u/Ecstatic-Ad-7510 ·
Reddit — r/algotrading
· July 02, 2026 at 12:39
· ⬆ 15 pts
· 💬 14 comments
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AI Summary
Summary
Author describes running a 15-min BTC quant strategy with $100k scale, using shadow bots for parameter optimization.
After 4 months, the strategy is not consistently profitable, prompting doubt and a question about when to abandon a strategy.
Quality assessment: This is a practitioner's reflection on live testing, not a researched thesis or speculative call. It's experiential noise with limited actionable data.
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I’ve been running a custom quant strategy on BTC contracts (15-min timeframe) for about 4 months now in live/paper mode with real balances (\~$100k scale testing). Core uses a combo of technicals, hybrid momentum and mean reversion with volatility filters.
To iterate without blowing up the main account, I have 4 shadow bots running variants and each one has 1-2 tweaked parameters (such as indicator periods, thresholds, weighting factors). I’m collecting detailed notes on performance, useless params, and regime behavior using those.
The good news seems to be decent data volume, some shadows outperforming in specific conditions, learning a ton about live execution (slippage, data quality, etc.).
The struggle is that after 4 months, it’s not consistently profitable. Win rates, profit factor, and drawdowns are okay but not “set and forget.” Im feeling the doubt creep in…
I guess my main question is how long do you typically run forward/live testing before deciding to drop or majorly overhaul a strategy? Do you full scrap it or let it run while you build something new?
I’m relatively new to this scene so any advice would be greatly appreciated.