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🚨HOW I BACKTEST TRADING STRATEGIES USING CLAUDE CODE > Most traders backtest the wrong way > They fit a strategy to historical data > See +300% and think they found the holy grail > Go live - blow up > That's curve fitting, the classic trap > The right...

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zostaff

17,391 subscribers

63,046 次观看 • 6 个月前 •via X (Twitter)

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28 条评论

ramb0 的头像
ramb06 个月前

HOW I BACKTEST TRADING STRATEGIES USING CLAUDE CODE- A: Have Claude write your C# and put it to NinjaTrader to backtest. Don't trust Claude's backtests in python

HedgeAlgo 🇺🇸 的头像
HedgeAlgo 🇺🇸6 个月前

Bro, this is algo dev 101. Even with forward testing, you don’t have enough data. If timeframe chart you are using is long, e.g. daily or 4 hour bars, you are NOT going to have enough test samples. Good luck collecting thousands of test samples.

Gipp 🦅 的头像
Gipp 🦅6 个月前

backtest is a very important thing

CryptoWatchtower 的头像
CryptoWatchtower6 个月前

You mention training the model, what model? Do you have an LLM that you’re feeding the data to? If so, do you have a link to that video?

HaasOnline 的头像
HaasOnline6 个月前

Exactly. The real test is forward-testing on data your model never saw. Perfect backtest results are basically a red flag at this point.

Alpha Bravo 的头像
Alpha Bravo6 个月前

No disrespect here, but building a true working algo that generates consistent PNL takes much more data, back testing, and effort. Being said, this could be the first stepping stone for someone who wants to get started.

Hussain Hashim | Building SundayBack 的头像
Hussain Hashim | Building SundayBack6 个月前

@zostaff yeah, learned this the hard way. thought I was a genius till reality hit. back to square one 😂

wladwtf 的头像
wladwtf6 个月前

walk-forward analysis is key, reduces overfitting. what metrics do u use to evaluate strategy performance during the walk-forward test?

Insomnia 的头像
Insomnia6 个月前

it's new part of video?

cristal💎 的头像
cristal💎6 个月前

backtested results are usually just a hallucination

wast3 的头像
wast36 个月前

excellent method, worth testing in practice

cvxv666 的头像
cvxv6666 个月前

full checklist for backtests - love it

shmidt 的头像
shmidt6 个月前

walk forward testing separates the pros from the gambling addicts who think they cracked the code with 6 months of data

zostaff 的头像
zostaff6 个月前

no gambling ,only predict

Valentin 的头像
Valentin6 个月前

bookmarked this alpha content 🤝

monokern 的头像
monokern6 个月前

What model did you use? Opus?

zostaff 的头像
zostaff6 个月前

yes,opus 4.6

sopersone 的头像
sopersone6 个月前

bookmarked this content

zostaff 的头像
zostaff6 个月前

the best content

leopardracer 的头像
leopardracer6 个月前

need it to watch

Willow the Trader 的头像
Willow the Trader6 个月前

Walk-forward is the real filter. I've been running systematic futures strategies for years and the number of "holy grail" backtests that fall apart on out-of-sample data is humbling. Setting up the entire train/test/slide pipeline in one Claude Code conversation — that used to take me days to configure properly.

srikant 的头像
srikant6 个月前

These things work initially, fade over time.

slash1s 的头像
slash1s6 个月前

backteset fr very important

toni 的头像
toni6 个月前

Backtest results look clean until you factor in slippage, spreads, and actual execution latency. Real question - are you testing against the same market regime we're in now, or a fundamentally different one?

アライさん 的头像
アライさん6 个月前

基本中の基本な

CryptoRichUser 的头像
CryptoRichUser6 个月前

@grok give us the prompt to give to Claude

vorty 的头像
vorty6 个月前

I do the same thing It really does help

Gorynich☄️ 的头像
Gorynich☄️6 个月前

Bro you made the real work

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this github repo should honestly be ILLEGAL for retail traders to have for free hedge funds pay quant PhDs six figures to find alphas, backtest them and stress-test every trade - this repo turns the whole quant desk into one sentence you type in a terminal it's called Vibe-Trading. built by a University of Hong Kong lab, 36.4k stars and it hit #1 repo of the day first understand how it works: > you type one sentence in plain English > a team of AI agents takes it: quant, crypto, macro, risk > they pull the data, write the strategy code and run the backtest > then they try to break it: Monte Carlo, Bootstrap, Walk-Forward > it tests 478 famous quant alphas in one line and tells you which are still ALIVE and which are DEAD > what survives exports straight to TradingView Pine Script or MetaTrader 5 > it's a full quant desk. it just runs on your laptop here is how to run it yourself for FREE: 1. open your terminal and install it: pip install vibe-trading-ai 2. run vibe-trading init and pick Claude Code as the brain. it runs on your Claude Code login, no separate API key 3. type one sentence: vibe-trading run -p "Backtest a BTC-USDT 20/50 moving-average strategy for 2024 and summarize return and drawdown" 4. watch the agents pull the data, write the code and run the backtest live 5. bench the alpha zoo in one line: vibe-trading alpha bench --zoo gtja191 --universe csi300 --period 2018-2025 --top 20 6. call a whole crypto desk: vibe-trading --swarm-run crypto_trading_desk '{"asset": "ETH-USDT", "timeframe": "1w"}' funding, liquidations and flow all go to a risk manager before anything moves 7. walk-forward it. if the edge only works on one window, it's not an edge 8. export what survives to TradingView: vibe-trading --pine 9. paper trade it with vibe-trading paper for a few weeks before you trust a single backtest i won't say anything more:
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Сarm1ne

65,316 次观看 • 15 天前

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zostaff

365,011 次观看 • 7 个月前

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qwinsi

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Ken Griffin, who runs Citadel, just admitted that the most expensive skill of his quants, the one he paid them millions for, now costs nothing And for the first time in history, that skill belongs not just to a hedge fund, but to anyone with access to AI A fund's entire quant team runs one cycle: come up with a strategy, turn it into code, test it on history, deploy it to the market Every step used to cost separate people and years of training. Now AI does the whole cycle You describe an idea in words, it writes the strategy itself, runs it on years of real data, and if it's alive, deploys it straight to a broker But the speed isn't the point. The point is what the retail trader doesn't understand You take a strategy with a beautiful chart and you believe it. But a beautiful chart is no proof Run enough versions and a profitable curve will appear even on random numbers. One just got lucky, and you decided you found gold Funds don't trust any curve until they test it on data the strategy has never seen. There the fit falls apart, and a real edge survives I ran two of the most-cited quant papers through this cycle Time Series Momentum on Bitcoin: +1,537%. Alive Betting Against Beta, a classic from every textbook: minus 7.78% a year while the market rose 14.86%. A corpse One still prints money, the other died long ago. And trading blind, you'd never know which is which until it took your deposit If you want to run your own idea through the same test, it takes minutes here (free trial after signing up): The entire hedge fund is six steps. AI already ate four of them. Which ones, I broke down in the article below
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qwinsi

424,580 次观看 • 27 天前

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It is becoming f*cking dangerous: A prompt for creating trading agents was accidentally leaked online. You paste it into claude code, and it builds a bot while you have lunch No code. Just Ctrl+C / Ctrl+V. One prompt solves everything: Time: 1 minute Action: copy and paste into Claude Code The main problem with 99% of bots: one strategy for everything. But the market on a calm day and during a crash are two completely different worlds Quant mode: detects market conditions (sideways, jittery, dump) and changes tactics on the fly E-brake: a strict 15% drawdown limit and risk checks before every order Hardcore backtest: runs the strategy on historical data and blocks trading if it is not profitable Nightly analyst: while you sleep, it analyzes mistakes and produces ready-to-approve setups Zero Code: type in the prompt, insert your parameters, and Claude handles the rest This used to take a whole development team and months of testing. Now it takes a single keyboard shortcut The prompt is published below, bookmark it so you do not lose it

Bober_smart

118,491 次观看 • 22 天前

Jane Street spent $6 billion and 4,032 GPUs on one thing: AI agents that loop through trading strategies 100x faster than any human. Generate, backtest, kill, repeat - thousands a week, until one survives data it's never seen. That loop is why they made $39.6 billion last year while 89% of retail went broke trading one idea they never stress-tested. The edge was never a smarter idea. It was volume. They test 100 strategies for every 1 you test by hand, and a second agent kills the 97 that only look good on old data before a dollar goes live. Now the entire loop fits in a chat box. Horizon takes one plain-English sentence and spins up the same two agents. One builds your strategy, one tries to kill it - backtests 5 years in seconds, and only what survives goes live on your exchange. The $150 billion setup. Free while beta open. Bookmark this & read the full breakdown below - the exact two-agent loop, step by step, and who just handed it to retail.
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Jane Street spent $6 billion and 4,032 GPUs on one thing: AI agents that loop through trading strategies 100x faster than any human. Generate, backtest, kill, repeat - thousands a week, until one survives data it's never seen. That loop is why they made $39.6 billion last year while 89% of retail went broke trading one idea they never stress-tested. The edge was never a smarter idea. It was volume. They test 100 strategies for every 1 you test by hand, and a second agent kills the 97 that only look good on old data before a dollar goes live. Now the entire loop fits in a chat box. Horizon takes one plain-English sentence and spins up the same two agents. One builds your strategy, one tries to kill it - backtests 5 years in seconds, and only what survives goes live on your exchange. The $150 billion setup. Free while beta open. Bookmark this & read the full breakdown below - the exact two-agent loop, step by step, and who just handed it to retail.

cvxv666

318,185 次观看 • 4 个月前

Citadel and Renaissance make billions on one test you've never heard of. It's now free. And almost no trading strategy survives it Most traders don't even know their strategy is dead. Because they never do the one thing every hedge fund does Split the history in half. Build the strategy on the first half. Don't touch the second one, the strategy must never see it Then test it only on that second half. If the edge is gone, it was never there. It was a fit to the past But here's the trap few people know. If you tried 200 versions and kept the best one, even this check will lie to you. Because one of the 200 passes it by pure luck That's what funds count and you don't. Not how pretty the chart is, but how many times you tried before it got pretty I took a simple rule: hold the S&P when it's above its 200-day average, exit when it's below On the years it had never seen, the same growth as buy-and-hold, but half the drawdown Then I ran it with the average at 50, 100, 150, 200, 250 days. A real edge holds across all of them. Luck works on one number and dies on the next Most textbook strategies don't pass this test. Your meme-coin one has no chance And here's the main trap: the prettier the chart, the more you believe it. The more you believe, the more you bet So the strategy you love most takes the most money The market will test your strategy either way. The only question is whether it's before or after you put money into it How to run this test on real data, before your strategy touches your money, in the article below
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Citadel and Renaissance make billions on one test you've never heard of. It's now free. And almost no trading strategy survives it Most traders don't even know their strategy is dead. Because they never do the one thing every hedge fund does Split the history in half. Build the strategy on the first half. Don't touch the second one, the strategy must never see it Then test it only on that second half. If the edge is gone, it was never there. It was a fit to the past But here's the trap few people know. If you tried 200 versions and kept the best one, even this check will lie to you. Because one of the 200 passes it by pure luck That's what funds count and you don't. Not how pretty the chart is, but how many times you tried before it got pretty I took a simple rule: hold the S&P when it's above its 200-day average, exit when it's below On the years it had never seen, the same growth as buy-and-hold, but half the drawdown Then I ran it with the average at 50, 100, 150, 200, 250 days. A real edge holds across all of them. Luck works on one number and dies on the next Most textbook strategies don't pass this test. Your meme-coin one has no chance And here's the main trap: the prettier the chart, the more you believe it. The more you believe, the more you bet So the strategy you love most takes the most money The market will test your strategy either way. The only question is whether it's before or after you put money into it How to run this test on real data, before your strategy touches your money, in the article below

qwinsi

10,334 次观看 • 22 天前

🚨| HOW ONE PERSON BUILT A FULLY AUTOMATED TRADING BOT IN 34 MINUTES, WITHOUT WRITING A SINGLE LINE OF CODE didn't touch a code editor. just talked to Claude Code. → detects market regime on its own: bull, bear, sideways → switches strategies automatically for each regime → 13 safety checks before every single trade → stop-loss, circuit breakers, position limits, correlation check → walk-forward backtest, not curve fitting, real simulation → connected to Alpaca for free paper trading → 45 out of 45 tests passing → full pipeline: data → regime → signal → risk → order → log hedge funds pay millions to build this. he paid $0.
34:17

🚨| HOW ONE PERSON BUILT A FULLY AUTOMATED TRADING BOT IN 34 MINUTES, WITHOUT WRITING A SINGLE LINE OF CODE didn't touch a code editor. just talked to Claude Code. → detects market regime on its own: bull, bear, sideways → switches strategies automatically for each regime → 13 safety checks before every single trade → stop-loss, circuit breakers, position limits, correlation check → walk-forward backtest, not curve fitting, real simulation → connected to Alpaca for free paper trading → 45 out of 45 tests passing → full pipeline: data → regime → signal → risk → order → log hedge funds pay millions to build this. he paid $0.

draven

451,338 次观看 • 2 个月前

Ken Griffin, who runs Citadel, just admitted that the biggest advantage of giant funds, armies of people and billion-dollar budgets, is disappearing And for the first time in history, a small team with AI can move faster than a hedge fund He’s seen it in his own firm. His AI agent does in 2-3 hours what used to take Citadel’s PhD researchers 6-8 weeks A fund runs one cycle: come up with a strategy, turn it into code, test it on history, deploy it to the market Every step used to cost separate people and years of training. Now AI does the whole cycle You describe an idea in words, it writes the strategy itself, runs it on years of real data, and if it’s alive, deploys it straight to a broker But the speed isn’t the point. The point is what the retail trader doesn’t understand You take a strategy with a beautiful chart and you believe it. But a beautiful chart is no proof Run enough versions and a profitable curve will appear even on random numbers. One just got lucky, and you decided you found gold Funds don’t trust any curve until they test it on data the strategy has never seen. There the fit falls apart, and a real edge survives I ran two of the most-cited quant papers through this cycle Time Series Momentum on Bitcoin: +1,537%. Alive Betting Against Beta, a classic from every textbook: minus 7.78% a year while the market rose 14.86%. A corpse One still prints money, the other died long ago. And trading blind, you’d never know which is which until it took your deposit Griffin says the future belongs to small teams with AI. But the only ones who win are the ones who test before they bet How to plug your AI into real market data so it runs this exact test instead of guessing, in the article below
0:24

Ken Griffin, who runs Citadel, just admitted that the biggest advantage of giant funds, armies of people and billion-dollar budgets, is disappearing And for the first time in history, a small team with AI can move faster than a hedge fund He’s seen it in his own firm. His AI agent does in 2-3 hours what used to take Citadel’s PhD researchers 6-8 weeks A fund runs one cycle: come up with a strategy, turn it into code, test it on history, deploy it to the market Every step used to cost separate people and years of training. Now AI does the whole cycle You describe an idea in words, it writes the strategy itself, runs it on years of real data, and if it’s alive, deploys it straight to a broker But the speed isn’t the point. The point is what the retail trader doesn’t understand You take a strategy with a beautiful chart and you believe it. But a beautiful chart is no proof Run enough versions and a profitable curve will appear even on random numbers. One just got lucky, and you decided you found gold Funds don’t trust any curve until they test it on data the strategy has never seen. There the fit falls apart, and a real edge survives I ran two of the most-cited quant papers through this cycle Time Series Momentum on Bitcoin: +1,537%. Alive Betting Against Beta, a classic from every textbook: minus 7.78% a year while the market rose 14.86%. A corpse One still prints money, the other died long ago. And trading blind, you’d never know which is which until it took your deposit Griffin says the future belongs to small teams with AI. But the only ones who win are the ones who test before they bet How to plug your AI into real market data so it runs this exact test instead of guessing, in the article below

qwinsi

67,760 次观看 • 15 天前

I backtested 400+ trading strategies with Claude, found a winner, and made + $19,537. In this video, I walk you through the entire process. Backtesting WINNING trading strategies with AI (cheat code):
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I backtested 400+ trading strategies with Claude, found a winner, and made + $19,537. In this video, I walk you through the entire process. Backtesting WINNING trading strategies with AI (cheat code):

Miles Deutscher

48,455 次观看 • 3 个月前

ChatGPT can write you a thousand trading strategies from one sentence. 999 will quietly bleed your account dry. Every single one of them looks like a winner. One method pulls the real one out. Quant desks have run it for years. It's called loop engineering. It's not a smarter prompt. It's a loop: generate a strategy, test it, score why it failed, feed that back, and run it again until one survives data it has never seen. Man Group runs $150 billion. They built an AI that writes and tests hundreds of strategies a week. And second agent whose only job is to hunt the ones that secretly overfit and kill them before a cent goes live. > describe an idea -> backtest 5 years in 12 seconds > read why it died -> feed that back in > run it again until one survives data it never saw AI can write a thousand strategies. Loop engineering is the only thing that tells you which one was ever real. 89% of retail lost money in 2025. Almost none of it from bad ideas. All of it from trading the first guess and never running it through backtest loop. Horizon just put the whole thing in a chat box, free while the beta's open: You're not losing to smarter traders. You're losing to the ones who opened the tab. Anyone can generate a strategy now. Almost no one runs the loop that proves it. That gap is the whole edge. You'll mean to check it later. Later is a "waitlist closed" page.
0:43

ChatGPT can write you a thousand trading strategies from one sentence. 999 will quietly bleed your account dry. Every single one of them looks like a winner. One method pulls the real one out. Quant desks have run it for years. It's called loop engineering. It's not a smarter prompt. It's a loop: generate a strategy, test it, score why it failed, feed that back, and run it again until one survives data it has never seen. Man Group runs $150 billion. They built an AI that writes and tests hundreds of strategies a week. And second agent whose only job is to hunt the ones that secretly overfit and kill them before a cent goes live. > describe an idea -> backtest 5 years in 12 seconds > read why it died -> feed that back in > run it again until one survives data it never saw AI can write a thousand strategies. Loop engineering is the only thing that tells you which one was ever real. 89% of retail lost money in 2025. Almost none of it from bad ideas. All of it from trading the first guess and never running it through backtest loop. Horizon just put the whole thing in a chat box, free while the beta's open: You're not losing to smarter traders. You're losing to the ones who opened the tab. Anyone can generate a strategy now. Almost no one runs the loop that proves it. That gap is the whole edge. You'll mean to check it later. Later is a "waitlist closed" page.

cvxv666

175,981 次观看 • 4 个月前

I Created a 520% Trading Strategy With One Prompt I have been testing the just-released “Index” product by Fraction AI. This is basically the “ChatGPT for trading,” where you can: > explain your trading strategy in one prompt > let agents create it for you > backtest it with real market data With one prompt, I was able to create a trading strategy that returned 520% APY with a 93% win rate. The prompt is in the video, but basically, it is long $BTC and $HYPE while shorting $SOL. Impressive how easy it is today to create and backtest incredibly profitable trading strategies. Extra note: the free version is already very powerful. I do NOT have the premium account.
0:31

I Created a 520% Trading Strategy With One Prompt I have been testing the just-released “Index” product by Fraction AI. This is basically the “ChatGPT for trading,” where you can: > explain your trading strategy in one prompt > let agents create it for you > backtest it with real market data With one prompt, I was able to create a trading strategy that returned 520% APY with a 93% win rate. The prompt is in the video, but basically, it is long $BTC and $HYPE while shorting $SOL. Impressive how easy it is today to create and backtest incredibly profitable trading strategies. Extra note: the free version is already very powerful. I do NOT have the premium account.

Diego

12,607 次观看 • 6 个月前

It is becoming f*cking dangerous: someone published the full prompt for building trading agents. You paste it into Claude Code and it builds the bot while you have lunch No code. Just Ctrl+C / Ctrl+V. One prompt sets up the whole thing: Time: 1 minute Action: copy and paste into Claude Code The main problem with 99% of bots: one strategy for everything. But the market on a calm day and during a crash are two completely different worlds Quant mode: detects the regime (sideways, jittery, dumping) and switches tactics on the fly E-brake: a hard 15% drawdown limit and a risk check before every single order Hardcore backtest: replays the strategy on historical data with fees and slippage, and shows you exactly where it falls apart. a curve that only looks good on the past is the easiest thing in the world to build Nightly analyst: while you sleep it reviews every fill, writes up what went wrong, and hands you the notes in the morning Near-zero code: you paste the prompt and your parameters, Claude writes the engine, and you read the risk layer before anything touches a live account This used to take a whole development team and months of testing. Now it takes a single keyboard shortcut The prompt is below. paper trade it first, then save it
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It is becoming f*cking dangerous: someone published the full prompt for building trading agents. You paste it into Claude Code and it builds the bot while you have lunch No code. Just Ctrl+C / Ctrl+V. One prompt sets up the whole thing: Time: 1 minute Action: copy and paste into Claude Code The main problem with 99% of bots: one strategy for everything. But the market on a calm day and during a crash are two completely different worlds Quant mode: detects the regime (sideways, jittery, dumping) and switches tactics on the fly E-brake: a hard 15% drawdown limit and a risk check before every single order Hardcore backtest: replays the strategy on historical data with fees and slippage, and shows you exactly where it falls apart. a curve that only looks good on the past is the easiest thing in the world to build Nightly analyst: while you sleep it reviews every fill, writes up what went wrong, and hands you the notes in the morning Near-zero code: you paste the prompt and your parameters, Claude writes the engine, and you read the risk layer before anything touches a live account This used to take a whole development team and months of testing. Now it takes a single keyboard shortcut The prompt is below. paper trade it first, then save it

Chen

192,088 次观看 • 21 天前

this is pure f*cking AI treasure 5 GitHub repos that give you the trading algo Wall Street pays millions dollars: want the engine serious quants run in production? -> NautilusTrader: a Rust-native trading engine where the backtest and the live run use the same code want a full research team arguing over every trade? -> TradingAgents: a multi-agent trading firm in one repo. analysts, researchers, a risk desk want the data terminal without the terminal bill? -> OpenBB: an open data platform for analysts, quants and AI agents want a bot that actually places the orders? -> freqtrade: free, open-source crypto trading bot with backtesting built in want all of it in one place? -> QuantDinger: an AI trading OS. research, Python strategies, backtest, paper and live trading ~282k stars of trading infrastructure. $0 in licences. now the part worth trying. every one of these has the same slow spot: the moment before the order, where something has to say yes or no. that's where Jev goes. it can't write a word. it returns BUY / SELL / HOLD with a probability in 70 to 500 milliseconds. QuantDinger already ships it as the pre-trade gate. and one repo, jev-trader, asks it buy or sell every ~300 ms and quotes a limit order on every single block. the stack in one line: OpenBB brings the data, the agents do the research, Jev makes the call, the bot places the order. the catch: fast is not the same as right. none of this is an ATM. paper trade it first. Jev + Claude + these 5 is the setup to test this month. check each repo before you install. save this.
0:27

this is pure f*cking AI treasure 5 GitHub repos that give you the trading algo Wall Street pays millions dollars: want the engine serious quants run in production? -> NautilusTrader: a Rust-native trading engine where the backtest and the live run use the same code want a full research team arguing over every trade? -> TradingAgents: a multi-agent trading firm in one repo. analysts, researchers, a risk desk want the data terminal without the terminal bill? -> OpenBB: an open data platform for analysts, quants and AI agents want a bot that actually places the orders? -> freqtrade: free, open-source crypto trading bot with backtesting built in want all of it in one place? -> QuantDinger: an AI trading OS. research, Python strategies, backtest, paper and live trading ~282k stars of trading infrastructure. $0 in licences. now the part worth trying. every one of these has the same slow spot: the moment before the order, where something has to say yes or no. that's where Jev goes. it can't write a word. it returns BUY / SELL / HOLD with a probability in 70 to 500 milliseconds. QuantDinger already ships it as the pre-trade gate. and one repo, jev-trader, asks it buy or sell every ~300 ms and quotes a limit order on every single block. the stack in one line: OpenBB brings the data, the agents do the research, Jev makes the call, the bot places the order. the catch: fast is not the same as right. none of this is an ATM. paper trade it first. Jev + Claude + these 5 is the setup to test this month. check each repo before you install. save this.

kiosa

22,061 次观看 • 28 天前

Less than 60 seconds to turn a trading idea into an executable quant strategy. Sorin configures a 4-hour BTC Turtle breakout, tunes its risk rules and validates the code. This backtest on Hyperliquid data returned 40%. Build yours at
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Less than 60 seconds to turn a trading idea into an executable quant strategy. Sorin configures a 4-hour BTC Turtle breakout, tunes its risk rules and validates the code. This backtest on Hyperliquid data returned 40%. Build yours at

Sahara AI 🔆

11,192 次观看 • 1 个月前

Ant Group's AI agents found a trading strategy on their own, and with it the fund hasn't lost once in the past year Sharpe ratio came out at 2.5. What used to take a team of PhDs and years of work, the agents did overnight, for free But the real power of this result isn't the number itself Agents can run thousands of times more strategy variations than any human could in a lifetime And the more variations you test, the higher the chance that one of them looks profitable purely by accident Your brain can't tell "I found a pattern" from "I got lucky on the hundredth try." The feeling is identical in both cases The only way to tell them apart: test the result on data the strategy has never seen A real edge survives there. Luck falls apart I got curious what this difference looks like live, so I took two real academic quant strategies and ran both through this exact test The first, Time Series Momentum on Bitcoin, passed: +1,537%, Sharpe 1.02. It looked honest, and it stayed honest The second, Betting Against Beta, a textbook classic in every finance course, looked flawless on paper. Under this test it fell apart: -7.78% a year, while the market grew 14.86% And the second result turned out to be the more valuable one. Not because it's positive, but because it's true, and it showed that before it could take real money I simply described both ideas in plain words, the AI built the exact rules itself and ran them on real data, with no peeking at future data You can test your own idea here (free trial period after sign-up): The same test on momentum and Betting Against Beta, step by step, so you can run it on your own idea tonight, is in the article below
0:24

Ant Group's AI agents found a trading strategy on their own, and with it the fund hasn't lost once in the past year Sharpe ratio came out at 2.5. What used to take a team of PhDs and years of work, the agents did overnight, for free But the real power of this result isn't the number itself Agents can run thousands of times more strategy variations than any human could in a lifetime And the more variations you test, the higher the chance that one of them looks profitable purely by accident Your brain can't tell "I found a pattern" from "I got lucky on the hundredth try." The feeling is identical in both cases The only way to tell them apart: test the result on data the strategy has never seen A real edge survives there. Luck falls apart I got curious what this difference looks like live, so I took two real academic quant strategies and ran both through this exact test The first, Time Series Momentum on Bitcoin, passed: +1,537%, Sharpe 1.02. It looked honest, and it stayed honest The second, Betting Against Beta, a textbook classic in every finance course, looked flawless on paper. Under this test it fell apart: -7.78% a year, while the market grew 14.86% And the second result turned out to be the more valuable one. Not because it's positive, but because it's true, and it showed that before it could take real money I simply described both ideas in plain words, the AI built the exact rules itself and ran them on real data, with no peeking at future data You can test your own idea here (free trial period after sign-up): The same test on momentum and Betting Against Beta, step by step, so you can run it on your own idea tonight, is in the article below

qwinsi

16,825 次观看 • 1 个月前

this is absolutely f*cking insane Black Rock just leaked 5 GitHub repos that give you the trading desk a fund pays millions for: want a full research team arguing over every trade? -> TradingAgents: a multi-agent trading firm in one repo. analysts, researchers, a risk desk want the data terminal without the terminal bill? -> OpenBB: an open data platform for analysts, quants and AI agents want a bot that actually places the orders? -> freqtrade: free, open-source crypto trading bot with backtesting built in want the engine serious quants run in production? -> NautilusTrader: a Rust-native trading engine where the backtest and the live run use the same code want all of it in one place? -> QuantDinger: an AI trading OS. research, Python strategies, backtest, paper and live trading ~282k stars of trading infrastructure. $0 in licences. now the part worth trying. every one of these has the same slow spot: the moment before the order, where something has to say yes or no. that's where Jev goes. it can't write a word. it returns BUY / SELL / HOLD with a probability in 70 to 500 milliseconds. QuantDinger already ships it as the pre-trade gate. and one repo, jev-trader, asks it buy or sell every ~300 ms and quotes a limit order on every single block. the stack in one line: OpenBB brings the data, the agents do the research, Jev makes the call, the bot places the order. the catch: fast is not the same as right. none of this is an ATM. paper trade it first. Jev + these five is the setup to test this month. bookmark and share with a friend
0:25

this is absolutely f*cking insane Black Rock just leaked 5 GitHub repos that give you the trading desk a fund pays millions for: want a full research team arguing over every trade? -> TradingAgents: a multi-agent trading firm in one repo. analysts, researchers, a risk desk want the data terminal without the terminal bill? -> OpenBB: an open data platform for analysts, quants and AI agents want a bot that actually places the orders? -> freqtrade: free, open-source crypto trading bot with backtesting built in want the engine serious quants run in production? -> NautilusTrader: a Rust-native trading engine where the backtest and the live run use the same code want all of it in one place? -> QuantDinger: an AI trading OS. research, Python strategies, backtest, paper and live trading ~282k stars of trading infrastructure. $0 in licences. now the part worth trying. every one of these has the same slow spot: the moment before the order, where something has to say yes or no. that's where Jev goes. it can't write a word. it returns BUY / SELL / HOLD with a probability in 70 to 500 milliseconds. QuantDinger already ships it as the pre-trade gate. and one repo, jev-trader, asks it buy or sell every ~300 ms and quotes a limit order on every single block. the stack in one line: OpenBB brings the data, the agents do the research, Jev makes the call, the bot places the order. the catch: fast is not the same as right. none of this is an ATM. paper trade it first. Jev + these five is the setup to test this month. bookmark and share with a friend

kiosa

30,442 次观看 • 29 天前

Supercharge your personal investing ✨Building advanced trading strategies used to take hours on end to code, test, and deploy. That changes with Composer: The AI-powered trading platform that lets you build trading strategies, test on historical data, and invest in a click. No coding needed. Learn more:
1:05

Supercharge your personal investing ✨Building advanced trading strategies used to take hours on end to code, test, and deploy. That changes with Composer: The AI-powered trading platform that lets you build trading strategies, test on historical data, and invest in a click. No coding needed. Learn more:

Composer by SoFi

72,110 次观看 • 3 年前

What if you could backtest an options trading strategy without writing a single line of code? Describe your idea in plain English, use AI to help build the strategy, and analyze historical performance. Here’s how Option Omega fits into that workflow 🧵👇
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What if you could backtest an options trading strategy without writing a single line of code? Describe your idea in plain English, use AI to help build the strategy, and analyze historical performance. Here’s how Option Omega fits into that workflow 🧵👇

Ai With Piyas

13,265 次观看 • 18 天前

The most insane thing i built with AI agents: A full hedge fund in a single file. 7 independent strategies. 2,000 lines of code. 5 symbols. One Expert Advisor. Each strategy has its own trade engine, its own risk parameters, its own magic numbers. They never touch each other. Why one file? Because that's how you control what actually matters: max drawdown across all strategies, overlap prevention so two algos don't stack the same directional bet, and anti-hedging logic for prop firm compliance. Nobody does this. Sharpe ratio above 2. Eight years of data. $100k starting balance. Claude Code launched MetaTrader, configured the parameters, ran the backtest, and switched to the equity curve. I didn't touch anything. Fully autonomous. I shared the exact logic and code structure inside the Discord. This is one of the most ambitious projects i've built so far:not just a strategy, a portfolio engine.
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The most insane thing i built with AI agents: A full hedge fund in a single file. 7 independent strategies. 2,000 lines of code. 5 symbols. One Expert Advisor. Each strategy has its own trade engine, its own risk parameters, its own magic numbers. They never touch each other. Why one file? Because that's how you control what actually matters: max drawdown across all strategies, overlap prevention so two algos don't stack the same directional bet, and anti-hedging logic for prop firm compliance. Nobody does this. Sharpe ratio above 2. Eight years of data. $100k starting balance. Claude Code launched MetaTrader, configured the parameters, ran the backtest, and switched to the equity curve. I didn't touch anything. Fully autonomous. I shared the exact logic and code structure inside the Discord. This is one of the most ambitious projects i've built so far:not just a strategy, a portfolio engine.

ZenomTrader

37,856 次观看 • 8 个月前