2 posts tagged with "algo-trading"

Estimated reading time: 30 minutes

Introduction​

Transaction cost analysis is the systematic measurement of the gap between the price a strategy intended and the price a broker delivered. It does not measure whether a strategy has an edge. That question is answered by backtesting and walk-forward validation. TCA answers a different question. Once a signal is generated and sent for execution, what do execution costs actually look like, and how large are they relative to the capital committed?

This question is almost never answered with real data. Traders build strategies, optimize them, validate them, and deploy them live. The execution layer is treated as a constant. Spreads are modelled with assumptions. Slippage is either ignored or set to a round number. The result is a systematic blind spot in the strategy development process. It is also a reliable source of the gap between backtest performance and live results.

This report fills that blind spot with direct measurement. Across nine days of live trading from April 1 to April 9, 2026, an EMA 2/5 crossover strategy on BTCUSD M1 generated 2,723 trade signals through a major CFD broker accessed via MetaTrader 5. Every fill was recorded. Every signal delivery timestamp was logged. The analysis covers signal delivery latency, slippage at entry and exit, temporal execution patterns, and the cost of slippage expressed as a percentage of margin committed per trade.

The strategy itself is not the subject of this report. An EMA 2/5 crossover on M1 is a high-frequency configuration that generates a large trade volume. It was chosen for that reason. The strategy is deliberately unprofitable over the analysis period. The signal quality is not the point. The point is the 1,336 completed fills it generated, which provide a statistically robust foundation for measuring execution quality. For strategy validation methodology, see the preceding article in this series.

The central finding is not complicated. At high leverage on a major CFD instrument, a nominal spread-driven slippage of 12 price units per entry translates to approximately 5.1% of broker margin per fill. That number is not directly readable from a broker's fee schedule or account documentation. An estimate is possible, but it requires knowing your effective leverage per trade, your typical spread, and your exact lot sizing after broker rounding constraints are applied. All three inputs carry uncertainty in practice. Direct measurement from live fills resolves that uncertainty. This report shows how to measure it, what the measurement reveals, and why it matters for any live deployment on a leveraged instrument.

The report is organized as follows. Chapter 1 defines slippage precisely and describes the measurement setup. Chapter 2 explains the signal delivery architecture. Chapter 3 examines signal delivery stability as a system property. Chapter 4 analyzes the slippage distribution and identifies what it actually represents. Chapter 5 examines slippage over time and across the trading week. Chapter 6 presents two independence findings. Chapter 7 identifies temporal execution patterns. Chapter 8 quantifies execution cost as a function of leverage and margin. The conclusion connects these findings to position sizing and live deployment.

Estimated reading time: 35 minutes

Introduction​

Walk-forward optimization is the most rigorous method available for testing whether a trading strategy has a real, repeatable edge, or whether it merely memorized a particular stretch of historical data. Standard backtesting has a fundamental flaw, when you optimize parameters on a dataset and then measure performance on the same dataset, you are selecting the best-fitting parameters after seeing all outcomes. Given enough combinations, some will appear excellent purely by chance. Walk-forward optimization breaks this cycle by testing each set of optimized parameters on data the optimizer has never seen.

The result is not a prediction. It is a probabilistic model of a non-stationary dynamic system, with empirical evidence for its time of validity and observable signals for when the model is no longer valid. Systematic trading does not offer certainty. It offers calibrated probabilities, honest risk boundaries, and a framework for making deployment decisions based on evidence rather than hope.

This report applies that framework to a concrete case study, a long-only EMA crossover strategy on BTCUSD M30, developed step by step from an initial backtest through parameter optimization, quality filtering, and walk-forward validation across 5 and 11 rolling windows. The central finding is that the strategy demonstrates a statistically significant edge in the current BTC market regime, remains profitable during the declining price phase of 2025 to 2026 that was entirely unseen during optimization, and reveals a clear regime boundary at late 2023 that separates two structurally different periods of market behavior.

The report is organized as follows. Chapter 1 establishes the baseline backtest and its limitations. Chapter 2 describes the parameter optimization process and the overfitting problem it exposes. Chapter 3 adds a quality filter and confirms a robust parameter region. Chapter 4 presents the primary walk-forward validation across 5 windows. Chapter 5 extends the analysis to 11 windows and identifies the regime boundary. Chapter 6 establishes a Monte Carlo risk framework for live deployment. The conclusion synthesizes all findings into a deployment hypothesis and a framework for ongoing strategy management.