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Hello everyone, I'm at a frustrating crossroads in my quant journey and could use some seasoned perspective.
My Background: ~5 years of discretionary FX trading with mixed results. For the last 3 months, I've been fully committed to building a robust, automated strategy to overcome discretionary pitfalls.
The Strategy & The Battle:
My core idea is anEMA ribbon trend-following strategy on EURUSD 1H, entering on pullbacks to the ribbon. To improve signal quality, I've layered on filters for ribbon slope, width (ATR-based), and a regime filter built from a multi-algo ML model (predicting Trending/Consolidation/Breakout for the next 12hours).
The battle is in validation. My process:
1. Train regime model on one period (2022-2023).
2. Use a later period for strategy IS ( 2024 , where I have generated the regime predictions purerly OOS), running massive parameter sweeps (30k-100k combos).
3. I avoid cherry-picking by taking the median parameters from the top 10-20% of performers.
4. Then, I get cucked in OOS (2025 split into two segments ). The equity curve falls apart.
My Core Dilemma:
I believe my issue isstatistical significance and regime capture. Optimizing on one year (2024) just finds a parameter set that fits that year's specific sequence of regimes, which doesn't hold in 2025.
I'm considering two paths and would love your critique:
1. The "Static Edge" Path: Significantly expand my IS to capture more cycles. For example:
· Train regime model on 2019-2022.
· Optimize strategy on 2023-2024 (using the frozen model's predictions).
· Do a true, final OOS test on the completely unseen 2025.
· Question: Is a 2-year IS (2023-2024) enough, or am I still likely overfitting to that period's peculiarities?
2. The "Adaptive Process" Path: Do a more classic Walk-Forward Analysis (WFA). The logic:
· Permanently freeze the regime model trained on, 2020-2022
· Perform rolling optimizations (e.g., 3-month IS → 1-month OOS) from 2023 onward.
· The result is the aggregated equity curve of all the OOS periods.
· Question: My regime signals predict up to 12 hours ahead. Is short-period WFA the only valid test for such a system, or does it become noise chasing?
Am I missing a third option? Is my entire approach of layering filters onto an EMA ribbon fundamentally flawed for finding a scalable edge? Should I scrap this and go back to the drawing board with a simpler, single-idea hypothesis?
Any feedback on the validation structure, the strategy premise, or sheer motivational perspective is deeply appreciated. This grind is humbling.
PS this whole thing looks like AI wrote it because it did (most of it). I use deepseek to be my notes taker and kind of like a journal and thus he did write out the thing in a better way than I could ever do it.