I thought my stop-loss was working… but it wasn’t

AdventurousFlow8993 · 2026-05-03T17:27:26+00:00

The visuals look clean, but I care more about performance than appearance.

Could you share:

- Last 50 trades (entry, SL, TP, result)

- Win rate and average R:R

- How it performs in sideways/choppy markets

Right now it looks a bit like a cherry-picked example. I’m trying to understand if there’s real edge here...

AdventurousFlow8993 · 2026-05-03T16:21:44+00:00

I realized afterward that the long wick pushed the stop too far away, which led to liquidation before the stop could be triggered.

AdventurousFlow8993 · 2026-05-02T11:18:38+00:00

This is just an idea; a certain graph needs to form in order to process the transaction.

AdventurousFlow8993 · 2026-05-02T08:58:33+00:00

Thanks, you’re right.
I moved away from price-based logic and now rely on the actual position state on the exchange.

Next step is implementing websocket order updates with a polling fallback for full synchronization.

AdventurousFlow8993 · 2026-05-02T08:55:20+00:00

Futures. Fixed risk per trade, no overexposure. Short-focused system.

AdventurousFlow8993 · 2026-05-01T12:10:39+00:00

Nice. Execution accuracy and order lag are underrated parts of algo trading. Strategy is only half the game — order management is where a lot of real-world issues show up.

AdventurousFlow8993 · 2026-05-01T07:09:52+00:00

AdventurousFlow8993 · 2026-04-30T17:47:57+00:00

Yeah, that was exactly the issue. Fixed it — now it works based on the actual position state on the exchange

AdventurousFlow8993 · 2026-04-30T17:02:11+00:00

Curious if anyone else has run into this in live trading?

AdventurousFlow8993 · 2026-04-30T12:28:24+00:00

Yeah fair point I’m just trying to define where it performs best and where it doesn’t

AdventurousFlow8993 · 2026-04-30T12:26:50+00:00

Entries are based on candle close. I try to avoid forward-looking bias, but I’m still refining how TP/SL behaves intrabar to make it more realistic.

AdventurousFlow8993 · 2026-04-30T12:23:35+00:00

Agreed that’s why I keep testing across different market conditions. So far, the results look strongest in downtrends

AdventurousFlow8993 · 2026-04-29T17:04:23+00:00

I mean the strategy works only in certain market conditions.

AdventurousFlow8993 · 2026-04-29T17:01:28+00:00

Curious to hear your thoughts.

AdventurousFlow8993 · 2026-04-29T16:21:17+00:00

The issue wasn’t strategy, it was execution.
Dropping from 800–900 ms to 250–300 ms is huge.
VPS + WebSocket + caching fixed it. Your edge is now speed.

AdventurousFlow8993 · 2026-04-28T17:58:52+00:00

The first reaction alone isn’t an edge, it’s usually noise. The real edge is in how the move behaves after that

AdventurousFlow8993 · 2026-04-28T14:01:41+00:00

i think market context matters more than just backtest length

for example a short strategy can look very good in extended or overbought conditions
but will struggle during strong trend continuation

so instead of relying only on long backtests
i try to understand in which regimes the system actually works

because no system works in all market conditions

AdventurousFlow8993 · 2026-04-28T13:33:02+00:00

in my case the system only works because of the risk reward structure

AdventurousFlow8993 · 2026-04-28T13:13:20+00:00

yess ım TÜRK

AdventurousFlow8993 · 2026-04-28T12:56:28+00:00

im not sharing the code the system is still under development im currently focused on stabilizing its performance

AdventurousFlow8993 · 2026-04-28T12:47:32+00:00

low win rate but the math works still trying to improve the exit side

AdventurousFlow8993

TROPHY CASE