2023-2025
Backtest vs Live

Backtest ↔ Live — Behavioral Convergence

What is “Convergence”?

In the algorithmic strategy space, the biggest divergence usually occurs when a strategy goes from historical simulation (backtest) to real-world implementation (live).

This page presents what we call convergence: That is, whether the behavior of the strategy remains consistent when the environment changes.

Convergence concerns both matching profits or timing of trades, as well as maintaining a similar risk structure, growth rate, and logical recovery (drawdown & recovery), which is a key indication that the strategy is not built exclusively for historical data.

Backtest Equity Curve

ForexBot Backtest equity curve
Historical simulation (backtest): the "theoretical" nature of the curve (rate, drawdowns, recovery).

Live Equity Curve

ForexBot Live equity curve
Live implementation (live): affected by spread/slippage, but we are interested in whether the risk behavior remains consistent.

Why convergence matters

Convergence reduces one of the main risks in algorithmic strategies: the risk that historical performance is a product of overfitting.

When a strategy exhibits similar behavior in both historical testing and live implementation, the likelihood that its logic is robust and not adapted to specific past conditions increases.

Funded — Backtest Snapshot

Funded Instatrader Backtest snapshot
Backtest snapshot in a funded environment: behavioral baseline.

Funded — Live Snapshot

Funded Instatrader Live snapshot
Live snapshot in a funded environment: the risk structure should be similar, even if the performance differs.

Limitations & Evaluation Honesty

The existence of convergence is not a guarantee of future performance and does not negate investment risk.

However, the absence of convergence would be a clear indication of inconsistency. For this reason, convergence is used here as a tool for assessing the quality and maturity of the strategy, not as a promise of results.

⚠️ ForexBot.gr is an infrastructure for evaluating and controlling the behavior of the automated system, with an emphasis on consistency, transparency and long-term verifiability.