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# Data collection basics
We do not expect all traders to jump into the deep end immediately, but even collecting data in a simple format, such as (3R +3R −1R +3R −1R −1R)=6R, over more than 150 trades and using it to calculate the maximum peak-to-trough and win rate represents a good starting point and exceeds what most retail traders undertake.
A lot of traders backtest and then wonder why their “profitable” strategies fall apart in live trading. If you apply these three principles, forward tests, and live deployment should improve dramatically.
# Here are some key actions to consider.
**1. Timeframe separation before combination**
Timeframe mixing e.g., 5m and 15m. If both timeframes are used for trading entries with the same setup, you should isolate their results before combining them to measure the true effectiveness of each and prevent overlap. If you choose to run both strategies, do not prioritise one setup, as uneven priorities will make it random whether the 5m or 15m setup is executed in real time, which is noisy rather than logical (sometimes the 5m trade will randomly cancel out the 15m trade, and vice versa). Run them both without cross-timeframe interference, or use a single timeframe (e.g., only 15m).
**2. Longs versus Shorts Analysis (directional separation)**
Why the strategy performs exceptionally on the long side versus the short side, or vice versa, must be logically justified; otherwise, this analysis falls into overfitting territory. Occasionally, a strategy will only work well for longing and vice versa and mechanical reasons can exist for it.
In terms of longs vs shorts, either side should only be isolated in real time if there is a logic-based reason that can justify the idea that is not drawn from the data itself. For example, a long-only S&P 500 swing approach can be justified by the positive drift caused by Quantitative Easing and consistent investment market buy flow, which creates this undercurrent.
On the short side, the strategy's formation depends on a fast follow-through to target, which is common in "bearish" price action (more volatile) and produces superior results. Research regarding the nature of modern market volatility supports this observation. Strategies whose logic aligns well with this can benefit.
**3. Discarding inefficient strategies**
For 15m or lower, if I do not get an expectancy / E.V reading beyond 0.2, I stop testing, as I would believe the strategy is underfitted or something else has gone wrong. If a small, logic-based optimisation that complements the system's logic cannot save it, I test on a few other markets that may be compatible. If that does not work, I toss the strategy away.
**Key: Having an E.V of 0.2 is equivalent to having a 60% winrate with a 1:1 RRR.**
It shows that a minor edge exists which I require before moving on to the optimisation stage.
If you try to save something that does not work well already, you are bound to overfit; avoid falling into that trap. It is an almost guaranteed path to failure.
**Make sure your ideas are predefined.** This is important for strategy integrity, as it helps ensure your results are not overly influenced by look-ahead bias or data snooping. Try to avoid tweaking the strategy as you go along.
**The effect:**
When you align yourself with these principles, the quantity of profitable backtests you produce will drop, but the quality will surge.
**Edit: Old version available here (updated text today):**
r/Trading/comments/1q2ip2p

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