#backtesting

How to Use CHATGPT to Backtest Any Trading Strategy in less than 3:00 Minutes. Watch & Try it out!

The first tool of its kind in the ICT space. Your first backtest now free :) Add this to your weekend plans, you’ll be surprised how sick this really is.

How to Use CHATGPT to Backtest Any Trading Strategy in less than 3 Minutes. Watch & Try it out! Reshare this, you might be helping someone.

Last month was pretty interesting, with some really good updates on StockMock 🚀 Quick roundup 🧵 1. 📈 Stocks are here in Simulator Stock F&O is now on StockMock. Go beyond the indices and test your strategies on individual stocks too. 2. 🛢️ Crude Oil (MCX) in Backtest & Simulator Backtest crude intraday (09:00–23:30), data from Jan 2023 3. 🧭 Smart Journey in Backtest Leg journeys got smarter: • Wait before the next leg enters • Repeat a branch ×N • A separate "SL on Profit" branch Build adjustment flows the way you actually trade them. 4.💰 Results after brokerage & taxes in Backtest Set brokerage per order or per lot (+18% GST), and turn on Taxes & Charges for STT/CTT, exchange charges, SEBI fees and stamp duty, all at today's rates. You now see Gross Profit, Brokerage and Taxes separately. 5. 🎯 Multiple of 100/500 strikes in backtest 6. 🛡️ Associated hedge at "Sold Strike ± points" Your hedge follows your sold leg, including on re-entries and journeys. 7. ⚡ Plus faster, steadier backtests and lots of smaller fixes. More coming next month 👀 Tell me what you want next 👇

BOT DE TRADING CON CLAUDE EN 17 MINUTOS Al terminar de ver este tutorial vas a saber: Construir tu bot desde cero. Hacer backtest de cualquier estrategia. Ejecutarlo de forma automática. 🔖 Guárdalo.

Backtest of the Day #10 - $QQQ Iron Butterfly - $157,968 Profit - 61% Win Rate - 1.72 Sharpe Ratio - $233.34 EV per trade Powered via @optionbots

Your $10K/month goal could be making you a WORSE trader. I learned this the hard way. When I started trading, I wanted to become a millionaire in 3 months. So I rushed the process. And here's what happens when you chase the payout instead of mastery: - You increase lot sizes to hit targets faster. - You overleverage because small gains feel too slow. - You skip backtesting to start making money sooner. You're so focused on making $10K/month that you neglect the work required to become profitable. Here's what you should focus on instead: 1. Backtest your strategy and gather data. 2. Journal your trades and review your mistakes. 3. Follow your trading plan and manage risk. 4. Get 1% better every day. You can't control your next trade's outcome. But you can control how well you execute your process.

Your trading account will forgive you for: • Taking longer to get funded • Losing a trade you were confident in • Closing the charts without making money Your trading account will NOT forgive you for these 5 MISTAKES: 🧵

🚨 these 3 github repos are the most POWERFUL trading combo i've ever seen Hedge funds pay analysts, traders and risk teams millions a year. These 3 repos turn that whole team into AI agents you can run for free It's TradingAgents, ai-hedge-fund and freqtrade, with 238k+ stars combined. First, get how it works: 1. TradingAgents is a multi-agent framework that mirrors the dynamics of real-world trading firms: fundamental, sentiment, news and technical analysts write reports, bull and bear researchers debate them, a trader decides and a risk team signs off. 2. ai-hedge-fund is a proof of concept for an AI-powered hedge fund: agents modeled on investors like Warren Buffett, Charlie Munger, Michael Burry and Cathie Wood each give a signal, then a risk manager and a portfolio manager make the call. 3. freqtrade is the free, open source trading bot that turns a signal into a strategy, with backtesting, hyperopt and a dry-run mode that trades on paper first. Here's how you can run it for free yourself: 1. Open Claude Code and clone the 3 repos: TradingAgents, ai-hedge-fund and freqtrade 2. Run TradingAgents' CLI with python -m cli.main and pick a ticker 3. Watch the analyst team write its reports, the bull and bear researchers argue, and the trader and risk team land on BUY, SELL or HOLD 4. Install ai-hedge-fund with pipx install aihf and run it on the same tickers. Every investor agent returns a signal and a confidence 5. Have Claude merge both: only act when TradingAgents and the investor agents agree 6. Have Claude turn that merged signal into a freqtrade strategy 7. Backtest it in freqtrade on 2 years of data, fees included 8. Tune it with freqtrade hyperopt, then re-test on data it never saw so you don't fool yourself with overfitting 9. Run it in dry-run mode for 2 weeks. If it doesn't beat buy-and-hold after fees, it's not an edge yet i won't say anything more:

these 10 GitHub repos are the most DANGEROUS thing in trading right now together they give you a full AI hedge fund: a research desk, an investment committee and an execution desk in this article I've shown EXACTLY how to build it from scratch

STUDY AND BACKTEST THIS FOR THE WEEKEND ✍🏼📈 When price sweeps a structural high or low, pay attention to the POI formed afterward. These POIs have a higher probability of holding because the liquidity sweep can influence the next move in price. Study the schematics below, understand the logic behind them, and backtest the concept before applying it to your trading. Structure > Liquidity. 📈♟️

this is the most dangerous thing on the internet today. someone built an ai model that reads candlestick charts and predicts future market movements. and they made it 100% free. meet kronos. → pre-trained on 12 billion financial candles → predicts future candles using historical market data → generates 100 possible market scenarios → works with binance market data → connects with claude code and tradingview → helps build ai powered trading systems feed it 512 historical candles and ask it to forecast the next 24. imagine combining this with an ai agent to analyze markets, backtest strategies and monitor trading setups. ai-powered trading is getting interesting. article below

The complete roadmap to becoming a quant: the foundation every firm builds on, the engineering edge and the one thing that separates the best.

Moving average crossover strategies are often considered "lagging", and too basic to have a real edge. The backtest results beg to differ. Here are a handful of strategies that BEAT buy & hold on well-known tickers, using nothing but moving average crossovers.

Backtest price action reversals, continuity, or buying the bottom of ranges and the results will tell you it doesn’t work. Your backtest isn’t the problem. How you are thinking is. As traders we must gather evidence, not historical outcomes. Historical probability has no bearing on future price. The statistically likely outcome is not always the probable one. “Oil can’t go to −$10. It’s never done that.” Oil below the Y Q M W opens ❓ Every group we track is selling futures at the bid. If that continues, it goes well past −$10 🤣 It settled at −$37.63. If everyone is selling it's going down. If everyone keeps selling it'll keep going down. That is what TheStrat lets you actually identify. Not historical probability, realtime evidence of algorithms & participation.

I have spent over 15,000 hours looking at Futures/Stock/Crypto charts in the past 6 years. If I could only say one thing to myself 6 years ago: Focus on what you can know to be true. What that means is...

this repository is f*cking BROKEN goldman sachs pays each quant up to $300k a year, all thanks to this repo It's called gs-quant, developed and maintained by Goldman Sachs' own quant team, and it already has 13.5k stars First, get how it works: 1. Goldman Sachs' quants built this toolkit on 25 years of their own risk experience: pricing, risk, backtesting, swaps, swaptions, FX options, timeseries - all in there. It even ships a skill that teaches Claude Code how to use it. 2. Every time Claude writes gs-quant code, it runs Goldman's risk math right in your notebook: volatility, drawdown, z-score, beta, RSI. 3. Pair it with Machine Learning for Trading (21.6k stars) - 27 chapters that take you from data sourcing to live execution. Together it's the exact stack a quant interview tests. Here's how you can run it for free yourself: 1. Open Claude Code, install Goldman Sachs' library: pip install gs-quant 2. Add the skill: python -m gs_quant.skills install --global 3. Have Claude pull 5-year daily prices for GS and SPY with free yfinance data 4. Run Goldman Sachs' risk math on it: volatility, max drawdown, z-score, Bollinger Bands, RSI 5. Check 60-day beta and correlation to SPY. Is it the stock moving, or the whole market? 6. Mark every day the z-score drops below -2. That means the price is 2 standard deviations below normal 7. Backtest a rule: buy below -2, sell when back at 0, including fees. Claude writes the code and runs it 8. Walk-forward it the ML4T way. If it doesn't beat buy-and-hold after fees, it's not an edge i won't say anything more:

🚨these github repos is SERIOUSLY DANGEROUS Wall Street pays its engineers $500K+ a year to write code like this, 6 of the top firms put theirs on GitHub 6 repos built inside Goldman Sachs, JPMorgan, Jane Street, Jump, Morgan Stanley and Man Group, with 38.6k stars combined First, get how it works: 1. Goldman Sachs' quants built gs-quant for pricing, risk and backtesting. JPMorgan's python-training is the exact course it uses to train its own traders and analysts. 2. Jane Street's magic-trace traces every function call at ~40ns. Morgan Stanley's Xpedite profiles low-latency trading code, "where latency directly translates to profitability". Jump's Firedancer runs Solana on a design taken from low-latency trading. 3. Man Group's ArcticDB stores and queries market data across billions of rows. Together it's a full desk: data, research, risk and speed. Here's how you can run it for free yourself: 1. Open Claude Code and start with JPMorgan's trader course, python-training. Have Claude run the notebooks with you 2. Install Goldman Sachs' library: pip install gs-quant 3. Add the skill that teaches Claude Code to use it: python -m gs_quant.skills install --global 4. Have Claude pull 5 years of daily prices for GS, JPM and MS with free yfinance data 5. Store it in Man Group's database: pip install arcticdb 6. Run Goldman's risk math on it: volatility, max drawdown, z-score, beta to SPY. Then backtest one rule with fees. Claude writes the code and runs it 7. On Linux, trace your backtest with Jane Street's magic-trace and see where every microsecond goes 8. Moving to C++? Measure every critical path the Morgan Stanley way with Xpedite 9. Have Claude walk you through how Jump built Firedancer's pipeline (net, verify, dedup, pack, bank), then apply the same ideas to your bot i won't say anything more:

i still don’t understand why everyone is NOT using this 100% FREE AI trading desk it does what a $750K/year quant firm research team does, for FREE you describe ANY trading idea in plain English & @quantpilotapp backtests it on TradingView charts with real market data & turns it into a TRADING BOT (in minutes) that trades live on Hyperliquid 24/7 i just built a REGIME trading bot that switches its strategy on its own when the market changes (incredible results) here’s exactly how it works: 1. most bots run ONE strategy in every market & that’s why they bleed, trend bots get chopped in sideways markets & range bots get destroyed in strong trends 2. so my bot reads the market’s MOOD on every candle using ADX for trend strength, ATR for volatility & the 200 EMA for direction 3. when ADX is above 25 it switches to TREND mode & rides breakouts of the 20-candle high or low in the direction of the 200 EMA with a 2x ATR trailing stop 4. when ADX drops below 20 it switches to RANGE mode & buys the lower Bollinger Band when RSI is under 30, taking profit at the middle band 5. when ATR spikes into its top 10% of the last 90 days it goes into PANIC mode, opens zero new trades & cuts every open position in half 6. between ADX 20 & 25 it holds its current mode, so it never flips back & forth on market noise, the detail most bots miss 7. it risks only 1% per trade & sizes every position by ATR, so it trades smaller automatically when the market gets wild 8. if one mode loses 3 trades in a row it pauses that mode for 24 hours, because a losing streak usually means the regime is changing 9. i built the entire thing inside QuantPilot, it ran a complete end-to-end backtest on 1-minute candles with real Hyperliquid order book data 10. then it suggested optimizations, i ran it in paper trading mode first & flipped it to live execution on Hyperliquid once it proved itself if you want to build a complete AI trading setup from scratch, read the article below:

This $BTC pullback is just a BULLISH BACKTEST. My first upside target remains $93K. Nothing changed.

Why forward testing is far better than back testing and How forward testing can help you become consistently profitable as a forex trader 3X faster. #Backtesting #forextrading #profitabletrader

A female trader came around so we can backtest together Guess the female trader and win $30 You can tag her too 🙂💙

7 small steps to start with algorithmic trading: 1. Start with Python 2. Learn to use VSCode 3. Take a pandas tutorial 4. Then a plotly tutorial 5. Make a portfolio with riskfolio 6. Make a backtest with vectorbt 7. Analyze performance with vectorbt You can do this! Want to learn how? Register here: