Quick context, I trade DAX40 mostly, indices and forex on liquidity sweeps, MSS and BPR. I built a journaling platform for myself last year because I was tired of Notion templates and bloated tools, ended up opening it up and now there's a small community using it.

The last few weeks I rolled out some AI features and honestly some of them changed how I review trades, so wanted to share what works and what doesn't from a trader perspective, not a product one.

What I added:

1. AI verdict per trade. After you close a trade and log it (entry, SL, TP, screenshots, notes), an AI gives you a verdict. Was it actually a valid setup, did you respect your rules, did you size correctly. Sounds gimmicky but when you log 30+ trades a month it catches the pattern of "I keep entering before candle close on Tuesdays" way faster than I would.

2. Coach profile. You define your own strategy (mine is liquidity + MSS + BPR + candle close confirmation), risk rules, instruments, and the coach updates as you log more trades. So the verdict isn't generic, it's against YOUR rules, not some ICT bro checklist.

3. AI chat with full access to your trades. This is the one I use the most. I can ask stuff like "show me all my losing DAX trades in London session where I entered before candle close" and it actually answers with the data. No more scrolling through 200 entries.

4. Analytics with confluence breakdowns. Best/worst confluences, profitability per setup combo, session performance, the usual but cross referenced. Found out my BPR + sweep combo is 68% win rate, BPR alone is 41%. Wouldn't have caught that manually.

Not a pitch, more curious if other people here use AI in their journaling or still old school spreadsheet. The verdict thing is what surprised me the most, brutal honesty when you ask for it.

Platform is TradingSFX if anyone wants to check, has a free tier. Happy to answer questions about how the AI is set up or how I trained the coach on my own methodology.

So i have been seeing alot of people using the powerful fable 5 to build a bot

Has anyone here managed to create anything worth with it?

I have access to it just want to known how to get started on it

Is it just me, or is it kind of funny seeing people post here that they spent 6 months building a trading bot that makes 30% a year?

The Nasdaq is up around 30% this year. 😅

Rule #1: if you've actually found a profitable strategy, managed to automate it, and it consistently outperforms the major indexes over the long run, the last thing you should be doing is posting about it all over Reddit or trying to sell the bot.

If you've really built a money-printing machine, why would you risk ruining your own edge?

At this point, I don't really see much difference between people selling trading courses and people promoting the "amazing" trading bots they built.

We've devised an algo with a straight 95.5% win-rate. Currently we won 17 days in a row & counting. These trades have all been made & tested on REALTIME trading days. These are the current results:

1 may 26 2026 - detect at 7537.74 bear call - close at 7519.47 - good credit - WIN
2 may 27 2026 - detect at 7506.82 bull put - close at 7521.29 - good credit - WIN
3 may 28 2026 - detect at 7565.70 bear call - close at 7563.43 - good credit - WIN
4 may 29 2026 - detect at 7601.33 bear call - close at 7581.25 - good credit - WIN
5 june 1 2026 - late detect past 3PM - no play
6 june 2 2026 - no play
7 june 3 2026 - detect at 7559.63 bull put - close at 7556.82 - good credit - LOSS
8 june 4 2026 - no play
9 june 5 2026 - detect at 7542.75 bear call - close at 7384.67 - good credit - WIN
10 june 8 2026 - detect at 7444.67 bear call - close at 7405.81 - good credit - WIN
11 june 9 2026 - detect at 7476.56 bear call - close at 7386.35 - good credit - WIN
12 june 10 2026 - detect at 7389.69 bear call - close at 7279.38 - good credit - WIN
13 june 11 2026 - no play
14 june 12 2026 - detect at 7453.69 bear call - close at 7430.86 - good credit - WIN
15 june 15 2026 - detect at 7568.41 bear call - close at 7555.26 - good credit - WIN
16 june 16 2026 - detect at 7569.32 bear call - close at 7511.22 - bad credit (10pt) - WIN
17 june 17 2026 - no play
18 june 18 2026 - detect at 7568.05 bull put - close at 7500.71 - good credit - WIN
19 june 22 2026 - detect at 7568.60 bull put - close at 7472.13 - good credit - WIN
20 june 23 2026 - detect at 7404.28 bear call - close at 7368.68 - good credit - WIN
21 june 24 2026 - detect at 7361.61 (7355/60) bull put - close at 7359.60 - good credit - half-WIN (40$ per contract loss)
22 june 25 2026 - detect at 7350.68 bull put - close at 7358.16 - good credit - WIN
23 june 26 2026 - detect at 7355.40 bear call - close at 7338.39 - good credit - WIN
24 june 29 2026 - detect at 7346.74 bull put - close at 7438.84 - good credit - WIN
25 june 30 2026 - detect at 7503.72 bear call - close at 7496.30 - good credit - WIN
26 july 1 2026 - detect at 7486.39 bear call - close at 7483.23 - good credit - WIN
27 july 2 2026 - detect at 7542.14 bear call - close at 7483.24 - good credit - WIN

total 27 days total / 21 wins / 1 loss / 22 total days detected

What would your guys' reaction be? I asked chatGPT a question "If after 17 out of 21 trading days doing 1, -2.1 credit spread per day, only 1 day was a loss, and 4 days were just no trades. If I start with 1 spread a day for the first month and then double every month. How much $ will I have by month 2, 4, 6, 12"

To which it replied:

https://preview.redd.it/9s2c7c52c2bh1.png?width=1682&format=png&auto=webp&s=194be10489d3f189e75e9ccfb0fdefdc6381066b

At first we were thinking about selling it, before we had enough realtime statistics to validate the algorithm, but after seeing it work as well as it is, in realtime ... we are going to keep it private to a very small select few individuals & only a profit-sharing model.

Over the last few months I built a few automations to remove the manual grind from my trading day. Sharing my morning routine in case it's useful, and I'd love to steal ideas from yours.

Before market open, my system auto-pulls:

• Overnight moves + gap scanners on my watchlist
• Key economic events for the day
• Yesterday's open positions + P&L carryforward
• Pre-set alerts on my key levels

On execution it logs entry/exit, size, and R automatically. At EOD it generates a one-page report — trades, P&L, mistakes tagged.

Two questions for the room:

1. What do you check every morning before the first trade that I might be missing?
2. What part of your day do you wish was automated but isn't?

EOD Report 02/07/2026 | Levels for 03/07/2026PCR 1.32 | VIX 12.29 (-7.21%)FII -311.82 Cr | DII +1,784.40 Cr

https://preview.redd.it/cro5ghipc7bh1.png?width=2168&format=png&auto=webp&s=53815928110e4115a7c73da766552d41fb710bb7

Daily summary

Six years of QQQ opening-range-breakout data, 112 raw trades, a filter waterfall, a loss autopsy, and a stress test aimed at the exact failure mode that gets backtests torn apart here. Posting the whole thing because I'd rather get this checked before real money touches it than after.

Setup: Solo build, systematic ORB on QQQ/NQ, no ML, deterministic rules only (regime gate, day-of-week filter, signal grade, opening range breakout). Going live on a funded futures account shortly, which is why I spent this weekend trying to break my own numbers before someone else did it for me.

The Sharpe was wrong

Original claim: 3.50 Sharpe. Sounded great. Turned out the annualization method was undocumented and effectively assumed daily trading frequency on a system that fires roughly 10 times a year. Recomputed properly:

• Per-trade Sharpe (mean_R / std_R): 0.49
• Correctly annualized for actual trade frequency: 1.54

3.50 was fiction. 1.54 is defensible. Retired the old number everywhere, including my own notes, and documented the methodology so it's reproducible.

The filter waterfall (112 raw trades → 59 filtered)

Stage	Trades	Win Rate	EV/trade	Sharpe	Max DD
Raw	112	48.2%	+0.888R	0.27	6.8R
+ Calendar guard (FOMC/NFP/CPI)	109	48.6%	+0.912R	0.27	6.8R
+ Friday blocked	80	53.8%	+1.246R	0.33	4.0R
+ Wed BULL blocked	70	58.6%	+1.479R	0.37	4.0R
+ Wed BEAR retained only	61	62.3%	+1.539R	0.38	3.0R
+ Signal grade filter (4-confirmation alignment)	59	57.6%	+0.987R	0.49	3.0R

Biggest single lever: the Friday filter alone accounts for ~38% of the total edge improvement from raw to final. Friday trades averaged -0.042R across 30 occurrences, essentially free money to remove. Everything else (day-of-week regime interaction, signal grading) matters, but nowhere near as much as just not trading on Fridays.

Loss autopsy—where does the edge actually die

Ran a structural post-mortem on all 59 filtered trades, winners and losers, looking for taxonomy rather than a magic filter (I know curve-fitting a "what-would-have-avoided-this-loss" rule off 25 losses is how people fool themselves, so I explicitly didn't do that, see below).

25 losses broke into three types:

• Target-miss reversals (13, 52%): reached ≥1R in favor, then reversed to a full stop
• Slow bleed (11, 44%): sideways chop, stopped late, no real signal
• Immediate reversal (1, 4%): stopped within 3 bars, the classic fakeout, essentially absent

The 52% figure was the interesting one. Half the losses weren't bad entries, they were good entries the market later took back.

The counterfactual that actually mattered

I'd already built a two-tier exit (bank 50% at +1R, trail the remainder) but never backtested it, it was execution-layer code, not signal logic. Ran it against the loss autopsy as a historical counterfactual:

Backtest (no engine)	With engine
13 target-miss losses	-13.0R
11 slow-bleed losses	-10.8R
34 winners	+82.0R
Total EV/trade	+0.987R

The mechanism is boring and mechanical, which is exactly why I trust it: locking half a position at +1R structurally can't be curve-fit to 13 specific historical trades, because it's a rule about R-multiples reached, not about any feature of those particular trades. It generalizes by construction.

Stress-testing against the thing that usually kills these posts

Saw enough "smooth equity curve = look-ahead bias" callouts on posts here to specifically check my own backtester for it. The risk: when a bar's high and low both contain the stop and target level, does the backtest assume favorable sequencing (target hit first) when live execution could easily have hit the stop first?

Audited all 93 grade-A trades (pre-final-filter set) for this exact condition:

• 79 trades (84.9%): unambiguous — stop and target far enough apart that same-bar sequencing isn't a question
• 14 trades (15.1%): ambiguous — same-day exit with price between stop and target

Worst-case stress test—force stop-first resolution on all 14 ambiguous trades:

• Original EV: +0.633R (this subset)
• Worst-case EV: +0.449R (-29%)
• After typical live degradation: +0.269R—still positive

It's not zero-impact, and I'm not pretending it is. But the edge survives an assumption that's actively hostile to it, which is a meaningfully different claim than "the backtest looks clean. " I've now wired live trade tracking to flag these same-bar-ambiguous trades going forward and compare real fills against this worst-case floor if, live underperforms +0.449R on this specific cohort, that's the signal something in the backtester's sequencing assumption was actually wrong, not just theoretically risky.

What I did NOT do (the trap I was trying to avoid)

Did not go hunting for a rule that would have "saved" the 25 losses. That's the classic move that always works and always means nothing, with enough features you can always draw a line around your own losses in hindsight. The asymmetry engine passed a higher bar: it existed before the autopsy, has a mechanical justification independent of these specific trades, and its cost side (what it gives up on winners) was measured with equal rigor. Anything that only showed up as "add this filter, get 15 more percentage points" got treated as a red flag, not a discovery.

Where it stands

• 59-trade filtered configuration, 57.6% win rate, +1.266R EV with the exit engine active
• Per-trade Sharpe 0.49, correctly annualized ~1.54
• Max drawdown 3.0R across the full filtered sample
• Live drift monitor now tracks rolling EV against this backtest floor, with explicit drift alerts at 10 and 20 trades, and separately tracks the 14 ambiguous-sequence trades against their own worst-case floor

Going live on a funded account shortly. Wanted this checked here first rather than finding out about a hole from a blown drawdown limit.

Genuinely interested in where this is still wrong. What would you attack first, the calendar guard's negligible impact (only removed 2 trades, is that suspicious in itself?), the grade-filter methodology, or something in the intrabar sequencing check I haven't thought of?

I built a screener for indian stock market with Fable 5 and the backtest results honestly shocked me:

→ 71% win rate

→ +6.9% avg win

→ sl fired on only 11% of trades

→ 226 trades on 5 years of NIFTY 500

Inspired by another post here, I wanted to share my experience so far.

Been building an algorithmic crypto trading bot with AI for a few months now, also some real money, and I want to share the honest version, because this sub looks like it leans toward 'AI finds the edge' and my experience was pretty much the opposite.

I tested a lot of entry signals - models to predict next-move direction, pattern classifiers, the works. Almost every one looked great in-sample. Some so much, that I thought my dream of getting rich quick would actually work out ;) But then they died the moment I ran proper validation (walk-forward, a multiple-testing correction, checking it on assets it wasn't tuned on). Single-asset price prediction in crypto is close to noise, and AI/ML is extremely good at fitting noise and calling it signal. The prettier the backtest, the more suspicious I got.

The things that actually survived testing were rarely anything based on classic indicators. I rather found structural edges any spreadsheet could describe: a market-neutral funding-carry trade (collect the funding that perps pay), and a hedged volatility-selling strategy. They get paid whether the market goes up or down.

Where AI helped me a lot and will for all time coming is the engineering around it. It wrote and reviewed the plumbing far faster than I would have had. Atomic entry+stop orders, a stop-loss that re-asserts every tick, position reconciliation, logging. The stuff that actually keeps a real-money bot from blowing up. I feel this part is not being talked about enough. It really is the boring part, but here AI is a giant multiplier for productivity.

To a smaller degree AI is also helpful in generating ideas, especially for topics that are new to you.

AI is for me an essential part of building my trading bot. But Im not using it to actually predict price. And maybe its just me, but I have a hard time trusting its predictions after my own backtests.

Curious where people here have actually gotten AI to add value - prediction, execution, research, or the plumbing? And has anyone gotten an ML price signal to survive real out-of-sample?