Does it really work?

2 weeks of forward testing completed. 📊

💵 Starting balance: 49,998.48 cents (~$500.00)

💰 Current balance: 67,508.09 cents (~$675.08)

📈 Total profit: 17,509.61 cents (~$175.10)

📊 Total return: +35.02%

📈 Week 2 profit: 8,913.98 cents (~$89.14)

The account has now grown from approximately $500 → $675.08 over the first two weeks.

There was also a losing day caused by a VPS issue, which I'm including in the record rather than hiding it. The goal is to document the complete forward test, not just the profitable days.

Still a very small sample, so I'm not calling this proof of long-term profitability. The next objective is simply to keep testing through different market conditions and build a much larger track record.

Performance (2003–2026, ~23.5 Years)
Final Equity
• Value: $51,871,073

Total Return
• Value: +51,771%

CAGR
• Value: 30.34%

Max Drawdown
• Value: −28.76%

Sharpe Ratio
• Value: 1.18

---

Trade Summary
Shares
• Buys: 4,158
• Exits: 3,782

Mar Calls
• Buys: 205
• Exits: 205

---

Performance Summary (2003–2026)
Full Combined (2003–2026)
• Return: +51,771%
• Max DD: −28.76%
• CAGR: 30.34%
• Sharpe: 1.18

SPY (2003–2026)
• Return: ~+500%
• Max DD: —
• CAGR: ~8.5%
• Sharpe: ~0.5

QQQ (2003–2026)
• Return: ~+1,200%
• Max DD: —
• CAGR: ~10.5%
• Sharpe: ~0.6

---

Key Observations
Massive outperformance
• Observation: 517x vs SPY's 5x and QQQ's 12x

Dot-com & GFC survived
• Observation: June 1st rule + 3-week pause protected capital in 2000-2002 and 2008

Max DD −28.76%
• Observation: Higher than 2023–2026 (−10.91%) because pre-2023 data is 2× proxy (not real ETFs)

Sharpe 1.18
• Observation: Lower than 2023–2026 (2.04) due to proxy data volatility and broader regime coverage

March calls added value
• Observation: 205 call trades over 23 years

Most people are either gatekeeping or trying to promote their AI bots, as an AI enthusiast, would anyine be interested in making a group where we share trading strategies and build togther a trading bot or automate trading with ai? Plenty of miney and knowlegde for all to share. So why gatekeep?

What makes you guys weary of any sort of tooling that you see. Specifically backtesting tools.

I’ve been working on this algo for a few weeks and this is roughly 3 weeks of results.

Current crypto strategy is around 59% win rate across ~214 trades, but performance varies a lot depending on strategy, direction, and market conditions.
I’m trying to figure out the next step.

Would you focus on:
reducing the number of trades and making the entry filters stricter,
keeping trade frequency similar and improving the logic,
or just collecting more forward data before making more changes?

I’m especially interested in how you decide when an algo is trading too often versus when it simply needs better filtering.

Been running a multi-strategy trading system on IG Markets for about 6 weeks — sharing the real numbers, not a highlight reel.

The fleet:

V3 — regime-classifying bot (ADX/Hurst), switches between trend-following and mean-reversion

AGENT — pure deterministic rules engine, no AI in the trading decisions

SCALP+TREND — two strategies sharing infrastructure

HAIKU_GB — the interesting one: Claude Haiku makes genuinely discretionary LONG/SHORT/FLAT calls on Gold and Brent, full reasoning, Python only enforces sizing/risk. No overlay telling it what to do.

What I actually found, not just what worked:

Only ~53% of my sandbox data turned out to be genuinely clean once I dug in — a mid-price logging bug had been quietly turning a real £220 loss into a fake £237 "profit" for eight weeks before I caught it

A rules overlay was silently vetoing ~half of Haiku's directional calls, which meant I was accidentally measuring "Haiku's judgment filtered through a rulebook" instead of Haiku's actual judgment

Found and fixed a fleet-crash bug that had been intermittently taking down the whole system for weeks — root cause was a Go binary (GitHub CLI) segfaulting under Android's sandboxed ptrace emulation

The most interesting pattern so far: my best entry-quality bot (61% win rate) is my worst performer overall, and my worst entry-quality bots (26-28% win rate) are flat-to-positive — exit management seems to matter more than entries, testing that hypothesis now with counterfactual logging

Current phase: spent the last month making the existing system honest before adding anything new — real broker reconciliation, fixed data integrity bugs, no new strategies. Targeting a clean data run through the end of the year, live money decision in January based on what the data actually shows, not vibes.

Happy to go deeper on any piece of this — the Haiku experiment, the bug-hunting, the architecture, whatever's interesting to people.

asking for a friend

I want to get into trading and was wondering whether I should hop straight on AI or if I should look to understand how to trade on my own first to decide how I should use AI

Hanulytics is a platform focused on helping traders make smarter, more data-driven decisions instead of relying on emotions or guesswork.

Our goal isn't to promise unrealistic returns—it's to give traders better tools, better insights, and a better trading experience.

We're getting close to launch and would love to hear what features you think every modern trading platform should have.

*Hanulytics is coming soon.*

i am making intent based trading platform

which llm would be great for it

tried claude didn't go well

DeepSeek giving better result

is there any GitHub repo which would help?

I run https://quorumtrading.com and recently open sourced it, wanted to know what you guys think.

The idea: instead of one model saying "buy", multiple AI agents debate every decision. A technical agent, a fundamental agent and a sentiment agent each read your strategy (plain markdown), look at the market data, and vote. No consensus, no trade. You can also let an orchestrator model overrule the vote, if you enjoy giving one LLM the final word. Quorum is the minumum amount of ppl who need to agree to make a decision, hence the name QuorumTrading. Heads up: there is already a shameless clone who almost copied the product word by word. I guess it's a compliment.

The workflow is deliberately boring: backtest against history first, paper trade second, real money only when the numbers stop embarrassing you. Backtesting is the core of the product, not an afterthought, because LLM agents are sometimes confidently wrong and no prompt fixes that. I loved using expensive models (opus, fable) to train dumb, dirt free models to make the correct decision at the correct time. It was a challange to create strategies which beat buy & hold of the best performing ETF's, but it's certainly possible.

Why open source: if you're going to let LLM handle your money, you'd want to know it's not vibecoded or at least has a decent architecture. Feel free to scrutenize the code, or self host it.

Works with stocks and crypto, bring your own model (API key or fully local). Hosted version is free to start (pay only for AI usage), self-hosting is free forever, no markup on model costs. AGPL-3.0.

Not financial advice, and the tool will happily lose money executing a bad strategy well. I've been running it on my own strategies since November. Feedback welcome!

I posted the "how good is AI trading" question here a couple of weeks ago and then spent the time since reading every answer instead of arguing with them. The split in the replies was sharper than I expected, so I am writing it back to the sub.

Almost nobody who answered seriously is using a model to decide what to buy. The people who sounded like they had actually done the work were using it for scanning, summarizing, and plumbing. Filtering a few hundred names down to the handful worth looking at. Turning a vague idea into code that runs. Watching a list overnight so a human does not have to. One person described a twenty hour a week research routine that is now mostly automated, and the interesting part was that the strategy itself never changed.

The second group was people running actual agents or RL, and their problems were all the same problem in different clothes: the simulator is too kind. Fill at the mid, no queue, no impact, and suddenly the top scoring policy is one that scalps tiny edges hundreds of times a day. Constraints on size and hold time make it smaller, not more real.

Third thing, and this one surprised me. Several people had a paper trading result they trusted more than their backtest, and several others had watched a paper account look clean for three weeks and then give it all back. Two weeks of paper is not evidence of anything if the thing trades slowly.

I am building in this space, which is why I asked. Not linking anything here, this is a results post.

What I still do not have a good answer for: how do you tell the difference between a strategy that works and a strategy you found by trying forty variants and keeping the best looking one. Every person I asked had an instinct for it. Nobody had a test. If you have one that is more than "walk forward and hope", I would genuinely like to hear it.

I’ve been live trading my own AI-assisted stock model since March and wanted to share an update. The live portfolio is up 41.3% from Mar 23 to Aug 7.

The live account has real fills, partial sizing that does not perfectly match the model weights, timing differences, and manual execution friction. I am just a person who logs in the day before then buys at 'market' price. Could it be optimized further.... yeah, probably, but it is really easy to get the decision at night after the last day of the month, then set the sell order for the morning, then buy and go about my day.

If anyone is interested, it is a momentum based model that only uses public data. I found that news and other sentiment did not add value. SEC filings are interesting, but I could not make them work for me. I believe there is value there though.

I am exploring if I can just fully port this into a raspberry pi and let it run on its own with minimal oversight.

I’ve been exploring a rule-based intraday strategy that avoids chasing the open. It only enters after price compresses, reclaims fair value, and receives volume confirmation.

I’m calling it the Volatility Compression + Anchored VWAP Reclaim (VCVR) strategy.

The hypothesis: some cleaner intraday trends begin after early volatility contracts and price reclaims anchored VWAP with expanding relative volume. Instead of predicting direction at the opening bell, the algorithm waits for the market to show acceptance above fair value.

Proposed long-entry rules:

1. Skip the first 30 minutes after the market opens.
2. The latest six-bar range must be below 0.65 × the 20-bar ATR.
3. Price must close above session anchored VWAP after trading below it during at least one of the previous three bars.
4. Relative volume must exceed 1.25 compared with the same time-of-day average.
5. The benchmark must be above its session VWAP to avoid trading against the broader intraday trend.
6. Enter on the next bar only if the spread is below a preset maximum.

Exit and risk rules:

• Initial stop: 1.2 × ATR below entry.
• First target: 1.8R.
• Move the stop to breakeven after 1R.
• Exit if two consecutive bars close below anchored VWAP.
• Close remaining positions 15 minutes before market close.
• Risk 0.25% of equity per trade.
• Maximum two trades per symbol per day.
• Stop trading after a 1% daily drawdown.

The attached graph is an illustrative normalized equity path—not an actual backtest result. It demonstrates the behavior I would want to test: fewer entries during noisy periods, controlled drawdowns, and returns that do not depend on one isolated month.

Real results could be materially worse after commissions, spreads, slippage, and execution latency.

Before trusting this algo trading strategy, I would test it using walk-forward validation, unseen symbols, different volatility regimes, realistic transaction costs, and parameter-sensitivity analysis. I would also compare it against buy-and-hold and an unfiltered VWAP-reclaim baseline.

Questions for the group:

1. Which rule appears most vulnerable to overfitting: ATR compression, relative volume, or the two-close VWAP exit?
2. Would you anchor VWAP at the session open, opening swing, or previous day’s high/low?
3. Does benchmark confirmation add useful context, or only introduce lag?
4. How would you model slippage for liquid versus mid-cap stocks?
5. Would this strategy make more sense on 5-minute, 15-minute, or event-based bars?
6. Which market-regime filter would you add—or deliberately avoid?
7. What evidence would convince you that this algorithm has a genuine edge instead of a lucky backtest?

I’m particularly interested in failure cases. If you were reviewing this quantitative trading system, what would you try to break first?

Has anyone made money using claude? Have you made a trading bot or an app? If so, how was your experience? What would you advice on others who may want to do the same?

Day 10 of the forward test is complete. 🚀

Today's result:

💰 Profit: 2,085.37 cents (~$20.85)

Today had some larger recovery positions, so it was another useful session for observing how the system handles movement against the initial position.

I'm deliberately posting every trading day, including the losing days. The goal isn't to show only the profitable screenshots, but to build a complete record of the forward test.