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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

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Sahara AI 🔆

586,041 subscribers

11,192 просмотров • 1 месяц назад •via X (Twitter)

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Комментарии: 4

Фото профиля Elise Tech Ai
Elise Tech Ai1 месяц назад

60 seconds to a full backtested strategy — that's insanely fast, love this!

Фото профиля Ella Tech & Tool
Ella Tech & Tool1 месяц назад

60 seconds to 40%?! 🤯 wild speed for quants

Фото профиля Engage IO
Engage IO1 месяц назад

The automated risk configuration is an interesting part of the workflow.

Фото профиля legen
legen1 месяц назад

Backtests look great until live slippage hits.

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Quant trading is now live in HeySorin.AI Choose one of 10 frameworks. Customize the rules. Backtest it against market history. Turn it into a live monitor. This version returned 28.90% across 25 trades in a 14-day backtest. Watch how it works, then build yours at 👇
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Quant trading is now live in HeySorin.AI Choose one of 10 frameworks. Customize the rules. Backtest it against market history. Turn it into a live monitor. This version returned 28.90% across 25 trades in a 14-day backtest. Watch how it works, then build yours at 👇

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GitHub lets your trading bot go broke on paper 10 open-source GitHub projects for turning a trading idea into a paper-tested bot GET THE DATA 01 yfinance ▸ → Yahoo Finance data → Python 02 CCXT ▸ → one API, 100+ crypto exchanges TEST THE IDEA 03 ▸ → backtest in a few lines 04 Qlib ▸ → ML quant research platform 05 QuantStats ▸ → risk + return report PAPER-RUN THE BOT 06 Freqtrade ▸ → crypto bot + dry-run + backtests 07 Hummingbot ▸ → market-making bots + paper-trading script 08 OctoBot ▸ → grid + DCA + paper trading HARDEN THE ENGINE 09 NautilusTrader ▸ → same strategy code, backtest + live 10 LEAN ▸ → event-driven engine + Python or C# the route: data → rules → research → score → dry-run → live engine → review 269k stars combined, as of Oct 6 live mode trades real money and needs exchange API keys paper shows where an idea breaks, not whether it pays ⭣
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GitHub lets your trading bot go broke on paper 10 open-source GitHub projects for turning a trading idea into a paper-tested bot GET THE DATA 01 yfinance ▸ → Yahoo Finance data → Python 02 CCXT ▸ → one API, 100+ crypto exchanges TEST THE IDEA 03 ▸ → backtest in a few lines 04 Qlib ▸ → ML quant research platform 05 QuantStats ▸ → risk + return report PAPER-RUN THE BOT 06 Freqtrade ▸ → crypto bot + dry-run + backtests 07 Hummingbot ▸ → market-making bots + paper-trading script 08 OctoBot ▸ → grid + DCA + paper trading HARDEN THE ENGINE 09 NautilusTrader ▸ → same strategy code, backtest + live 10 LEAN ▸ → event-driven engine + Python or C# the route: data → rules → research → score → dry-run → live engine → review 269k stars combined, as of Oct 6 live mode trades real money and needs exchange API keys paper shows where an idea breaks, not whether it pays ⭣

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Manual trading is the slowest part of your strategy. Not another bot, but a control panel to build your own trading agents & trade across DEXs from one terminal. Build agents, manage positions, and control TP/SL - all in Oraichain Quant Terminal. You set the rules and risk. The agent executes using quant models - to find entries, select orders, and run 24/7. Miss fewer moves. Think less. Execute better. 👉

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HORIZON V0.3 JUST HANDED RETAIL TRADERS THE EXACT TOOLS THAT USED TO COST A QUANT $400,000 A YEAR Backtest one strategy across BTC, ETH and SOL in a single run. Scale out in pieces with partial exits. Then deploy it live on Hyperliquid, the biggest perp DEX on the planet, running 24/7 with no broker and no KYC. Type an idea in plain English, test it across every asset in seconds, run it live while you sleep.

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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 way is walk-forward analysis > Train on one chunk of data, test on another > Slide the window, repeat > If the strategy survives on data it's never seen - that's a signal > If it doesn't - trash it, no regrets > The entire process runs through > Claude Code in one conversation > Not a single line of code written by hand > Trading here: > The same method used by quant funds The difference between a trader and an artist is a walk-forward test

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62,685 просмотров • 6 месяцев назад

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64,644 просмотров • 5 месяцев назад

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Herman Trading

11,203 просмотров • 14 дней назад

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OPUS 5.5 + DOTS + JEV built an armed trading bot in 60 minutes for $6.80 three agents, 1.45M tokens, and the bot had a cap, a stop and a kill switch before its first order rules → data → backtest → armed Dario's Opus 5.5 writes the strategy and the risk rules Dots pulls and cleans four years of candles Jev backtests it, sets the caps and is the only one allowed to send an order six switches flip one after another: strategy spec, data, bot code, backtest, paper trade, caps and kill switch. only the sixth lets it trade Jev holds three limits: a $2,000 daily loss cap, a 4% max position and an armed kill switch the backtest replayed 1,840 trades over four years. 58% green, 31% a year, worst drawdown -22% then fees, spread and slippage eat 45% of that, and 31% on paper becomes 17% in life at 17% a year, $100,000 a month needs $7.06M of capital so the $100k comes from the capital, and the bot only cost $6.80 not one live dollar has traded yet. sixty minutes proved the bot runs, whether it earns is still open 60 minutes, $6.80, three agents, zero live trades ↓

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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.

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OptimAI Network

22,280 просмотров • 2 месяцев назад

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opus 5.5 is now live on grok bot... this is a dangerously powerful combo for building a 24/7 quant research desk. agent swarms to research ideas. cursor agents to build and backtest strategies. {here's how to build it} → quant desk lead → market intelligence → quant researcher → validator → risk controller → portfolio adviser → execution operator run the whole desk under one constitution: no agent grades its own work. no lead overrides the risk limits. start with the desk lead. give every agent a role before you give it a task.

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🚨 $BTC IS SETTING UP A MAJOR BULL TRAP... Market cycles keep telling same story: • 2017 → ATH breakout → panic → 20x run • 2021 → ATH breakout → fear → 8x expansion • 2025 → ATH breakout → next panic phase loading... This move higher feels less like a genuine breakout and more like a liquidity grab Most traders are starting to turn bullish again That's usually when the market sets its biggest traps A major flush could be closer than it appears Turn on notifs - I'll keep you updated

Klarck

16,948 просмотров • 4 месяцев назад

A quick 6-step workflow to take a trading idea from raw data to a validated edge with the help of Claude. Step 1 - Connect Dhan (MCP) Claude can place orders, fetch positions, and pull info across NSE, BSE, MCX Step 2 - Install the Dhan skill The MCP gives tools, the skill gives know-how (correct API calls, instrument IDs etc) Step 3 - Download historical data Ask Claude to pull EOD history. Step 4 - Build the ratio file Create a CSV with necessary data for backtest Step 5 - Define rules and backtest Ratio breaks above 20-day high → hold NIFTYBEES Ratio breaks below 20-day low → rotate into GOLDBEES Step 6 - Validate
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A quick 6-step workflow to take a trading idea from raw data to a validated edge with the help of Claude. Step 1 - Connect Dhan (MCP) Claude can place orders, fetch positions, and pull info across NSE, BSE, MCX Step 2 - Install the Dhan skill The MCP gives tools, the skill gives know-how (correct API calls, instrument IDs etc) Step 3 - Download historical data Ask Claude to pull EOD history. Step 4 - Build the ratio file Create a CSV with necessary data for backtest Step 5 - Define rules and backtest Ratio breaks above 20-day high → hold NIFTYBEES Ratio breaks below 20-day low → rotate into GOLDBEES Step 6 - Validate

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26,409 просмотров • 3 месяцев назад

Google AI built my entire Android app — and I wrote ZERO code. I typed one simple English prompt, and Google AI handled the design, the backend, and the code. Watch me turn a basic idea into a working, installable app on my phone. Try it yourself: Google AI Studio → Build an Android app → type your prompt → done. #GoogleAI #AndroidApp #nocode See less
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Google AI built my entire Android app — and I wrote ZERO code. I typed one simple English prompt, and Google AI handled the design, the backend, and the code. Watch me turn a basic idea into a working, installable app on my phone. Try it yourself: Google AI Studio → Build an Android app → type your prompt → done. #GoogleAI #AndroidApp #nocode See less

Mohini Maheshwari

11,252 просмотров • 1 месяц назад

🚨Someone just dropped a free 1-hour masterclass on trading with Claude + TradingView, 0% to 100%: no fake signals, no "get rich" -> it literally opens with what AI can't do. 0% → 9:12 - what AI can and can't do in trading 15% → 11:46 - connect Claude to your TradingView charts, step by step 30% → 19:42 - how to prompt Claude once it can see your chart 55% → 47:43 - turn a plain-English idea into a rules-based strategy 75% → 53:06 - backtest that strategy with AI, no coding 100% → 1:01:23 - risk management and position sizing, the part that keeps you in the game most people chase a magic signal. this teaches you to make AI do the boring analysis and size so one bad call can't wipe you out. bookmark & watch ↓
1:10:16

🚨Someone just dropped a free 1-hour masterclass on trading with Claude + TradingView, 0% to 100%: no fake signals, no "get rich" -> it literally opens with what AI can't do. 0% → 9:12 - what AI can and can't do in trading 15% → 11:46 - connect Claude to your TradingView charts, step by step 30% → 19:42 - how to prompt Claude once it can see your chart 55% → 47:43 - turn a plain-English idea into a rules-based strategy 75% → 53:06 - backtest that strategy with AI, no coding 100% → 1:01:23 - risk management and position sizing, the part that keeps you in the game most people chase a magic signal. this teaches you to make AI do the boring analysis and size so one bad call can't wipe you out. bookmark & watch ↓

Ridark

35,911 просмотров • 2 месяцев назад

"Long oil the second Trump says something bad about Iran" Someone typed that into an AI agent. It pulled the data, wrote the code, ran a 10-year backtest, and shipped it live to their broker. Minutes. Every trader has an idea like that rotting in their head - never tested, because coding it wasn't worth a weekend. One that got typed in: buy stocks that gap up 2%, hold 30 days. Backtest came back +562%, profit factor 3.27. One click, live on Alpaca. The $650k quant job, now a text box. First 10,000 traders. Go type the idea you've been sitting on for two years. Demo's below.
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"Long oil the second Trump says something bad about Iran" Someone typed that into an AI agent. It pulled the data, wrote the code, ran a 10-year backtest, and shipped it live to their broker. Minutes. Every trader has an idea like that rotting in their head - never tested, because coding it wasn't worth a weekend. One that got typed in: buy stocks that gap up 2%, hold 30 days. Backtest came back +562%, profit factor 3.27. One click, live on Alpaca. The $650k quant job, now a text box. First 10,000 traders. Go type the idea you've been sitting on for two years. Demo's below.

cvxv666

245,608 просмотров • 3 месяцев назад

$87,500 per hypothesis, 6 weeks per cycle This was the Wall Street norm for 30 years > start building > 6 weeks building a crypto bot from scratch > strategy died on the first live trade > $4,800 on VPS, data, subscriptions, time > result: zero Same idea today in Horizon: > 90 seconds from text to backtest > strategy dies - you try the next one > cost of being wrong: a cup of coffee the bottleneck isn't code anymore, it's verification speed
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$87,500 per hypothesis, 6 weeks per cycle This was the Wall Street norm for 30 years > start building > 6 weeks building a crypto bot from scratch > strategy died on the first live trade > $4,800 on VPS, data, subscriptions, time > result: zero Same idea today in Horizon: > 90 seconds from text to backtest > strategy dies - you try the next one > cost of being wrong: a cup of coffee the bottleneck isn't code anymore, it's verification speed

Paruchh

17,100 просмотров • 4 месяцев назад

This self-evolving trading system kills 97% of its own strategies It's called SETS Machine. Nobody writes its strategies. It breeds them, tests them and kills them by itself Observe → hypothesize → mutate → backtest → select → deploy. Then again. Every 5 seconds Each strategy is a grid bot with 8 genes: entry logic, lookback, entry threshold, grid levels, spacing, size multiplier, take-profit, stop Every generation: 96 strategies. 8 random newcomers get injected. 80 offspring are bred from the strongest parents. Everything gets backtested on real BTC candles Then comes the part most "AI trading bots" skip Every strategy has to survive data it has never seen. It trains on 70% of the history and gets judged on the last 30%. Lose money there, draw down more than 10%, or win less than half your trades, and you're dead Only the elite live to the next generation. Everyone else is buried Four species compete at once: momentum, mean reversion, volatility breakout, range grid. Quotas stop one lucky species from wiping out the others The winner gets hot-swapped into a paper-trading grid, sized by Kelly. You watch every fill, every take-profit, every stop in real time This is the loop quants get paid $650K a year to run: take an idea, test it on history, kill it if it doesn't work, repeat Here it never stops No PhDs. No team. No servers And it's fully open source. Engine, backtests, dashboard, tests. No API keys, no build step. It runs right in your browser Click any strategy in the gene pool and see its DNA. Change the seed and grow a completely different evolution It even shows you honestly where it loses to buy & hold. Because a system that hides its losses isn't worth running GitHub:
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This self-evolving trading system kills 97% of its own strategies It's called SETS Machine. Nobody writes its strategies. It breeds them, tests them and kills them by itself Observe → hypothesize → mutate → backtest → select → deploy. Then again. Every 5 seconds Each strategy is a grid bot with 8 genes: entry logic, lookback, entry threshold, grid levels, spacing, size multiplier, take-profit, stop Every generation: 96 strategies. 8 random newcomers get injected. 80 offspring are bred from the strongest parents. Everything gets backtested on real BTC candles Then comes the part most "AI trading bots" skip Every strategy has to survive data it has never seen. It trains on 70% of the history and gets judged on the last 30%. Lose money there, draw down more than 10%, or win less than half your trades, and you're dead Only the elite live to the next generation. Everyone else is buried Four species compete at once: momentum, mean reversion, volatility breakout, range grid. Quotas stop one lucky species from wiping out the others The winner gets hot-swapped into a paper-trading grid, sized by Kelly. You watch every fill, every take-profit, every stop in real time This is the loop quants get paid $650K a year to run: take an idea, test it on history, kill it if it doesn't work, repeat Here it never stops No PhDs. No team. No servers And it's fully open source. Engine, backtests, dashboard, tests. No API keys, no build step. It runs right in your browser Click any strategy in the gene pool and see its DNA. Change the seed and grow a completely different evolution It even shows you honestly where it loses to buy & hold. Because a system that hides its losses isn't worth running GitHub:

Shelpid.WI3M

119,738 просмотров • 23 дней назад

🚨| 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

450,599 просмотров • 1 месяц назад

MIT defines an algorithm in one sentence that changes how you think about trading "a computational procedure that takes an input and produces an output through a well-defined sequence of steps" that's it. not AI. not machine learning. not a black box a set of rules that takes data in and spits a decision out every quant strategy ever built is just an algorithm Citadel's execution system that routes 40% of US equity volume is an algorithm Renaissance's Medallion Fund running millions of trades per year is an algorithm Jane Street's market making engine processing $26 trillion annually is an algorithm input: market data rules: mathematical conditions output: trade or no trade the difference between a quant desk and a retail trader is not the data it's that one side wrote down their rules precisely enough for a machine to execute them retail says "if RSI is low and the chart looks good, i'll probably buy" a quant desk says "if RSI 1.5, buy 0.3% of NAV" same logic. one is a feeling. the other is an algorithm the feeling can't be tested, can't be repeated, can't be measured the algorithm can be backtested across 10,000 trades and you know exactly when it works and when it doesn't > this lecture: MIT, free, 70 seconds > algorithmic trading volume: 60-75% of all US equity trades > Jane Street, Citadel, Two Sigma: every trade is algorithmically executed > tools to build your own: Python, free data, a laptop you don't need a faster computer or better data you need to write your strategy down precisely enough that a machine could run it without you that's the whole leap. from intuition to algorithm full breakdown in the video below
1:10

MIT defines an algorithm in one sentence that changes how you think about trading "a computational procedure that takes an input and produces an output through a well-defined sequence of steps" that's it. not AI. not machine learning. not a black box a set of rules that takes data in and spits a decision out every quant strategy ever built is just an algorithm Citadel's execution system that routes 40% of US equity volume is an algorithm Renaissance's Medallion Fund running millions of trades per year is an algorithm Jane Street's market making engine processing $26 trillion annually is an algorithm input: market data rules: mathematical conditions output: trade or no trade the difference between a quant desk and a retail trader is not the data it's that one side wrote down their rules precisely enough for a machine to execute them retail says "if RSI is low and the chart looks good, i'll probably buy" a quant desk says "if RSI 1.5, buy 0.3% of NAV" same logic. one is a feeling. the other is an algorithm the feeling can't be tested, can't be repeated, can't be measured the algorithm can be backtested across 10,000 trades and you know exactly when it works and when it doesn't > this lecture: MIT, free, 70 seconds > algorithmic trading volume: 60-75% of all US equity trades > Jane Street, Citadel, Two Sigma: every trade is algorithmically executed > tools to build your own: Python, free data, a laptop you don't need a faster computer or better data you need to write your strategy down precisely enough that a machine could run it without you that's the whole leap. from intuition to algorithm full breakdown in the video below

delost

23,905 просмотров • 3 месяцев назад