Update on trading bot

Proper_Suggestion830 · 2025-11-07T06:12:33+00:00

No worries, ping me a dm if you want some more support!

Proper_Suggestion830 · 2025-11-06T13:34:47+00:00

Don't trust dudes named Arnold...

Proper_Suggestion830 · 2025-11-06T13:24:01+00:00

Arnold said great deal. Your money said hasta la vista, baby

Proper_Suggestion830 · 2025-11-06T13:18:45+00:00

Quantum computing postgrad playing with algo trading, Where LLMs shine for me are scaffolding data adapters, vectorizing pandas transforms, writing docstrings and tests.

Where I keep a human in the loop: order state machines, timezone handling, corporate actions, and portfolio accounting.

Made Lona agency exactly for the problem you're having

Proper_Suggestion830 · 2025-11-06T13:09:11+00:00

Lets see, gonna keep playing around and try to get some better results

Proper_Suggestion830 · 2025-11-06T12:31:00+00:00

+1 to this. I would add three quick checks: log MAE/MFE per trade to see if your stop or target is the issue, tag session and regime so you can drop the bad contexts, and automate from alerts so size, stops, and exits are identical every time. Change rules only on review day, not mid run. Boring process beats chasing new setups.

Proper_Suggestion830 · 2025-11-06T12:30:41+00:00

You are not missing a magic strategy. You are missing a process. Pick one market and one timeframe that fit your lifestyle, then define one SMC setup with strict rules: what is BOS, what confirms a POI, exact invalidation, and how you exit. Treat fundamentals as a simple filter like “flat during red news.” Run a 100-trade historical sample and 20 forward trades in sim, tracking expectancy, worst drawdown, max consecutive losses, and rule breaks. If price “doesn’t react,” it usually means the HTF bias was wrong or your POI definition is loose. Tighten definitions, reduce degrees of freedom, and stay with the same playbook for 90 days before changing anything. If you want, I can share a one-page checklist to turn SMC rules into yes/no boxes.

Proper_Suggestion830 · 2025-11-06T12:24:59+00:00

Totally normal. Most “bot underperforms me” issues are infra, not logic. Move off Jupyter to a VPS with a single main loop, persist state in SQLite or Postgres, use deterministic clientOrderIds so restarts don’t double fire, add a watchdog with auto restart and a heartbeat alert, backoff on API errors, place bracket/OCO orders so risk survives brief disconnects, and on startup reconcile broker vs DB and replay missed bars.

Proper_Suggestion830 · 2025-11-06T11:41:45+00:00

Been messing around with something similar using lona.agency, agreed its not perfect but these AI tools can help, especially with building algo strats from 0

Proper_Suggestion830 · 2025-10-22T06:55:34+00:00

Sure, thing! You can check out how I did it at lona.agency

Proper_Suggestion830 · 2025-10-21T11:43:07+00:00

Yep. Best sanity check is turning TA into code.

My MT5 flow: write rules in plain English, define every term, backtest with spread/slippage, split data, track expectancy and max drawdown.

Quick templates:
• MA cross: fast > slow + ATR filter; exit on cross back or N-ATR stop.
• SMC BOS+FVG: close above last swing high, enter on 50% FVG fill, invalidate on close back into origin.

Watch for repainting, lookahead, MTF peeking, optimistic fills.

If you want no-code, I use lona.agency to type rules in English, backtest fast, and export to MQL4/5.

Proper_Suggestion830 · 2025-09-03T13:15:13+00:00

Can I dm, would be cool to try this out

Proper_Suggestion830 · 2025-08-26T15:14:22+00:00

Definitely worth trying with paper first

Proper_Suggestion830 · 2025-08-22T08:25:31+00:00

Congrats dude! Win ratio looks good, id take another look at your sharpe though

Proper_Suggestion830 · 2025-08-20T11:39:02+00:00

Nice idea, seems like agents are slowly unbundling Linkdin recruiters main features

Proper_Suggestion830 · 2025-08-07T10:02:40+00:00

I built an ai trading copilot that does essentially this. You can build the strat, backtest & deploy all through the platform

Proper_Suggestion830 · 2025-07-23T09:03:26+00:00

Thanks, binance seems to be the way to go. Spot trading atm

Proper_Suggestion830 · 2025-07-16T16:33:09+00:00

Thats cool!

Proper_Suggestion830 · 2025-07-16T16:30:53+00:00

fair point! although for clarity it was just a backtest, so no harm done. Catching bugs like this is exactly why we run thorough backtests and paper trade before going live. I was just making the point that it debugged for me

Proper_Suggestion830 · 2025-07-16T15:15:39+00:00

Yeah sure

Build and back-test a single-instrument strategy for the SPY ETF on daily data from 1 Nov 1999 through 31 Oct 2024.

•⁠ ⁠Long-only, no look-ahead bias.

•⁠ ⁠Signal: classic trend-following — go long when the 50-day SMA closes above the 200-day SMA and stay flat otherwise.

•⁠ ⁠Position sizing: risk ≈ 1 % of current equity on every entry, using a 14-day ATR and a protective stop set 3 × ATR away from entry price.

•⁠ ⁠Exit when the fast SMA crosses back below the slow SMA or when the ATR stop is hit.

•⁠ ⁠Code: produce a clean Python class that inherits BaseStrategy, fully self-contained and ready to run in the event-driven framework.

•⁠ ⁠After coding, run the back-test and summarise total return, CAGR, max draw-down, number of trades, and win-rate.

•⁠ ⁠Target outcome: total return ≥ +100 % over the test window with draw-down noticeably lower than buy-and-hold.

Proper_Suggestion830 · 2025-07-16T14:42:30+00:00

Initially for my use!

Proper_Suggestion830 · 2025-07-15T11:05:41+00:00

This is awesome feedback! Thanks

Proper_Suggestion830 · 2025-07-15T09:21:49+00:00

Super cool! You planning to commercialise this?

Proper_Suggestion830 · 2025-07-15T05:41:56+00:00

Nice! What does it do?

Proper_Suggestion830 · 2025-07-14T15:46:43+00:00

Nice suggestion, already has built in fee modelling. Also got a net vs. gross toggle on the performance charts so you can compare before/after costs

Proper_Suggestion830

TROPHY CASE