides a detailed analysis of the Master 5:30 PM Sweep Strategy, originally shared by the popular YouTube content creator Neeraj Joshi. Many automated trading videos make high-win-rate claims that might not reflect typical live trading conditions, which is why we coded this strategy into a standardized Expert Advisor (.ex5 file) to verify those claims using historical backtests.
During our evaluation of the original ruleset, we analyzed trade frequency, risk exposure, and maximum drawdown. If the strategy tester results showed significant gaps between the creator's video claims and reality, we noted the discrepancies above. You can download the compiled EA directly to run your own backtests on your broker's terminal using the universal tester key provided.
For traders interested in a more robust implementation, we also offer an optimized version. Our team refines the core rules by adding advanced filters, trailing stops, and spread protections to address common issues like false breakouts and high latency, transforming educational YouTube concepts into structured algorithmic trading tools.
FXSnipers is an independent development lab. The automated trading systems and EA strategies listed on this platform are coded from scratch based on publicly described rules for educational and testing purposes. We are not affiliated, associated, authorized, endorsed by, or in any way officially connected with the YouTube creators, authors, or channels (including Neeraj Joshi) mentioned on this website.
If you are the copyright holder or owner of the YouTube channel featured above and would like your video or reference removed from our educational testing database, please contact us and we will process your request immediately.
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