Multi Stochastic
This EA uses multiple Stochastic oscillators across different timeframes to generate trading signals. It enters trades when multiple Stochastic indicators align in the same direction across timeframes, confirming overbought or oversold conditions. The strategy aims to filter out false signals by requiring confluence from several timeframe perspectives.
Open the interactive view (equity curve, symbol switch)Key facts
Backtest results (H1, about 1 year)
| Symbol | P&L | Annual | Max DD | Trades | Win rate | Sharpe | Recovery | Losing streak | Per trade | Status |
|---|---|---|---|---|---|---|---|---|---|---|
| EURUSD | +3.69% | +3.20% | 2.36% | 128 | 57.00% | 0.90 | 1.56 | 4 | +$2.88 | Reliable |
| GBPUSD | +5.96% | +5.16% | 4.77% | 127 | 53.20% | 1.00 | 1.25 | 6 | +$4.92 | Reliable |
| USDJPY | -2.11% | -1.83% | 7.69% | 105 | 49.00% | -0.36 | -0.27 | 6 | −$2.09 | Reliable |
| XAUUSD | -44.77% | -38.76% | 85.65% | 113 | 50.40% | 0.78 | -0.52 | 5 | −$39.62 | Reliable |
Profitable on 2 of 4 markets with reliable results.
Converted from MQL5 to Python and run with the same rules for every EA. Positions use MT5 lot sizes (100,000 units per FX lot, 100 oz per gold lot) with 1:100 leverage, one order at a time, and a stop-out below 10% of the starting balance. Sharpe is computed from the equity curve. Results marked as unreliable are shown for completeness only.
Against the other Multi-Timeframe EAs with a reliable result on the same market: EURUSD: better than 73% of 11 · GBPUSD: better than 83% of 12 · USDJPY: better than 8% of 12 · XAUUSD: better than 17% of 12
Month by month
| Market | Months with a gain | Best month | Worst month |
|---|---|---|---|
| EURUSD | 69% | +1.67% 2026-02 | -1.42% 2026-03 |
| GBPUSD | 54% | +4.55% 2026-04 | -3.00% 2026-03 |
| USDJPY | 38% | +2.11% 2026-09 | -2.86% 2026-07 |
| XAUUSD | 46% | +210.73% 2026-02 | -69.07% 2026-01 |
From the backtest equity curve, sampled about every two days; only reliable results.
Blow-up risk: No blow-up pattern found
- Has no stop loss or trailing stop
On its own this is not a typical blow-up pattern, and the account was not stopped out in our backtests. That is not a guarantee: any EA can lose money.
High: stopped out in our backtest, or martingale/grid sizing without a stop loss. Elevated: martingale or grid sizing with a stop loss, or several positions at once without a stop loss. What the label means and what we found →
Strategy analysis
The core strategy concept of the Multi Stochastic EA is based on confluence confirmation across multiple timeframe Stochastic oscillators. Entry conditions require Stochastic indicators from multiple timeframes to simultaneously reside in overbought territory (above a threshold) for sell signals, or in oversold territory (below a threshold) for buy signals, creating a multi-timeframe resonance effect. Exit conditions typically involve closing positions when the Stochastic returns from overbought/oversold zones to neutral territory, or when predefined take-profit and stop-loss levels are reached. Position sizing may employ fixed lot sizes or dynamic lot calculations based on account balance. Money management is partially addressed by the multi-timeframe confluence filter itself, which reduces the number of low-quality trade signals; however, without strict stop-loss enforcement or position size controls, the strategy could face significant drawdowns during extreme market conditions. The strategy's strength lies in reducing false signals through multi-timeframe confirmation, but the downside is that excessive filtering may result in too few trading opportunities, and Stochastic indicators can produce repeated crossover signals in ranging markets.
Strengths
- Multi-timeframe confluence confirmation effectively reduces false signals and improves entry quality
- Stochastic indicators are intuitive and highly adjustable, suitable for optimization across different market conditions
- Clear strategy logic with multi-timeframe filtering provides more justified trading decisions
Weaknesses
- Multiple filtering conditions may result in too few trading opportunities, potentially missing profits during trending markets
- Stochastic indicators are prone to repeated false crossover signals in ranging markets, which multi-timeframe filtering cannot entirely eliminate
- Lacks volatility and spread filters, which may lead to unfavorable entries during low-liquidity or high-spread periods
Possible improvements
- Add spread and volatility filters (e.g., ATR) to avoid entries during high-spread or extreme volatility periods
- Implement a trailing stop mechanism to lock in floating profits and improve money management
Risk assessment
Overall risk: Medium
- Multi-timeframe confluence filters signals but without new bar checks, the same signal may trigger repeated entries
- Fixed lot sizing lacks dynamic position management based on account risk
Evaluation scores
Parameters
| Parameter | Default | Description |
|---|---|---|
Stoch_Timeframe1 | M15 | Timeframe for first Stochastic indicator |
Stoch_Timeframe2 | H1 | Timeframe for second Stochastic indicator |
Stoch_Timeframe3 | H4 | Timeframe for third Stochastic indicator |
Stoch_K_Period | 14 | Stochastic K period |
Stoch_D_Period | 3 | Stochastic %D line smoothing period |
Stoch_Slowing | 3 | Stochastic deceleration factor |
Overbought_Level | 80 | Overbought threshold level |
Oversold_Level | 20 | Oversold threshold level |
LotSize | 0.1 | Fixed lot size (0 = dynamic calculation) |
StopLoss | 50 | Stop loss in points |
TakeProfit | 100 | Overall take profit target (points) |
Similar Multi-Timeframe EAs
Backtests are historical simulations and do not guarantee future results. Not financial advice.