MQL5 Developer Needed: Volume-Spike Reversal EA with Confluence Filters

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Below is a detailed breakdown of how the “volume-spike reversal” Expert Advisor (EA) operates based on the demonstration, along with an outline for how a developer might implement it. It is organized into sections covering the robot’s logic, inputs, confluences, trade management, and important considerations.


1. OVERVIEW OF THE ROBOT

  1. Core Principle

    • The EA detects massive one-off volume spikes in tick data that signal large institutional orders (often from governments, central banks, etc.).
    • These large orders come in suddenly and can quickly reverse an ongoing trend.
  2. High Accuracy Reversal Detection

    • The robot seeks to consistently catch turning points with a nearly 100% accuracy rate in backtests.
    • It pairs volume spikes with other confluence factors (e.g., support/resistance, trendlines, channel lines).
    • Because entries can be extremely precise, it often trades with very tight stop-losses (or exits upon the next reversal signal in some cases).
  3. Continuous (Always-in-the-Market) Trades

    • The EA immediately reverses position when a new spike-based reversal is detected.
    • One trade’s exit is effectively the next trade’s entry—so the robot flips between buy and sell signals continuously.
  4. Small Account, High Return Potential

    • The demonstration showed how even a 0.1-lot size on a $100 account could yield significant returns over a short period, given ideal conditions.
    • Real-world caution: This is highly broker- and feed-dependent, but it highlights the potential for a high R:R ratio when executed properly.
  5. Time-of-Day & Broker Data

    • The EA relies on a high-speed broker feed with reliable, consistent volume data.
    • Large orders (and thus volume spikes) often appear during high-liquidity sessions or news events.
    • Part of the approach involves filtering trades to only be taken when large institutional orders are more likely.

2. LOGIC FOR DETECTING REVERSALS

2.1 Volume Spike Detection

  • The EA monitors tick-by-tick volume changes (rather than standard candlestick volume alone).
  • When a sudden, large single-tick volume jump is detected (significantly larger than average), the EA flags a potential reversal signal.
  • Implementation Detail:
    • Maintain a rolling average or threshold of typical tick volume.
    • Compare each new tick’s volume to the threshold; if it exceeds by a multiple (e.g., 2×, 3×, 5×), trigger an alert.

2.2 Price Spike / Candlestick Pattern Confirmation

  • Large institutional orders often create a spike candle (or effectively two opposing candles back-to-back).
  • The EA checks for sudden intra-candle (tick-level) movements in the opposite direction of the current trend.
  • Implementation Detail:
    • Observe if, within the last few ticks, price travels a sizable distance in the opposite direction.
    • Merge or analyze short-term tick data to detect the formation of a large wick.

2.3 Confluence with Other Technical Factors

  • The EA looks for additional reversal cues that line up with the volume spike:
    1. Support/Resistance Zones (horizontal or diagonal).
    2. Trendline Bounces or Channel Boundaries.
    3. Pivot Points (optional).
  • Implementation Detail:
    • The EA can auto-calculate or store known S/R levels.
    • If the sudden spike occurs near a support or resistance level, it adds confidence to the signal.

2.4 Time-of-Day Filter (Optional)

  • Large-volume institutional moves often cluster around major session opens or scheduled news releases.
  • Implementation Detail:
    • Allow the EA to only trade within specified hours (e.g., London open, New York overlap).

3. TRADE ENTRY LOGIC

  1. Identify Current Trend

    • The EA needs to know whether the market is in an uptrend or downtrend, often via moving averages or by reference to the last open trade.
  2. Volume/Price Spike Trigger

    • Once the EA detects a volume spike “contradicting” the current trend, it checks for confluences.
    • If confluences are met, it prepares to reverse position.
  3. Enter Trade

    • Close any open position that’s in the opposite direction.
    • Open a new position in the direction indicated by the big order flow (the volume spike).
  4. Stop-Loss Placement

    • Some demonstrations show a tight, fixed or ATR-based stop-loss just beyond the spike candle’s wick.
    • Alternatively, the strategy can exit only on the next reversal signal, relying less on a traditional SL.
    • Implementation Detail:
      • Option A: Hard-coded small SL placed just beyond the spike’s high/low.
      • Option B: No SL, exit on next reversal signal.
      • Option C: ATR-based SL (e.g., 0.5× ATR(14)).
  5. Take-Profit or Exit Mechanism

    • The primary mechanism is the next volume-spike reversal signal.
    • Alternatively, a developer can add a trailing stop or partial profit logic for safety.

4. TRADE MANAGEMENT & POSITION SIZING

  1. Money Management

    • The demonstration suggests very high gains with small capital, but real-world usage requires responsible risk parameters (e.g., 1–2% risk per trade).
  2. Continuous / Always-in-the-Market

    • The EA flips from buy to sell with each new reversal signal, maintaining near-constant market exposure.
  3. Drawdown Management

    • Given the high success rate in backtests, drawdowns can be minimal.
    • In live conditions, consider adding a global max drawdown fail-safe (e.g., halting trading if account drops more than 10%).

5. ADDITIONAL IMPLEMENTATION CONSIDERATIONS

  1. Broker Selection

    • Reliable tick volume data is crucial. If the broker’s data feed is inconsistent, the EA may not perform as intended.
    • Execution speed and low latency are important to catch sudden moves.
  2. Latency & Execution

    • Since entries depend on single ticks, minimal slippage and fast execution are needed.
    • Hosting on a VPS close to the broker’s server is recommended.
  3. Spread Widening / News Events

    • Large spikes often coincide with news. Spread can widen, possibly causing slippage on entries.
    • The EA might filter out trades if the spread exceeds a specified threshold.
  4. Data Handling

    • Maintain a buffer of the last X tick volumes to compute an average or median volume in real time.
    • Account for edge cases (connection drops, zero-volume ticks, etc.).
  5. User Inputs & Parameters

    • Volume Spike Threshold (e.g., 2× or 3× average volume).
    • Confluence Methods (enabling/disabling S/R lines, pivot lines, trendlines, etc.).
    • Time-of-Day Filter (start hour, end hour).
    • Stop-Loss Method (none, fixed, ATR-based, or exit-on-next-reversal).
    • Position Sizing (lot size or % of account risk).
    • Broker-Data Filters (max allowed spread, min tick volume, etc.).
  6. Backtesting & Visualization

    • True tick-by-tick backtesting with high-quality data is time-consuming but necessary to replicate real volume behavior.
    • Summaries are often more practical than watching each tick in a full year’s simulation.

6. STEP-BY-STEP OUTLINE FOR A DEVELOPER

A. INPUT PARAMETERS

  1. Volume Spike Threshold
    • double volumeSpikeMultiplier (e.g., 2.0, 3.0, 5.0)
  2. Allowed Trading Hours
    • int startHour, int endHour
  3. Stop-Loss Method
    • enum StopLossType { NONE, FIXED, ATR, NEXT_REVERSAL }
    • double fixedStopPips (if using FIXED)
    • int atrPeriod, double atrMultiplier (if using ATR)
  4. Position Sizing
    • double lotSize (fixed) or double riskPerTrade (e.g., 1–2%)
  5. Broker Execution Filter
    • double maxAllowedSpread
  6. Confluence Options
    • Toggles for S/R detection, trendlines, pivot points, etc.

B. INITIALIZATION (On EA Start)

  1. Load/Calculate S/R and Trendlines (if auto-detection is used).
  2. Initialize Volume Arrays
    • Keep track of recent tick volumes to compute the rolling average.

C. ON TICK EVENT (CORE LOGIC)

  1. Check Time Filter

    • If outside the specified trading hours, do nothing.
  2. Collect Tick Data

    • Gather current price and current volume.
    • Update rolling average volume.
  3. Check Volume Spike

    • If currentVolume >= volumeSpikeMultiplier * averageVolume , flag a potential reversal.
  4. Check Price Action Reversal

    • Confirm whether there is a rapid move in the opposite direction compared to the recent short-term trend.
  5. Check Confluence

    • See if price is near any S/R or trendline area that aligns with the spike.
  6. Determine Final Signal

    • If conditions are valid, confirm the reversal signal in the opposite direction of the prior trend.
  7. Trade Execution

    • Close any open position that conflicts with the new direction.
    • Open a new position in the spike’s direction.
    • Set stop-loss if applicable (fixed, ATR-based, or none if using next reversal exit).

D. TRADE MANAGEMENT (ONGOING)

  1. Check Next Reversal for Exit

    • If StopLossType == NEXT_REVERSAL , exit occurs only upon the next signal.
  2. Trailing Stop / Partial Exit (Optional)

    • If desired, allow trailing stops or partial closes.
  3. Risk Management

    • Optionally, reduce or pause trading if floating losses exceed a certain threshold.

E. LOGGING & DEBUGGING

  1. Log Every Potential Spike

    • Record timestamp, tick volume, and price when a spike is detected.
  2. Log Rejection Reasons

    • If a spike fails confluence checks, log why it was skipped.
  3. Performance Metrics

    • Track consecutive winners/losers, net profit, etc.

7. RECAP OF KEY POINTS

  • Main Edge: Rapid detection of high-volume reversals using tick data.
  • Confluence: Key for filtering out false signals (S/R, trendlines).
  • Stop-Loss: Often very tight or replaced by a “flip on next reversal” approach.
  • Data Quality: Requires reliable tick data; standard M1 or M5 volume is insufficient.
  • Always in the Market: Flips between buy and sell upon each new reversal trigger.

8. NEXT STEPS

  1. Implement the Tick Logic

    • Focus on precise detection of volume spikes and quick triggers.
  2. Add Confluence Checks

    • Ensure robust S/R or trendline routines if automated.
  3. Test Thoroughly on Demo

    • Compare performance across multiple broker data feeds.
  4. Optimize

    • Adjust thresholds for volume, confluence, stop methods, etc., to match real-world data conditions.

Final Note

This EA concept illustrates what can be done by combining tick-level volume spike detection with tight confluence checks. Developers should maintain realistic expectations about real-world latency, broker differences, and potential slippage, but the framework above provides a road map for replicating the strategy’s core logic:

  • Detect sudden institutional-sized volume surges.
  • Confirm with support/resistance or trendlines.
  • Enter quickly with tight stops or a continuous reversal approach.
  • Keep position sizing and risk management at the forefront.

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