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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 ▸ →...

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Joying.eth

49,555 subscribers

17,443 次观看 • 9 天前 •via X (Twitter)

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8 条评论

𖥧 sof · ݁ ˖ 的头像
𖥧 sof · ݁ ˖9 天前

Totally agree with this

Brian Hadu 的头像
Brian Hadu8 天前

paper trading can be misleading if you don’t account for slippage and fees

Blanc 的头像
Blanc9 天前

dry-run fills at last price with no spread or queue position, so paper overstates by 2-5% a year. model fill as a function of book depth or live teaches it in week one.

SharkDev 的头像
SharkDev2 天前

last line is the whole post paper shows where it breaks not whether it pays most ppl never get that far

Mojunel.eth 的头像
Mojunel.eth9 天前

nice repo bro

Ridark 的头像
Ridark9 天前

wow

Andrew 的头像
Andrew9 天前

For the Freqtrade step, also run lookahead-analysis. Its docs give an easy-to-miss example: taking .mean() over the full dataframe lets a past signal use future candles. The check only covers signals that actually trigger, so make sure the test exercises each entry and exit.

安叫兽|Bird🕊️ 🔶 BNB 的头像
安叫兽|Bird🕊️ 🔶 BNB9 天前

纸上回测看着挺美，实盘的滑点和手续费才是硬仗

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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
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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

28,500 次观看 • 6 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.
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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 • 5 天前

8 GITHUB REPOS FOR BUILDING AN AI TRADING STACK Want to understand how autonomous trading systems are actually built? Start here. Not “AI predicts the next candle” bullshit. 8 repos covering agents, backtesting, exchange APIs, market making, RL and execution. 1. AI Hedge Fund Multi-agent setup where different agents analyze a trade from different angles before reaching a conclusion. 2. Freqtrade One of the biggest open-source crypto trading bots. Strategies, backtesting and automated execution. 3. CCXT The infrastructure layer for connecting to Binance, Bybit, OKX and dozens of other exchanges through one API. 4. NautilusTrader Trading infrastructure for research, backtesting and live execution. 5. Hummingbot Market making, arbitrage and liquidity automation. 6. Eliza Framework for autonomous agents that can interact with crypto wallets and onchain systems. 7. FinRL Reinforcement learning for financial trading research. 8. Jesse Python framework for developing and backtesting crypto trading strategies. The stack basically looks like: data → strategy → backtest → risk → execution → agent I’d treat these as a research starting point, not a money printer. Build locally. Test without real funds. Understand the system before giving an agent access to your wallet.
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8 GITHUB REPOS FOR BUILDING AN AI TRADING STACK Want to understand how autonomous trading systems are actually built? Start here. Not “AI predicts the next candle” bullshit. 8 repos covering agents, backtesting, exchange APIs, market making, RL and execution. 1. AI Hedge Fund Multi-agent setup where different agents analyze a trade from different angles before reaching a conclusion. 2. Freqtrade One of the biggest open-source crypto trading bots. Strategies, backtesting and automated execution. 3. CCXT The infrastructure layer for connecting to Binance, Bybit, OKX and dozens of other exchanges through one API. 4. NautilusTrader Trading infrastructure for research, backtesting and live execution. 5. Hummingbot Market making, arbitrage and liquidity automation. 6. Eliza Framework for autonomous agents that can interact with crypto wallets and onchain systems. 7. FinRL Reinforcement learning for financial trading research. 8. Jesse Python framework for developing and backtesting crypto trading strategies. The stack basically looks like: data → strategy → backtest → risk → execution → agent I’d treat these as a research starting point, not a money printer. Build locally. Test without real funds. Understand the system before giving an agent access to your wallet.

TeddyCEO

160,364 次观看 • 26 天前

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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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 ↓

Sprytix

13,497 次观看 • 13 天前

Tools from this article earn the creator of this bot $1,000,000. I reverse-engineered it and gave you all the tools completely for free. 28 repositories. 6 layers of execution - from bot reasoning engine all the way to paper-trading strategies. On-chain proof of the $1M wallet: This bot doesn’t predict the future. It exploits inefficiencies. This is literally what separates every single top leaderboard wallet - pure math edge, not crystal ball magic. Bot brain = Claude Squad with multiple Opus agents running in parallel. Trading management = Agency Agents, a pro swarm engine that replaces an entire PhD dev team. Full data collection suite (OpenBB + Binance collector + everything else you need). All the necessary repos for trading intelligence, strategy backtesting and Polymarket API interaction are included. This isn’t some basic script anymore - this is a full quant pipeline that shows you exactly how real trading algorithms are built. Bookmark this post or you’ll lose the article. Or start making money right now - copy trades of this $1M PnL bot using copy-trading terminal:
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Tools from this article earn the creator of this bot $1,000,000. I reverse-engineered it and gave you all the tools completely for free. 28 repositories. 6 layers of execution - from bot reasoning engine all the way to paper-trading strategies. On-chain proof of the $1M wallet: This bot doesn’t predict the future. It exploits inefficiencies. This is literally what separates every single top leaderboard wallet - pure math edge, not crystal ball magic. Bot brain = Claude Squad with multiple Opus agents running in parallel. Trading management = Agency Agents, a pro swarm engine that replaces an entire PhD dev team. Full data collection suite (OpenBB + Binance collector + everything else you need). All the necessary repos for trading intelligence, strategy backtesting and Polymarket API interaction are included. This isn’t some basic script anymore - this is a full quant pipeline that shows you exactly how real trading algorithms are built. Bookmark this post or you’ll lose the article. Or start making money right now - copy trades of this $1M PnL bot using copy-trading terminal:

cvxv666

56,293 次观看 • 5 个月前

i cancelled $2,000/month in trading subscriptions replaced every single one with open-source repos here's the full stack: 1. TradingView Pro ($30/mo) → lightweight-charts 14K stars. by TradingView themselves. 45KB. free 2. Bloomberg Terminal ($2,000/mo) → fredapi + Claude every macro dataset the Fed publishes. free API 3. backtest platform ($100/mo) → prediction-market-backtesting NautilusTrader fork with Polymarket + Kalshi adapters 4. real-time dashboard → polyrec terminal UI: Chainlink oracle, Binance feed, orderbook depth 70+ indicators. auto CSV logging. strategy backtester 5. bot framework (7 strategies) → Polymarket-Trading-Bot 53K lines TypeScript. arbitrage, momentum, market making, AI forecast, whale copy-trade, convergence 6. strategy reverse engineering → polybot execution + market data infrastructure. paper trading Kafka, ClickHouse, Grafana. full analytics pipeline 7. paper trading for AI agents → polymarket-paper-trader real order books. exact fee model. slippage tracking your Claude agent gets $10K paper money and trades 8. token savings → rtk CLI proxy. cuts Claude Code tokens by 60-90% Rust. single binary. 10 AI tools supported 9. Claude Code itself ($200/mo) → goose 35K stars. by Block (Jack Dorsey). Rust works with any LLM. full agent loop. free 10. wallet tracking + copy trading → Kreo track top Polymarket wallets. auto copy trades the only tool on this list i actually pay for because it makes more than it costs total before: ~$2,600/month total now: $0 + Kreo bookmark this. you'll need it
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i cancelled $2,000/month in trading subscriptions replaced every single one with open-source repos here's the full stack: 1. TradingView Pro ($30/mo) → lightweight-charts 14K stars. by TradingView themselves. 45KB. free 2. Bloomberg Terminal ($2,000/mo) → fredapi + Claude every macro dataset the Fed publishes. free API 3. backtest platform ($100/mo) → prediction-market-backtesting NautilusTrader fork with Polymarket + Kalshi adapters 4. real-time dashboard → polyrec terminal UI: Chainlink oracle, Binance feed, orderbook depth 70+ indicators. auto CSV logging. strategy backtester 5. bot framework (7 strategies) → Polymarket-Trading-Bot 53K lines TypeScript. arbitrage, momentum, market making, AI forecast, whale copy-trade, convergence 6. strategy reverse engineering → polybot execution + market data infrastructure. paper trading Kafka, ClickHouse, Grafana. full analytics pipeline 7. paper trading for AI agents → polymarket-paper-trader real order books. exact fee model. slippage tracking your Claude agent gets $10K paper money and trades 8. token savings → rtk CLI proxy. cuts Claude Code tokens by 60-90% Rust. single binary. 10 AI tools supported 9. Claude Code itself ($200/mo) → goose 35K stars. by Block (Jack Dorsey). Rust works with any LLM. full agent loop. free 10. wallet tracking + copy trading → Kreo track top Polymarket wallets. auto copy trades the only tool on this list i actually pay for because it makes more than it costs total before: ~$2,600/month total now: $0 + Kreo bookmark this. you'll need it

self.dll

803,521 次观看 • 6 个月前

most traders pay $3,000+/month for tools GitHub replaced for free. 9 repos. zero subscriptions. 1. OpenBB → replaces Bloomberg Terminal ($2,000/mo) financial data platform built for AI agents and quants connects natively to claude via MCP. most people don't know this. 2. freqtrade → replaces paid crypto bot services ($100/mo) ML strategy optimization. runs on binance, bybit, hyperliquid and 10+ others 34,000 stars. free and always will be. 3. hummingbot → replaces HFT bot platforms ($200/mo) $34B+ in user-generated trading volume has a native claude MCP integration. connect your AI directly to 140+ exchanges. 4. FinGPT → replaces financial AI subscriptions ($150/mo) open-source LLMs that outperform GPT-4 on market sentiment bloomberg spent $3M training theirs. this costs $17 to fine-tune. 5. NautilusTrader → replaces institutional trading platforms ($500/mo) production-grade. rust-native. fast enough to train RL trading agents same codebase for backtesting and live. zero rewrite needed. 6. QuantConnect Lean → replaces paid quant research platforms ($100/mo) professional algo trading engine. python + C# from backtest to live in one click. used by 200K+ quants worldwide. 7. jesse → replaces TradingView algo subscriptions ($25/mo) advanced crypto trading framework for serious strategy builders clean. powerful. no bloat. 8. vectorbt → replaces paid backtesting tools ($80/mo) fastest backtesting library in existence tests thousands of strategies in seconds. pandas-based. 9. FinRL → replaces custom AI trading infrastructure ($300/mo) financial reinforcement learning. train your own trading AI. from the same team behind FinGPT. 10. AlphaCartel Setup → replaces hedge fund signal services ($300/mo) this is where all 9 repos above connect into one working system claude-powered bots. live signals. no-code setup. community of traders already printing. total before: ~$3,455/month total now: $0 + alphacartel like + bookmark. you'll need this.
0:58

most traders pay $3,000+/month for tools GitHub replaced for free. 9 repos. zero subscriptions. 1. OpenBB → replaces Bloomberg Terminal ($2,000/mo) financial data platform built for AI agents and quants connects natively to claude via MCP. most people don't know this. 2. freqtrade → replaces paid crypto bot services ($100/mo) ML strategy optimization. runs on binance, bybit, hyperliquid and 10+ others 34,000 stars. free and always will be. 3. hummingbot → replaces HFT bot platforms ($200/mo) $34B+ in user-generated trading volume has a native claude MCP integration. connect your AI directly to 140+ exchanges. 4. FinGPT → replaces financial AI subscriptions ($150/mo) open-source LLMs that outperform GPT-4 on market sentiment bloomberg spent $3M training theirs. this costs $17 to fine-tune. 5. NautilusTrader → replaces institutional trading platforms ($500/mo) production-grade. rust-native. fast enough to train RL trading agents same codebase for backtesting and live. zero rewrite needed. 6. QuantConnect Lean → replaces paid quant research platforms ($100/mo) professional algo trading engine. python + C# from backtest to live in one click. used by 200K+ quants worldwide. 7. jesse → replaces TradingView algo subscriptions ($25/mo) advanced crypto trading framework for serious strategy builders clean. powerful. no bloat. 8. vectorbt → replaces paid backtesting tools ($80/mo) fastest backtesting library in existence tests thousands of strategies in seconds. pandas-based. 9. FinRL → replaces custom AI trading infrastructure ($300/mo) financial reinforcement learning. train your own trading AI. from the same team behind FinGPT. 10. AlphaCartel Setup → replaces hedge fund signal services ($300/mo) this is where all 9 repos above connect into one working system claude-powered bots. live signals. no-code setup. community of traders already printing. total before: ~$3,455/month total now: $0 + alphacartel like + bookmark. you'll need this.

AlphaCartel

108,813 次观看 • 5 个月前

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:
0:25

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 次观看 • 23 天前

i build a polymarket trading bot. took an old abandoned repo, rewrote 90% of it. name: zostaff repo: mit. python 3.12+. py-clob-client with eip-712 signed orders. usdc.e on polygon. three strategies in the box: AI Directional (llm via openrouter, any model), Safe Compounder (pure edge math, no llm), Beast Mode (aggressive, kept for comparison). most "ai bots" for polymarket on github are abandoned templates with hardcoded losing strategies. this one ships with a streamlit dashboard, sqlite telemetry on every trade, fallback chain across llm models, circuit breaker at 15% drawdown, quarter-kelly default, paper trading mode by default - until you flip LIVE_TRADING_ENABLED=true, nothing goes live. the readme has a section called "Lessons From Live Trading". real numbers from real losses: three-quarter kelly destroys capital on a 45% win-rate strategy. category discipline beats ai confidence every single time. a 50% drawdown limit isn't a limit. more trades without edge = faster path to zero. this is not a turnkey bot. nobody on polymarket is selling a turnkey bot that works. it's a toolkit with three example strategies you fork to fit your own edge. if you don't have an edge - no llm will save you. if you do - run paper mode for a week before flipping live.
0:46

i build a polymarket trading bot. took an old abandoned repo, rewrote 90% of it. name: zostaff repo: mit. python 3.12+. py-clob-client with eip-712 signed orders. usdc.e on polygon. three strategies in the box: AI Directional (llm via openrouter, any model), Safe Compounder (pure edge math, no llm), Beast Mode (aggressive, kept for comparison). most "ai bots" for polymarket on github are abandoned templates with hardcoded losing strategies. this one ships with a streamlit dashboard, sqlite telemetry on every trade, fallback chain across llm models, circuit breaker at 15% drawdown, quarter-kelly default, paper trading mode by default - until you flip LIVE_TRADING_ENABLED=true, nothing goes live. the readme has a section called "Lessons From Live Trading". real numbers from real losses: three-quarter kelly destroys capital on a 45% win-rate strategy. category discipline beats ai confidence every single time. a 50% drawdown limit isn't a limit. more trades without edge = faster path to zero. this is not a turnkey bot. nobody on polymarket is selling a turnkey bot that works. it's a toolkit with three example strategies you fork to fit your own edge. if you don't have an edge - no llm will save you. if you do - run paper mode for a week before flipping live.

zostaff

28,174 次观看 • 5 个月前

Do you understand what just got open sourced? 5,800+ quant trading strategies for FREE along with its CODEBASE i gave these trading strategies to opus 5.5 + jev and it built me a PROFITABLE 24/7 trading agent here is how to use the repo and build AI trading bot: 1. clone the vault and get 5,806 trading strategies (spot, perps, grid, stat arb and market making) 2. open any strategy file, the math, the parameters and the source are already inside 3. point AgenKit at that file inside Claude Code with model as opus 5.5, one command and it reads the spec as ground truth 4. it will run a full engineering team and ships a production bot with risk controls 5. Jev is the live brain, it answers buy sell or hold in 81 milliseconds, calibrated, no hallucination 6. your engine sizes the trade from that confidence and fires it before the next block 7. a hard risk layer holds every limit, nothing goes live until you approve it 8. every night Opus 5.5 reviews the fills and ships a better version, so the agent improves while you sleep one model THINKS, the other REACTS and a strategy that sat locked in a vault now trades itself 24/7 the COMPLETE codebase and the EXACT Opus 5.5 + Jev trading system which I am running is in the article below:
0:39

Do you understand what just got open sourced? 5,800+ quant trading strategies for FREE along with its CODEBASE i gave these trading strategies to opus 5.5 + jev and it built me a PROFITABLE 24/7 trading agent here is how to use the repo and build AI trading bot: 1. clone the vault and get 5,806 trading strategies (spot, perps, grid, stat arb and market making) 2. open any strategy file, the math, the parameters and the source are already inside 3. point AgenKit at that file inside Claude Code with model as opus 5.5, one command and it reads the spec as ground truth 4. it will run a full engineering team and ships a production bot with risk controls 5. Jev is the live brain, it answers buy sell or hold in 81 milliseconds, calibrated, no hallucination 6. your engine sizes the trade from that confidence and fires it before the next block 7. a hard risk layer holds every limit, nothing goes live until you approve it 8. every night Opus 5.5 reviews the fills and ships a better version, so the agent improves while you sleep one model THINKS, the other REACTS and a strategy that sat locked in a vault now trades itself 24/7 the COMPLETE codebase and the EXACT Opus 5.5 + Jev trading system which I am running is in the article below:

Roan

96,054 次观看 • 18 天前

Jev builds the MOST POWERFUL trading agents and someone JUST open sourced jev-trader, a fully working 24/7 trading bot with Jev along with COMPLETE low latency CODEBASE WHAT THIS MEANS FOR YOU - you no longer have to build a trading bot with Jev from scratch, you just clone this and make it yours here is how you make your own Jev trading bot with this repo: 1. clone it and run three commands, it boots straight into dry run mode with real book data, real decisions, and simulated fills so you can watch it think with zero capital 2. drop in your Jev API key and the model starts answering buy or sell on every block with calibrated probabilities in 81 milliseconds 3. swap the book reader for your own venue, the model interface is clean so any order book that returns bids and asks plugs straight in 4. tune the decision cadence and horizon, ask the model every N blocks about the move over the next M, so you control how aggressive the engine trades 5. the hot loop already fits one block with exactly two round trips, one to read the book, one to send the order, nothing else on the path, this is the institutional latency discipline most retail bots never reach 6. plug in the live server and every block, every decision, every fill streams to a public dashboard so you watch your engine run the whole point is this repo hands you HARDEST part for FREE - > the low latency engine the COMPLETE breakdown of how i turned this into hedge fund grade HFT trading system is in my article below:
0:30

Jev builds the MOST POWERFUL trading agents and someone JUST open sourced jev-trader, a fully working 24/7 trading bot with Jev along with COMPLETE low latency CODEBASE WHAT THIS MEANS FOR YOU - you no longer have to build a trading bot with Jev from scratch, you just clone this and make it yours here is how you make your own Jev trading bot with this repo: 1. clone it and run three commands, it boots straight into dry run mode with real book data, real decisions, and simulated fills so you can watch it think with zero capital 2. drop in your Jev API key and the model starts answering buy or sell on every block with calibrated probabilities in 81 milliseconds 3. swap the book reader for your own venue, the model interface is clean so any order book that returns bids and asks plugs straight in 4. tune the decision cadence and horizon, ask the model every N blocks about the move over the next M, so you control how aggressive the engine trades 5. the hot loop already fits one block with exactly two round trips, one to read the book, one to send the order, nothing else on the path, this is the institutional latency discipline most retail bots never reach 6. plug in the live server and every block, every decision, every fill streams to a public dashboard so you watch your engine run the whole point is this repo hands you HARDEST part for FREE - > the low latency engine the COMPLETE breakdown of how i turned this into hedge fund grade HFT trading system is in my article below:

Roan

173,149 次观看 • 25 天前

Grok Bot ran 13 hours 40 minutes of backtesting and paper trading today, straight through, no breaks I didn't run any of it. it called the agents it needed, they did the work, it read the results back to me numbers look good enough to move. first live tests with real money go in tonight, small, just to see how the mechanics hold up outside the sandbox if it doesn't embarrass me overnight, tomorrow $10,000 goes onto the public wallet and every trade starts streaming to the site first fully autonomous AI trading desk anybody has actually watched work. that's the shot. what's coming for solana:C3bajJW843KN9Uu441JkXN7zVMs4VM2HvdAGyGiBpump after that: - the source code and the prompts behind this whole build, so you can run your own Grok trading bot. paid in solana:C3bajJW843KN9Uu441JkXN7zVMs4VM2HvdAGyGiBpump, and every token spent gets burned - a separate strategy inside the fund that buys the dip on the token and burns what it buys - getting the site on dexscreener, so the live trading and all the data will be visible to everyone. someone else deployed the token, so I need access first god bless
0:41

Grok Bot ran 13 hours 40 minutes of backtesting and paper trading today, straight through, no breaks I didn't run any of it. it called the agents it needed, they did the work, it read the results back to me numbers look good enough to move. first live tests with real money go in tonight, small, just to see how the mechanics hold up outside the sandbox if it doesn't embarrass me overnight, tomorrow $10,000 goes onto the public wallet and every trade starts streaming to the site first fully autonomous AI trading desk anybody has actually watched work. that's the shot. what's coming for solana:C3bajJW843KN9Uu441JkXN7zVMs4VM2HvdAGyGiBpump after that: - the source code and the prompts behind this whole build, so you can run your own Grok trading bot. paid in solana:C3bajJW843KN9Uu441JkXN7zVMs4VM2HvdAGyGiBpump, and every token spent gets burned - a separate strategy inside the fund that buys the dip on the token and burns what it buys - getting the site on dexscreener, so the live trading and all the data will be visible to everyone. someone else deployed the token, so I need access first god bless

cvxv666

27,764 次观看 • 1 个月前

these 5 GitHub repos replaced my $2,400/month trading setup you're spending thousands on software anyone can fork the repos behind those tools are sitting on GitHub for free here's what i actually run: 1. fredapi (1K ★) Bloomberg charges $2,000/mo for macro data this pulls every Fed dataset into Python with one API key GDP, CPI, rates, employment - pair it with Claude and you're set /// 2. ccxt (42K ★) i was paying $50/mo per exchange just for API access ccxt connects 107 exchanges through one interface JS; Python; PHP; Go; C# the industry standard since day one /// 3. freqtrade (47K ★) killed my $200/mo bot subscription in one afternoon backtesting, hyperopt, FreqAI, live on 20+ exchanges 7 years of weekly commits. Telegram control. your strategies stay yours /// 4. OpenBB (40K ★) equities, options, crypto, fixed income, macro - one terminal plugs into Python, Excel, REST API, MCP for AI agents my $500/mo data feed couldn't do half of this /// 5. goose (40K ★) by Block (Jack Dorsey). Rust. Apache 2.0 full AI coding agent. any LLM. 3,000+ MCP tools. runs locally the $200/mo i was spending on Claude Code now costs me $0 /// bookmark this and thank me later
0:57

these 5 GitHub repos replaced my $2,400/month trading setup you're spending thousands on software anyone can fork the repos behind those tools are sitting on GitHub for free here's what i actually run: 1. fredapi (1K ★) Bloomberg charges $2,000/mo for macro data this pulls every Fed dataset into Python with one API key GDP, CPI, rates, employment - pair it with Claude and you're set /// 2. ccxt (42K ★) i was paying $50/mo per exchange just for API access ccxt connects 107 exchanges through one interface JS; Python; PHP; Go; C# the industry standard since day one /// 3. freqtrade (47K ★) killed my $200/mo bot subscription in one afternoon backtesting, hyperopt, FreqAI, live on 20+ exchanges 7 years of weekly commits. Telegram control. your strategies stay yours /// 4. OpenBB (40K ★) equities, options, crypto, fixed income, macro - one terminal plugs into Python, Excel, REST API, MCP for AI agents my $500/mo data feed couldn't do half of this /// 5. goose (40K ★) by Block (Jack Dorsey). Rust. Apache 2.0 full AI coding agent. any LLM. 3,000+ MCP tools. runs locally the $200/mo i was spending on Claude Code now costs me $0 /// bookmark this and thank me later

Hanako

55,342 次观看 • 6 个月前

Jev builds the BEST trading agents and someone JUST open sourced QuantDinger, a full AI Trading OS with Jev your strategy no longer fires blind, every entry gets reviewed by Jev first and bad trades die before they cost you here is how you set it up: 1. run one install command, it asks for your admin login, generates every secret, and boots the whole stack in docker 2. start in paper mode, backtest your Python strategy on server side data before a single real dollar moves 3. drop your Jev API key into System Settings, AI / LLM, and flip on AI Decision Filter 4. now every entry gets scored across evidence quality, signal consistency, market regime, account risk, and execution quality 5. Jev returns a typed decision with full probabilities and confidence, not a paragraph of text you have to parse 6. rejected entries NEVER reach the exchange, and every decision lands in an audit timeline with the reason and latency the whole point is this repo hands you the HARDEST part for FREE > the risk gate between your strategy and your money Bookmark it, then read my step by step guide below on making money from your own agent team for free
0:35

Jev builds the BEST trading agents and someone JUST open sourced QuantDinger, a full AI Trading OS with Jev your strategy no longer fires blind, every entry gets reviewed by Jev first and bad trades die before they cost you here is how you set it up: 1. run one install command, it asks for your admin login, generates every secret, and boots the whole stack in docker 2. start in paper mode, backtest your Python strategy on server side data before a single real dollar moves 3. drop your Jev API key into System Settings, AI / LLM, and flip on AI Decision Filter 4. now every entry gets scored across evidence quality, signal consistency, market regime, account risk, and execution quality 5. Jev returns a typed decision with full probabilities and confidence, not a paragraph of text you have to parse 6. rejected entries NEVER reach the exchange, and every decision lands in an audit timeline with the reason and latency the whole point is this repo hands you the HARDEST part for FREE > the risk gate between your strategy and your money Bookmark it, then read my step by step guide below on making money from your own agent team for free

Khairallah AL-Awady

11,778 次观看 • 24 天前

The founder of the hedge fund behind DeepSeek built an AI machine that wrecks human traders. Now that same principle is available to anyone +56.6% a year. $10 billion under management. Almost no people in the room Liang Wenfeng did to trading what DeepSeek did to AI. Replaced the smartest people with a machine that doesn't sleep When someone who's been winning this game for decades puts a machine in place of a team of PhDs, it's worth listening The edge was never brains. One cycle: idea, test it on history, kill it if it doesn't work, repeat. A thousand times a night, while you test one idea a week That cycle used to cost millions and only lived inside hedge funds. This year the price dropped to zero I took Time Series Momentum, the paper by Moskowitz, Ooi, and Pedersen, one of the most-cited in quant trading. Just told AI to reproduce it on real data from the last 6 years. 10 minutes of backtesting +1,537% on Bitcoin. Sharpe 1.02 Then I took Carry, the paper by Koijen, Moskowitz, Pedersen, and Vrugt, cited for decades. Flawless on paper. On current data, dead: Sharpe dropped from 0.61 to 0.11 That dead result was the most valuable one. It's what saved me real money The machine doesn't tell you what you want to hear. It tells you the truth. And now that truth costs nothing The same cycle Liang's hedge fund runs on now fits into a text box Describe a strategy in one sentence, see if it survives (free trial after signing up): The same test on momentum and carry trade, step by step, so you can run it on your own idea tonight, in the article below
0:25

The founder of the hedge fund behind DeepSeek built an AI machine that wrecks human traders. Now that same principle is available to anyone +56.6% a year. $10 billion under management. Almost no people in the room Liang Wenfeng did to trading what DeepSeek did to AI. Replaced the smartest people with a machine that doesn't sleep When someone who's been winning this game for decades puts a machine in place of a team of PhDs, it's worth listening The edge was never brains. One cycle: idea, test it on history, kill it if it doesn't work, repeat. A thousand times a night, while you test one idea a week That cycle used to cost millions and only lived inside hedge funds. This year the price dropped to zero I took Time Series Momentum, the paper by Moskowitz, Ooi, and Pedersen, one of the most-cited in quant trading. Just told AI to reproduce it on real data from the last 6 years. 10 minutes of backtesting +1,537% on Bitcoin. Sharpe 1.02 Then I took Carry, the paper by Koijen, Moskowitz, Pedersen, and Vrugt, cited for decades. Flawless on paper. On current data, dead: Sharpe dropped from 0.61 to 0.11 That dead result was the most valuable one. It's what saved me real money The machine doesn't tell you what you want to hear. It tells you the truth. And now that truth costs nothing The same cycle Liang's hedge fund runs on now fits into a text box Describe a strategy in one sentence, see if it survives (free trial after signing up): The same test on momentum and carry trade, step by step, so you can run it on your own idea tonight, in the article below

qwinsi

83,889 次观看 • 20 天前

Made $530,000 with Ai Bot that started with $313. Didn't know how to code. Now this bots run 24/7 printing money while sleeping. I've made the exact step-by-step guide to build this Claude Code Polymarket trading bot. Prompts. Code. Risk settings. Paper trading checklist. Everything from zero to running bot. It's free. For 24 hours. After that I'm charging $499 for it. To grab it right now: 1. Comment "Claude Bot" 2. Like and Retweet this post 3. Follow me Himanshu Kumar ( I can't send DMs to non-followers ) I'm DMing everyone who Complete the 3 steps. I spent hundreds of thousands hiring developers because he was too scared to learn. Then learned Claude Code. Built algorithmic trading systems. $313 → $530,000. You have the same tools available right now. And you're using them to ask ChatGPT for Instagram captions. This attached video is a goldmine. Full live walkthrough. Claude Code building actual Polymarket trading bots. From zero. Every line of code. Every decision explained. Now let me break down why everything you're doing in trading is wrong and exactly how to fix it. Save this post. You'll hate yourself if you lose it. ↓ Let's start with why you keep losing money. You already know the answer. You just won't admit it. You overtrade. Every. Single. Day. You see a candle move. You feel something. You enter. No plan. No edge. No reason. Just feelings. Then it goes against you. You feel something else. Panic. Anger. Denial. You move your stop loss. Or you didn't set one at all. "It'll come back." It doesn't come back. So you take another trade. A revenge trade. Bigger size this time. Because you need to "make it back." That one fails too. Now you're emotional. Now you're tilted. Now you're using leverage you have no business touching. 40x. 50x. 100x. On a trade you entered because a candle looked "bullish" and some guy on Twitter said "send it." You get liquidated. Close the laptop. Punch something. Tell yourself you'll be "more disciplined" tomorrow. Tomorrow comes. Same cycle. Same result. Same liquidation. You've been doing this for months. Maybe years. And you still think the problem is your strategy. The problem isn't your strategy. The problem is you. Save this post right now. What I'm about to show you is the only way to remove yourself from the equation. Follow Himanshu Kumar so you don't miss any of this. ↓ Here's what's actually killing your account. It's not the market. The market doesn't care about you. It's not your indicators. RSI works fine. MACD works fine. They all "work." It's not your timeframe. It's not your broker. It's not the "manipulation." It's four things: 1. Emotions. You hold losers because hope feels better than loss. You cut winners because fear feels stronger than greed. You size up when angry. You skip trades when scared. Your emotional state determines your position size. That's insane. And you know it's insane. But you keep doing it. 2. Overtrading. You take 15 trades a day. Maybe 5 of them had actual setups. The other 10 were boredom. Boredom trades are the most expensive hobby in human history. 3. Leverage. You use 20x-50x on trades where you're not even sure about the direction. That's not trading. That's a casino with a nicer interface. 4. Fees. You're smashing market orders. Paying spread. Paying commission. On 15 trades a day. Your broker makes more money from your account than you do. Think about that. Your broker is profitable on your account. You're not. You're the product. Not the trader. These four things are why 90% of traders lose. Not bad luck. Not the market. You. Save this post and follow Himanshu Kumar because the solution is coming next. ↓ The solution is painfully obvious. Remove yourself from the equation. Not partially. Not "I'll be more disciplined." Not "I'll journal my trades." Not "I'll meditate before trading." Completely remove yourself. Build a bot. Let the bot trade. You go live your life. The bot doesn't feel emotions. The bot doesn't overtrade. The bot doesn't use reckless leverage. The bot doesn't smash market orders and bleed fees. The bot follows the rules. Every single time. Without exception. Without "just this once." Without "I have a feeling about this one." Rules in. Execution out. No human in the middle to mess everything up. That's algorithmic trading. And before your ego jumps in with "but I'm different, I have discipline" — No you don't. Your account balance proves you don't. If you had discipline, your account would be green. It's not. So you don't. Accept it. Automate it. Move on. This is the hardest truth in trading. Your discipline will always fail. A bot's won't. Save this post. Follow Himanshu Kumar for the exact bot setup that removes your emotions permanently. ↓ "But I don't know how to code." Neither did he. The guy in this video didn't know how to code for most of his life. Got held back in 7th grade. People counted him out early. Spent years building apps and SaaS businesses without writing a single line of code. Hired developers on Upwork instead. Spent hundreds of thousands of dollars paying other people to build what he could have built himself. Because he was scared to learn. That fear cost him years. And hundreds of thousands of dollars. Sound familiar? You're doing the same thing right now. Not with developers. But with your time. You're spending thousands of hours trading manually because you're scared to learn the thing that would make trading automatic. The fear of learning to code is costing you more than any bad trade ever did. Because every month you trade manually is a month of emotional decisions, overleveraged entries, and unnecessary losses that a bot would never make. And here's the thing that should really frustrate you: AI does the hard parts now. You don't need a computer science degree. You don't need to work at a hedge fund. You don't need to be "good at math." Claude Code writes the code for you. You just need to think clearly about trading ideas. That's it. If you can describe a strategy in English, Claude can build it in Python. "I don't know how to code" stopped being a valid excuse in 2024. It's 2026. You're 2 years late on that excuse. Find a new one. Or stop making excuses entirely. Save this post. Follow Himanshu Kumar because I'm showing you how people with zero coding experience are building profitable bots. ↓ The process that actually makes money. Three letters. R. B. I. Research. Backtest. Implement. That's it. That's the entire process. Every single day. Research: Find an idea. A pattern. A market inefficiency. Don't trade it yet. Don't even think about trading it yet. Just research it. Backtest: Test the idea against historical data. Does it work? Not "does it look good on one chart." Does it work across thousands of trades? Across different market conditions? Across in-sample AND out-of-sample data? If no, kill it. Find another idea. If yes, move to step 3. Implement: Build the bot. Deploy it. Paper trade first. Then live with small size. Scale only on evidence. Research. Backtest. Implement. Every day. No exceptions. You know what your current process is? Feel. Enter. Pray. F. E. P. Feel bullish. Enter a trade. Pray it works. That's not a process. That's gambling with a TradingView subscription. RBI is the only process that works. Save this post. Tattoo it on your forearm. Follow Himanshu Kumar for daily RBI breakdowns. ↓ What Claude Code actually does that your manual process can't. You can maybe test 3-5 strategy ideas per week. Manually adjusting parameters. Manually checking results. Manually writing code (badly). Claude Code tests 50-100 ideas per week. With parallel agents running simultaneously. Multiple strategies being built, tested, and validated at the same time. While you sleep. The guy in this video spends 4-8 hours a day building systems with Claude Code. Not trading. Building. Research. Backtest. Implement. Then iterate. Improve. Optimize. Every day the systems get better. Every day the edge compounds. Every day the bots get smarter. While you? You spend 4-8 hours a day staring at charts making the same mistakes you made last month. Same indicators. Same patterns. Same entries. Same losses. He's iterating forward. You're running in circles. Same 8 hours per day. Completely different outcomes. Because he's building systems. And you're feeding a casino. Stop feeding the casino. Start building the machine. Save this post and follow Himanshu Kumar for the Claude Code workflow that iterates strategies while you sleep. ↓ Jim Simons. That's the benchmark. You probably don't know who Jim Simons is. And that tells me everything about how seriously you take trading. Jim Simons. Mathematician. Founded Renaissance Technologies. Built a net worth of $31 billion. 100% from algorithmic trading. Not one single manual trade. Not one "gut feeling" entry. Not one RSI divergence. Not one "smart money concept." Algorithms. Bots. Systems. Data. $31 billion. His fund averaged 66% annual returns for over 30 years. While you're excited about making $200 on a trade that you'll give back tomorrow. The best trader in human history never placed a manual trade in his life. And you think your edge is staring at a 5-minute chart with bloodshot eyes at 2 AM? Your edge is building the system. Not being inside it. Jim Simons is the benchmark. Everything else is noise. Save this post. Follow Himanshu Kumar because I'm building toward the same goal and showing every step publicly. ↓ What you need to understand about patience. This is not get-rich-overnight. The guy in this video says it directly: "This channel is not for people looking to get rich overnight. It's not plug and play. There are no shortcuts. If you're impatient, this probably isn't for you." And that's exactly why most people will fail at this. Because you want results now. Today. This trade. You don't want to spend a week building a bot. You don't want to paper trade for 2 weeks. You don't want to test 50 ideas to find 1 that works. You want to copy someone's bot, run it live with your rent money, and be rich by Friday. That's why you'll be broke by Friday. The guy making $2.3M spent months iterating. Testing. Failing. Rebuilding. Testing again. He was patient when you would have quit. He was calm when you would have panicked. He was consistent when you would have given up. Patience isn't just a virtue in trading. It's the only virtue. Without it, everything else fails. Impatience is the most expensive personality trait in trading. Save this post. Follow Himanshu Kumar and learn to build systems with the patience that actually pays. ↓ The live streams where the real learning happens. The YouTube video is the trailer. The live streams are the movie. Real-time bot building. Real-time questions answered. Real code shown. Real mistakes made and fixed. Not polished highlight reels where everything works perfectly. Actual development. Where things break. Where strategies fail. Where code doesn't compile. Where the fix takes 2 hours. Because that's what real development looks like. And seeing the messy parts is more valuable than any polished tutorial. Because when your bot breaks at 3 AM, you need to know how to fix it. Not just how to celebrate when it works. The streams mix beginner and advanced. Start with how to automate trading. How to use AI for code generation. Then dive into the daily work. Claude Code. Parallel agents. Constant iteration. Live debugging. 4-8 hours of real algorithmic trading development. Live. Uncut. No filter. Most "trading education" shows you the wins. This shows you the work. Save this post. Follow Himanshu Kumar for the stream schedules and breakdowns. ↓ The belief that changes everything. Code is the greatest equalizer. Not money. Not connections. Not a degree. Not where you grew up. Not what school you went to. Code. Once you can build systems, you can build anything. For the rest of your life. A trading bot today. A SaaS product tomorrow. An automation business next month. A completely different life next year. The skill isn't "algorithmic trading." The skill is building systems. And that skill transfers to everything. The guy who can build a trading bot can also build a lead gen tool. Can also build a content pipeline. Can also build a SaaS product. Can also build literally anything that runs on logic and code. One skill. Infinite applications. And AI makes learning it 100x easier than it was 5 years ago. You don't need to be smart. You don't need talent. You need Claude Code and the willingness to sit down and build something instead of consuming content about building something. Building is the skill. Everything else is entertainment disguised as education. Save this post. Follow Himanshu Kumar because I'm showing you how to build, not just how to watch. ↓ If any of this applies to you, pay attention. If you've lost money from overtrading. If you've been liquidated. If you know trading is the vehicle but manual execution keeps crashing you. If you've tried "being more disciplined" and it never lasted more than a week. If you keep saying "next month I'll start automating." If you've spent more money on courses than you've made from trading. There is a better way. It's not a magic indicator. It's not a signal group. It's not a $997 mentorship from a guy who makes money teaching, not trading. It's building your own system. A system that trades without emotion. A system that follows rules without exception. A system that runs while you sleep. A system that compounds while you live your life. That's the answer. It's always been the answer. You've just been too scared to accept that the solution requires building something instead of buying something. ↓ What the next 30 days look like if you actually commit. Week 1: Watch the video. Learn Claude Code basics. Build your first simple strategy. Run your first backtest. Week 2: Iterate. Let Claude improve the strategy. Run Monte Carlo validation. Paper trade. Week 3: Go live with $50-100. Tiny positions. Watch every trade. Compare to paper results. Week 4: Scale based on evidence. Not based on excitement. Not based on one good day. Based on data. 30 days from now you either have a running bot that trades without your emotions destroying every position. Or you're exactly where you are right now. Reading another post. Making another promise. Breaking it by Tuesday. Same 30 days either way. Different actions. Different results. Different life. ↓ Full video tutorial attached. Live bot building with Claude Code. From zero to running Polymarket trading bot. Every line of code. Every decision explained. The video is free. Claude Code is available now. The market is open 24/7. The only thing standing between you and a profitable trading bot is the same thing that's been standing there for months. You. Get out of your own way. Follow Himanshu Kumar for daily AI trading bot breakdowns, live build sessions, and the full RBI process. Save this post. Watch the video. Build the bot. Or keep trading manually and keep losing. The choice has never been easier. And you've never been more stubborn about making the wrong one.
18:53

Made $530,000 with Ai Bot that started with $313. Didn't know how to code. Now this bots run 24/7 printing money while sleeping. I've made the exact step-by-step guide to build this Claude Code Polymarket trading bot. Prompts. Code. Risk settings. Paper trading checklist. Everything from zero to running bot. It's free. For 24 hours. After that I'm charging $499 for it. To grab it right now: 1. Comment "Claude Bot" 2. Like and Retweet this post 3. Follow me Himanshu Kumar ( I can't send DMs to non-followers ) I'm DMing everyone who Complete the 3 steps. I spent hundreds of thousands hiring developers because he was too scared to learn. Then learned Claude Code. Built algorithmic trading systems. $313 → $530,000. You have the same tools available right now. And you're using them to ask ChatGPT for Instagram captions. This attached video is a goldmine. Full live walkthrough. Claude Code building actual Polymarket trading bots. From zero. Every line of code. Every decision explained. Now let me break down why everything you're doing in trading is wrong and exactly how to fix it. Save this post. You'll hate yourself if you lose it. ↓ Let's start with why you keep losing money. You already know the answer. You just won't admit it. You overtrade. Every. Single. Day. You see a candle move. You feel something. You enter. No plan. No edge. No reason. Just feelings. Then it goes against you. You feel something else. Panic. Anger. Denial. You move your stop loss. Or you didn't set one at all. "It'll come back." It doesn't come back. So you take another trade. A revenge trade. Bigger size this time. Because you need to "make it back." That one fails too. Now you're emotional. Now you're tilted. Now you're using leverage you have no business touching. 40x. 50x. 100x. On a trade you entered because a candle looked "bullish" and some guy on Twitter said "send it." You get liquidated. Close the laptop. Punch something. Tell yourself you'll be "more disciplined" tomorrow. Tomorrow comes. Same cycle. Same result. Same liquidation. You've been doing this for months. Maybe years. And you still think the problem is your strategy. The problem isn't your strategy. The problem is you. Save this post right now. What I'm about to show you is the only way to remove yourself from the equation. Follow Himanshu Kumar so you don't miss any of this. ↓ Here's what's actually killing your account. It's not the market. The market doesn't care about you. It's not your indicators. RSI works fine. MACD works fine. They all "work." It's not your timeframe. It's not your broker. It's not the "manipulation." It's four things: 1. Emotions. You hold losers because hope feels better than loss. You cut winners because fear feels stronger than greed. You size up when angry. You skip trades when scared. Your emotional state determines your position size. That's insane. And you know it's insane. But you keep doing it. 2. Overtrading. You take 15 trades a day. Maybe 5 of them had actual setups. The other 10 were boredom. Boredom trades are the most expensive hobby in human history. 3. Leverage. You use 20x-50x on trades where you're not even sure about the direction. That's not trading. That's a casino with a nicer interface. 4. Fees. You're smashing market orders. Paying spread. Paying commission. On 15 trades a day. Your broker makes more money from your account than you do. Think about that. Your broker is profitable on your account. You're not. You're the product. Not the trader. These four things are why 90% of traders lose. Not bad luck. Not the market. You. Save this post and follow Himanshu Kumar because the solution is coming next. ↓ The solution is painfully obvious. Remove yourself from the equation. Not partially. Not "I'll be more disciplined." Not "I'll journal my trades." Not "I'll meditate before trading." Completely remove yourself. Build a bot. Let the bot trade. You go live your life. The bot doesn't feel emotions. The bot doesn't overtrade. The bot doesn't use reckless leverage. The bot doesn't smash market orders and bleed fees. The bot follows the rules. Every single time. Without exception. Without "just this once." Without "I have a feeling about this one." Rules in. Execution out. No human in the middle to mess everything up. That's algorithmic trading. And before your ego jumps in with "but I'm different, I have discipline" — No you don't. Your account balance proves you don't. If you had discipline, your account would be green. It's not. So you don't. Accept it. Automate it. Move on. This is the hardest truth in trading. Your discipline will always fail. A bot's won't. Save this post. Follow Himanshu Kumar for the exact bot setup that removes your emotions permanently. ↓ "But I don't know how to code." Neither did he. The guy in this video didn't know how to code for most of his life. Got held back in 7th grade. People counted him out early. Spent years building apps and SaaS businesses without writing a single line of code. Hired developers on Upwork instead. Spent hundreds of thousands of dollars paying other people to build what he could have built himself. Because he was scared to learn. That fear cost him years. And hundreds of thousands of dollars. Sound familiar? You're doing the same thing right now. Not with developers. But with your time. You're spending thousands of hours trading manually because you're scared to learn the thing that would make trading automatic. The fear of learning to code is costing you more than any bad trade ever did. Because every month you trade manually is a month of emotional decisions, overleveraged entries, and unnecessary losses that a bot would never make. And here's the thing that should really frustrate you: AI does the hard parts now. You don't need a computer science degree. You don't need to work at a hedge fund. You don't need to be "good at math." Claude Code writes the code for you. You just need to think clearly about trading ideas. That's it. If you can describe a strategy in English, Claude can build it in Python. "I don't know how to code" stopped being a valid excuse in 2024. It's 2026. You're 2 years late on that excuse. Find a new one. Or stop making excuses entirely. Save this post. Follow Himanshu Kumar because I'm showing you how people with zero coding experience are building profitable bots. ↓ The process that actually makes money. Three letters. R. B. I. Research. Backtest. Implement. That's it. That's the entire process. Every single day. Research: Find an idea. A pattern. A market inefficiency. Don't trade it yet. Don't even think about trading it yet. Just research it. Backtest: Test the idea against historical data. Does it work? Not "does it look good on one chart." Does it work across thousands of trades? Across different market conditions? Across in-sample AND out-of-sample data? If no, kill it. Find another idea. If yes, move to step 3. Implement: Build the bot. Deploy it. Paper trade first. Then live with small size. Scale only on evidence. Research. Backtest. Implement. Every day. No exceptions. You know what your current process is? Feel. Enter. Pray. F. E. P. Feel bullish. Enter a trade. Pray it works. That's not a process. That's gambling with a TradingView subscription. RBI is the only process that works. Save this post. Tattoo it on your forearm. Follow Himanshu Kumar for daily RBI breakdowns. ↓ What Claude Code actually does that your manual process can't. You can maybe test 3-5 strategy ideas per week. Manually adjusting parameters. Manually checking results. Manually writing code (badly). Claude Code tests 50-100 ideas per week. With parallel agents running simultaneously. Multiple strategies being built, tested, and validated at the same time. While you sleep. The guy in this video spends 4-8 hours a day building systems with Claude Code. Not trading. Building. Research. Backtest. Implement. Then iterate. Improve. Optimize. Every day the systems get better. Every day the edge compounds. Every day the bots get smarter. While you? You spend 4-8 hours a day staring at charts making the same mistakes you made last month. Same indicators. Same patterns. Same entries. Same losses. He's iterating forward. You're running in circles. Same 8 hours per day. Completely different outcomes. Because he's building systems. And you're feeding a casino. Stop feeding the casino. Start building the machine. Save this post and follow Himanshu Kumar for the Claude Code workflow that iterates strategies while you sleep. ↓ Jim Simons. That's the benchmark. You probably don't know who Jim Simons is. And that tells me everything about how seriously you take trading. Jim Simons. Mathematician. Founded Renaissance Technologies. Built a net worth of $31 billion. 100% from algorithmic trading. Not one single manual trade. Not one "gut feeling" entry. Not one RSI divergence. Not one "smart money concept." Algorithms. Bots. Systems. Data. $31 billion. His fund averaged 66% annual returns for over 30 years. While you're excited about making $200 on a trade that you'll give back tomorrow. The best trader in human history never placed a manual trade in his life. And you think your edge is staring at a 5-minute chart with bloodshot eyes at 2 AM? Your edge is building the system. Not being inside it. Jim Simons is the benchmark. Everything else is noise. Save this post. Follow Himanshu Kumar because I'm building toward the same goal and showing every step publicly. ↓ What you need to understand about patience. This is not get-rich-overnight. The guy in this video says it directly: "This channel is not for people looking to get rich overnight. It's not plug and play. There are no shortcuts. If you're impatient, this probably isn't for you." And that's exactly why most people will fail at this. Because you want results now. Today. This trade. You don't want to spend a week building a bot. You don't want to paper trade for 2 weeks. You don't want to test 50 ideas to find 1 that works. You want to copy someone's bot, run it live with your rent money, and be rich by Friday. That's why you'll be broke by Friday. The guy making $2.3M spent months iterating. Testing. Failing. Rebuilding. Testing again. He was patient when you would have quit. He was calm when you would have panicked. He was consistent when you would have given up. Patience isn't just a virtue in trading. It's the only virtue. Without it, everything else fails. Impatience is the most expensive personality trait in trading. Save this post. Follow Himanshu Kumar and learn to build systems with the patience that actually pays. ↓ The live streams where the real learning happens. The YouTube video is the trailer. The live streams are the movie. Real-time bot building. Real-time questions answered. Real code shown. Real mistakes made and fixed. Not polished highlight reels where everything works perfectly. Actual development. Where things break. Where strategies fail. Where code doesn't compile. Where the fix takes 2 hours. Because that's what real development looks like. And seeing the messy parts is more valuable than any polished tutorial. Because when your bot breaks at 3 AM, you need to know how to fix it. Not just how to celebrate when it works. The streams mix beginner and advanced. Start with how to automate trading. How to use AI for code generation. Then dive into the daily work. Claude Code. Parallel agents. Constant iteration. Live debugging. 4-8 hours of real algorithmic trading development. Live. Uncut. No filter. Most "trading education" shows you the wins. This shows you the work. Save this post. Follow Himanshu Kumar for the stream schedules and breakdowns. ↓ The belief that changes everything. Code is the greatest equalizer. Not money. Not connections. Not a degree. Not where you grew up. Not what school you went to. Code. Once you can build systems, you can build anything. For the rest of your life. A trading bot today. A SaaS product tomorrow. An automation business next month. A completely different life next year. The skill isn't "algorithmic trading." The skill is building systems. And that skill transfers to everything. The guy who can build a trading bot can also build a lead gen tool. Can also build a content pipeline. Can also build a SaaS product. Can also build literally anything that runs on logic and code. One skill. Infinite applications. And AI makes learning it 100x easier than it was 5 years ago. You don't need to be smart. You don't need talent. You need Claude Code and the willingness to sit down and build something instead of consuming content about building something. Building is the skill. Everything else is entertainment disguised as education. Save this post. Follow Himanshu Kumar because I'm showing you how to build, not just how to watch. ↓ If any of this applies to you, pay attention. If you've lost money from overtrading. If you've been liquidated. If you know trading is the vehicle but manual execution keeps crashing you. If you've tried "being more disciplined" and it never lasted more than a week. If you keep saying "next month I'll start automating." If you've spent more money on courses than you've made from trading. There is a better way. It's not a magic indicator. It's not a signal group. It's not a $997 mentorship from a guy who makes money teaching, not trading. It's building your own system. A system that trades without emotion. A system that follows rules without exception. A system that runs while you sleep. A system that compounds while you live your life. That's the answer. It's always been the answer. You've just been too scared to accept that the solution requires building something instead of buying something. ↓ What the next 30 days look like if you actually commit. Week 1: Watch the video. Learn Claude Code basics. Build your first simple strategy. Run your first backtest. Week 2: Iterate. Let Claude improve the strategy. Run Monte Carlo validation. Paper trade. Week 3: Go live with $50-100. Tiny positions. Watch every trade. Compare to paper results. Week 4: Scale based on evidence. Not based on excitement. Not based on one good day. Based on data. 30 days from now you either have a running bot that trades without your emotions destroying every position. Or you're exactly where you are right now. Reading another post. Making another promise. Breaking it by Tuesday. Same 30 days either way. Different actions. Different results. Different life. ↓ Full video tutorial attached. Live bot building with Claude Code. From zero to running Polymarket trading bot. Every line of code. Every decision explained. The video is free. Claude Code is available now. The market is open 24/7. The only thing standing between you and a profitable trading bot is the same thing that's been standing there for months. You. Get out of your own way. Follow Himanshu Kumar for daily AI trading bot breakdowns, live build sessions, and the full RBI process. Save this post. Watch the video. Build the bot. Or keep trading manually and keep losing. The choice has never been easier. And you've never been more stubborn about making the wrong one.

Himanshu Kumar

38,373 次观看 • 6 个月前

Found a free GitHub repo that screens every PumpFun launch with 4 Grok agents before you risk a cent. It's called PumpGuard Bot. Inside: → Researcher, Auditor, Narrative, Timing - 4 Grok agents vote, a 5th Checker runs the final adversarial pass → Wash-trading detection, meme attention score, launch-timing odds, synthesized into one verdict → Reputation book blocks creators after a rug, forgets them after N days → Real stop-loss on actual bonding-curve/DEX prices, not paper numbers → Multi-chain: Solana PumpFun, Base Clanker, Robinhood Chain hoodfun Setup: → Clone the repo, add your GROK_API_KEY → Deploy on Railway/Render in one click, or run it on your own VPS → Button-only Telegram UI, RU/EN → Read-only dashboard and backtest report included Honest flag: it never places a trade. No wallet, no signing, no on-chain execution, dry-run only by design. Bookmark this before you ape into another PumpFun launch blind.
1:08

Found a free GitHub repo that screens every PumpFun launch with 4 Grok agents before you risk a cent. It's called PumpGuard Bot. Inside: → Researcher, Auditor, Narrative, Timing - 4 Grok agents vote, a 5th Checker runs the final adversarial pass → Wash-trading detection, meme attention score, launch-timing odds, synthesized into one verdict → Reputation book blocks creators after a rug, forgets them after N days → Real stop-loss on actual bonding-curve/DEX prices, not paper numbers → Multi-chain: Solana PumpFun, Base Clanker, Robinhood Chain hoodfun Setup: → Clone the repo, add your GROK_API_KEY → Deploy on Railway/Render in one click, or run it on your own VPS → Button-only Telegram UI, RU/EN → Read-only dashboard and backtest report included Honest flag: it never places a trade. No wallet, no signing, no on-chain execution, dry-run only by design. Bookmark this before you ape into another PumpFun launch blind.

Ridark

19,636 次观看 • 1 个月前

What It's Like Building a Proprietary Power Trading Firm with Cory Paddock Cory Paddock (Cory Paddock) has been trading the physical power grid since the early 2000s. He built his own firm from scratch in 2014 — trading his own capital — and has navigated every major paradigm shift in U.S. energy markets since coal dominated the grid. "It's the perfect amount of darkness. The information is public — but it's in the dark." We cover: - How Cory built a point of view on paradigm shifts before the market caught up - Why power trading sits at the intersection of economics & physics — and why that matters for edge - Why backtesting more than a few years of power data is basically useless — the grid isn't the same grid - LMP & locational marginal pricing: how physical grid constraints turn into alpha if you know where to look - The 5–10 trades that make your year — and why forcing setups in the lean periods kills you - How GBE stripped pay uncertainty out of trader comp so people can just focus on trading well - What it actually feels like watching your own money swing in real-time — and when it stops feeling that way Thanks so much Cory for coming on Odds on Open! Timestamps: 00:00 Intro 01:09 Starting GBE and finding trading edge 01:38 Overview of electricity markets and pricing 02:05 Power trading structure and deregulated markets 04:11 Research pipeline for market data analysis 04:56 Domain knowledge and renewable energy trading 06:39 LLMs and AI tools for quants 07:49 Grid data and intraday market signals 09:25 Finding alpha in electricity markets 10:13 Paradigm shifts and regime change insights 13:19 Coal to gas, wind, and solar trends 14:39 Data centers, EVs, and load growth 16:47 Recruiting talent in energy trading firms 17:38 Gen Z quants and algorithmic trading skills 21:55 Outliers, agency, and Gen Z traders 24:11 Culture and innovation in quant finance 27:29 Trading personal capital and risk management 28:06 PJM West Hub and market dynamics 32:19 Five-minute tick data and volatility 34:49 High-conviction trades and alpha generation 35:35 Incentive alignment and trader performance 36:42 Pay structure and removing stress capital 39:22 Motivation and purpose in trading careers 40:00 Passing knowledge to the next generation 42:04 Host reflections on electricity trading 42:14 Closing thoughts and sign-off
42:18

What It's Like Building a Proprietary Power Trading Firm with Cory Paddock Cory Paddock (Cory Paddock) has been trading the physical power grid since the early 2000s. He built his own firm from scratch in 2014 — trading his own capital — and has navigated every major paradigm shift in U.S. energy markets since coal dominated the grid. "It's the perfect amount of darkness. The information is public — but it's in the dark." We cover: - How Cory built a point of view on paradigm shifts before the market caught up - Why power trading sits at the intersection of economics & physics — and why that matters for edge - Why backtesting more than a few years of power data is basically useless — the grid isn't the same grid - LMP & locational marginal pricing: how physical grid constraints turn into alpha if you know where to look - The 5–10 trades that make your year — and why forcing setups in the lean periods kills you - How GBE stripped pay uncertainty out of trader comp so people can just focus on trading well - What it actually feels like watching your own money swing in real-time — and when it stops feeling that way Thanks so much Cory for coming on Odds on Open! Timestamps: 00:00 Intro 01:09 Starting GBE and finding trading edge 01:38 Overview of electricity markets and pricing 02:05 Power trading structure and deregulated markets 04:11 Research pipeline for market data analysis 04:56 Domain knowledge and renewable energy trading 06:39 LLMs and AI tools for quants 07:49 Grid data and intraday market signals 09:25 Finding alpha in electricity markets 10:13 Paradigm shifts and regime change insights 13:19 Coal to gas, wind, and solar trends 14:39 Data centers, EVs, and load growth 16:47 Recruiting talent in energy trading firms 17:38 Gen Z quants and algorithmic trading skills 21:55 Outliers, agency, and Gen Z traders 24:11 Culture and innovation in quant finance 27:29 Trading personal capital and risk management 28:06 PJM West Hub and market dynamics 32:19 Five-minute tick data and volatility 34:49 High-conviction trades and alpha generation 35:35 Incentive alignment and trader performance 36:42 Pay structure and removing stress capital 39:22 Motivation and purpose in trading careers 40:00 Passing knowledge to the next generation 42:04 Host reflections on electricity trading 42:14 Closing thoughts and sign-off

Ethan Kho

17,408 次观看 • 8 个月前

Most traders spend thousands of dollars on tools. Meanwhile, free GitHub repos can replace almost everything - at zero cost. Bookmark this, so you don't lose it. 1. FinceptTerminal (+10.7K ★) • A real Bloomberg Terminal alternative - built in C++20 + Qt6. • 37 AI agents modeled after Buffett, Munger, and Graham. 🔗 2. TradingAgents (+1.5K ★) • Multi-agent trading system (UCLA/MIT research). • Fundamental + sentiment + technical + risk agents • Works with Claude, GPT, Gemini, Grok 🔗 3. last30days-skill (+1.4K ★) • AI agent skill for recent signal (last 30 days) across Reddit, X, YouTube, HN, Polymarket. 🔗 4. daily_stock_analysis (+31K ★) • LLM-powered stock analysis engine. • US + A-share + H-share markets • Daily dashboards with entry/exit levels • Auto delivery via Telegram, Discord, Email 🔗 5. QuantDinger (+919 ★) • Self-hosted AI quant OS. • Strategy generation + backtesting + live trading • Crypto, stocks (IBKR), forex (MT5) 🔗 6. HKUDS/Vibe-Trading (+611 ★) • Natural language → strategy → backtest → execution. • 70+ finance skills • Export to TradingView / MT5 🔗 7. freqtrade (+467 ★) • Open-source crypto trading bot. • Multi-exchange support • Backtesting + optimization • Telegram control 🔗 8. OpenBB (+447 ★) • Open-source Bloomberg Terminal alternative. • Stocks, crypto, options, macro • AI-native integrations (MCP) 🔗 9. 500 AI Agents Projects (+386 ★) • Curated collection of real-world AI agent use cases (including finance). 🔗 10. AlphaCartel Discord (+1280 ★) • 100% free community for AI traders:
0:37

Most traders spend thousands of dollars on tools. Meanwhile, free GitHub repos can replace almost everything - at zero cost. Bookmark this, so you don't lose it. 1. FinceptTerminal (+10.7K ★) • A real Bloomberg Terminal alternative - built in C++20 + Qt6. • 37 AI agents modeled after Buffett, Munger, and Graham. 🔗 2. TradingAgents (+1.5K ★) • Multi-agent trading system (UCLA/MIT research). • Fundamental + sentiment + technical + risk agents • Works with Claude, GPT, Gemini, Grok 🔗 3. last30days-skill (+1.4K ★) • AI agent skill for recent signal (last 30 days) across Reddit, X, YouTube, HN, Polymarket. 🔗 4. daily_stock_analysis (+31K ★) • LLM-powered stock analysis engine. • US + A-share + H-share markets • Daily dashboards with entry/exit levels • Auto delivery via Telegram, Discord, Email 🔗 5. QuantDinger (+919 ★) • Self-hosted AI quant OS. • Strategy generation + backtesting + live trading • Crypto, stocks (IBKR), forex (MT5) 🔗 6. HKUDS/Vibe-Trading (+611 ★) • Natural language → strategy → backtest → execution. • 70+ finance skills • Export to TradingView / MT5 🔗 7. freqtrade (+467 ★) • Open-source crypto trading bot. • Multi-exchange support • Backtesting + optimization • Telegram control 🔗 8. OpenBB (+447 ★) • Open-source Bloomberg Terminal alternative. • Stocks, crypto, options, macro • AI-native integrations (MCP) 🔗 9. 500 AI Agents Projects (+386 ★) • Curated collection of real-world AI agent use cases (including finance). 🔗 10. AlphaCartel Discord (+1280 ★) • 100% free community for AI traders:

AlphaCartel

30,801 次观看 • 5 个月前

honestly this should be f*cking illegal your LLM takes 30 seconds to think about a trade, but candle closed 29 seconds ago. Jev answers in 70 to 500 milliseconds. it can't write a word. it returns BUY / SELL / HOLD and a probability, and that's all a scalp needs. 3 repos already built on it: -> jev-trader: one AI trade decision every single block, on a live MON-USDC market -> QuantDinger: a full open-source AI trading OS with Jev built in. research, strategy, agent trading -> AIStock: "one person can become their own super-analyst" $0.042 per million input tokens. output is free. that's thousands of decisions for the price of one LLM call. the catch: fast is not the same as right. a wrong call in 200 milliseconds is still a wrong call. paper trade it first. > let the big model do the research. > let Jev pull the trigger. > let code set the stop. bookmark this before your bot is the slow one in the room.
0:25

honestly this should be f*cking illegal your LLM takes 30 seconds to think about a trade, but candle closed 29 seconds ago. Jev answers in 70 to 500 milliseconds. it can't write a word. it returns BUY / SELL / HOLD and a probability, and that's all a scalp needs. 3 repos already built on it: -> jev-trader: one AI trade decision every single block, on a live MON-USDC market -> QuantDinger: a full open-source AI trading OS with Jev built in. research, strategy, agent trading -> AIStock: "one person can become their own super-analyst" $0.042 per million input tokens. output is free. that's thousands of decisions for the price of one LLM call. the catch: fast is not the same as right. a wrong call in 200 milliseconds is still a wrong call. paper trade it first. > let the big model do the research. > let Jev pull the trigger. > let code set the stop. bookmark this before your bot is the slow one in the room.

kiosa

97,090 次观看 • 8 天前