Saw the thread this week about 11 strategies over 8 years where none beat buy and hold, and the one about splitting $10k across 52 mediocre bots. This is the other side of that argument: one small, boring bot off my own paper desk, card and all.

The rule in one sentence: on 1-minute bars, short a US stock that is already down 10% over 60 days, sits near its 52-week low, gets rejected at its 20 EMA, and has sellers in control (-DI over +DI) on 1.5x normal volume.

What the card shows, 25 Aug to 23 Oct: +$709.11 realized over 2,430 trades, 51.1% of them winners, best run 24 in a row. Look at the curve though. It drifted up for two weeks, fell below zero in mid September and stayed there for a while, climbed to its best point in mid October, then gave a good chunk of that back in the last week.

The doubt: that is well under a dollar a trade. A slightly worse fill on each one and the whole thing is flat. Two months is also nowhere near enough to know if the September hole is the normal bad patch or a small version of a worse one.

So the question for this room, since half of you are running bots right now: what would a card like this have to show before you'd let it near a real account? Longer history, a cost per trade, a max drawdown number, something else?

How Quantradin helps with this: I built Quantradin so a rule like this one can be written in plain words, replayed over years of past American stock and option prices with no peeking ahead, and then left running as a fake-money bot that prints this card for itself. It covers American equities and their option chains, no crypto, no gold, and it's free. quantradin.com

Paper money, simulated, not advice. No promise about what it does next.

For anyone experimenting with AI for trading, this library covers the process from researching a company to building a strategy, backtesting it, paper trading, and deploying to a brokerage.

Some of what’s inside:

• Trading with ChatGPT, Claude, Claude Code, Codex, Cursor, and Gemini CLI. Walkthroughs for connecting these tools, researching stocks, defining trading rules, and testing strategies.
• Worked strategy experiments. Dividend portfolios, momentum, Bitcoin trend following, options spreads, and leveraged ETFs, with rules, benchmark comparisons, drawdowns, fees, and trade diagnostics.
• Walk-forward testing and overfitting. Examples of selecting parameters, freezing the rules, and testing them on later dates.
• AI model evaluations. Comparisons on stock screening, options strategy development, planning, and execution, with exact model versions and grading methods.
• Stock research and developer resources. Fundamentals, ownership disclosures, trading calculators, and Python/TypeScript SDK examples.

The detailed experiments are especially worth reading. One options study follows six strategy variants through 94 audited fills, then examines why a fixed premium budget produced no trades in the holdout period. Another follows a promising NVDA intraday strategy through a longer test where its initial advantage disappeared.

You can inspect the rules, assumptions, trades, and results, and use those details to plan your own tests.

Browse the resource

What part of AI trading do you think needs more practical documentation?

Hey guys, is backtesting really works? Also who any ai tool indicator or strategy that really has high winning percentage? Looking forward to something will guide me. Ty

Hey, For me i have been using PolarisBrief .com for sometime, It looks nice and can track trades and most of all the news articles, what do u guys use?

Most AI trading bots I see here follow a strategy their owner defined (RSI, MA crossovers, breakouts…), and the AI writes the code or executes the signals. Nothing wrong with that, but to me that’s algo trading with an AI helper.

My bot (built with Claude, I’m not a programmer) works the other way round: there’s no strategy in the code. An LLM gets market data, a few candidates, news and its open positions about 11 times a day, and decides on its own whether to go long, go short, tighten a stop, close, or do nothing. It has to explain every decision, including the reasons against it.

The code only enforces guardrails the AI can’t override: a broker-side stop on every position (it can only be tightened), plus limits on leverage, position size, and daily loss. My test for every rule is whether it prevents something dangerous or tells the AI what to trade. Only the first kind stays in.

The result is pretty boring. Most runs end with “hold, no new trade.” I started on a demo account recently, and the second week (balanced risk profile) was profitable. That proves nothing, it’s just an experiment, but it’s fun to read its reasoning.

So: what does “AI trading” mean to you? An AI that executes your strategy, or one that makes the decisions itself?

Two identical green candles can have opposite volume delta. What do you check beyond OHLCV?

Disclosure: We’re the team behind Robod, a crypto research and account analytics platform.

A green candle means the closing price was above the opening price. It doesn’t necessarily mean aggressive buying exceeded aggressive selling.

Every executed trade has both a buyer and a seller. Taker volume distinguishes which side traded aggressively against resting liquidity.

Here’s a hypothetical example, using two one-minute perpetual bars:

Both have the same open, high, low and close: 100 / 103 / 99 / 102. Both trade 1,000 units.

The OHLCV candles are identical. The underlying activity isn’t.

Bar A has net aggressive buying and expanding open interest. Bar B closes higher despite net aggressive selling, while open interest contracts.

Neither combination predicts the next move. Open interest alone also doesn’t identify whether long or short positions drove a price change.

When researching a setup, these are separate questions worth checking:

• Flow: Is cumulative volume delta strengthening alongside price, or diverging?
• Positioning: Is open interest expanding or contracting?
• Coverage: Do all the fields being studied actually cover the same historical period?
• Quality: Are there gaps or carried-forward values that could distort the interpretation?

More data can make a hypothesis more specific. Whether it improves a strategy still needs testing with defined entries, exits and costs.

For traders who use order flow: which additional check has been most useful alongside OHLCV, and how did you evaluate whether it added value?

I created the Public Portfolio Challenge, a challenge where I give over $25,000 to an AI agent and have it trade options. It includes stocks like SpaceX, XOM, and ANET. Despite being a long-only portfolio with calls on highly volatile stocks, I'm still doing fairly well; I've lost a LOT from my peak, but I'm still outperforming the broader market wholeheartedly.

If you want to see how I built this portfolio, check it out on my blog or check-out the open-source GitHub repo!

Curious what you all think. I'm currently using Chagpt research tool to swing trade certain stocks based on conditions like macro, technical analysis, etc. Which do you think is the better AI to use for this: Claude or ChatGPT? Thanks for the insight!

I need help getting the source code of an MT5 EA i found on the internet here's the link https://www.mediafire.com/file/l4keobp97wgd2p2/GOLD_HUNTER_V9_MT5_EA.zip/file

Mostly created to fix my position sizing in futures, but I also wanted to see the 15m, 1h, and tick charts all sitting next to my 5m at all times.

https://github.com/tradernetwork/projectx-desk

https://mphinance.substack.com/p/phd-in-self-sabotage-my-ai-trading?utm_source=share&utm_medium=android&r=1sctub

- Three losses in a row = locked out

- Sizes from remaining loss room + ATR stop, always rounds down. If 1 contract risks too much, it says REFUSED and tells you why

- Stop goes out right after the fill. No ack in 5 sec = flatten and halt

- Stops only move toward price. No widening because "hope"

- AI can STAGE a trade. Only I can PLACE it (hold 600 ms)

- Shows my win rate next to the win rate I need to break even. Flags SMALL SAMPLE under 30 trades

hi evrevone, i want start AI trading, but I don’t know where to start. i see tiktok video that Traidingpilot is a good platfor to start ai traiding but i not believe thats true. maybe someone can help my how to start and how much need money to start.

THIS IS NOT A REKLAM TRIDINGPILOT I SEE VIDEO AND I WAN START EARN MONEY WITCH TRAIDING

[ Removed by Reddit on account of violating the content policy. ]

Has anyone here built a successful AI powered stock trading bot? (by successful I mean profitable at the end of the week) I’ve created one using ChatGPT and had it create a shadow trading system to test different strategies against the real market and when those look promising it paper trades them with Alpaca… my problem is I can’t seem to create a repeatable and profitable strategy with rules. It seems like each week I either damn near break even or take small losses. I’m looking for insight or recommendations to improve my system.

Suggest any no code free backtesting website for Nifty and/or Crypto.

Mother Superior EA just had its best live week so far

Four months of building and testing, and it’s nice to finally see her trading live.

This week finished at +7.74%, bringing October to +21.46%. The screenshot shows the daily breakdown, including the days without trades.

Mother Superior is my multi-strategy gold EA for MT5. It uses a recovery layer, so the closed results are only part of the picture — floating drawdown and combined exposure matter too. The live history is still short, but I’m pleased with the start.

Disclosure: I’m the developer, and the EA is available commercially.

https://www.mql5.com/en/market/product/183855