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The most expensive engineering teams released their trading tools on GitHub Jane Street, Goldman Sachs, JP Morgan, BlackRock, Two Sigma, Hudson River Trading, D.E. Shaw 1. Jane Street - magic-trace > CPU instruction tracer. > When your profiler is blind, this sees everything. 2. Goldman Sachs - gs-quant >... The derivative pricing library GS traders use at their desks. > MIT licensed. 3. JP Morgan - perspective > What JPM traders use to watch markets in real time. > A $24k/year terminal. 4. BlackRock - lcso > Rust portfolio optimizer. > Where scipy gives up, this works. 5. Hudson River Trading - corral > Structured concurrency for C++20. > The foundation of HFT infrastructure at one of the largest trading firms. 6. Two Sigma - flint > Time-series joins on Apache Spark with temporal tolerance. > Built for billions of ticks. 7. D.E. Shaw - pyflyby > Auto-import for IPython and Jupyter. D.E. > Shaw funded the development of IPython itself. These are the tools running under the signal combination engine I built in the article below. Bookmark both.show more

Atlas

89,862 subscribers

26,293 views • 4 months ago •via X (Twitter)

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6 Comments

Weq4 months ago

gs-quant being MIT licensed still feels illegal somehow

Lunar4 months ago

ty for best repos

leakgambler4 months ago

flint doing time-series joins with temporal tolerance on spark is solving a specific problem that anyone who has tried to join tick data with event data at scale has hit badly

Ruuj4 months ago

Great article man! Thanks for sharing it

leanxbt4 months ago

gs-quant being MIT licensed is the one that consistently surprises people, goldman releasing their actual derivative pricing library publicly is a real thing worth sitting with

hanayuki4 months ago

さて、これらのツールを使ってみたくはありませんか？楽しそうですね！

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Jane Street, Goldman Sachs, JP Morgan, BlackRock, Hudson River Trading, Two Sigma, D.E. Shaw. The most expensive engineering teams in the world released their financial tools on GitHub. Here are 7 repos, one from each. 1. Jane Street, janestreet/magic-trace 5.3k stars. Process tracer powered by Intel PT. When your profiler is blind, magic-trace sees every CPU instruction. 2. Goldman Sachs, goldmansachs/gs-quant Derivative pricing the GS traders use at their desks. MIT licensed. 3. JP Morgan, finos/perspective What JPM traders use to watch markets in real time. A $24k/year terminal, for free. 4. BlackRock, blackrock/lcso Rust optimizer for portfolio problems. Where scipy gives up, this works. 5. Hudson River Trading, hudson-trading/corral Structured concurrency for C++20. The foundation of HFT infrastructure at one of the largest U.S. trading firms. 6. Two Sigma, twosigma/flint Time-series joins on Apache Spark with temporal tolerance. Built for billions of ticks. 7. D.E. Shaw, deshaw/pyflyby Auto-import for IPython and Jupyter. D.E. Shaw also funded the development of IPython itself. Bookmarked it

Jane Street, Goldman Sachs, JP Morgan, BlackRock, Hudson River Trading, Two Sigma, D.E. Shaw. The most expensive engineering teams in the world released their financial tools on GitHub. Here are 7 repos, one from each. 1. Jane Street, janestreet/magic-trace 5.3k stars. Process tracer powered by Intel PT. When your profiler is blind, magic-trace sees every CPU instruction. 2. Goldman Sachs, goldmansachs/gs-quant Derivative pricing the GS traders use at their desks. MIT licensed. 3. JP Morgan, finos/perspective What JPM traders use to watch markets in real time. A $24k/year terminal, for free. 4. BlackRock, blackrock/lcso Rust optimizer for portfolio problems. Where scipy gives up, this works. 5. Hudson River Trading, hudson-trading/corral Structured concurrency for C++20. The foundation of HFT infrastructure at one of the largest U.S. trading firms. 6. Two Sigma, twosigma/flint Time-series joins on Apache Spark with temporal tolerance. Built for billions of ticks. 7. D.E. Shaw, deshaw/pyflyby Auto-import for IPython and Jupyter. D.E. Shaw also funded the development of IPython itself. Bookmarked it

zostaff

293,642 views • 4 months ago

THE MOST EXPENSIVE ENGINEERING TEAMS ON EARTH JUST PUT THEIR FINANCIAL TOOLS ON GITHUB FOR FREE. Jane Street. Goldman Sachs. JP Morgan. BlackRock. Hudson River Trading. Two Sigma. D.E. Shaw. Seven firms. Seven repos. Billions in engineering talent open sourced. Save this before you scroll past it. 1. Jane Street — magic-trace 5,300 stars. Process tracer powered by Intel PT. When your profiler is blind this sees every CPU instruction. 2. Goldman Sachs — gs-quant Derivative pricing the GS traders use at their actual desks. MIT licensed. Free. 3. JP Morgan — perspective What JPMorgan traders use to watch markets in real time. A $24,000 per year terminal. Available to anyone with a GitHub account. 4. BlackRock — lcso Rust optimizer for portfolio problems. Where scipy gives up this works. Built for problems that break standard optimization libraries. 5. Hudson River Trading — corral Structured concurrency for C++20. The foundation of HFT infrastructure at one of the largest US trading firms. 6. Two Sigma — flint Time-series joins on Apache Spark with temporal tolerance. Built for billions of ticks. The data infrastructure layer behind systematic trading at scale. 7. D.E. Shaw — pyflyby Auto-import for IPython and Jupyter. D.E. Shaw also funded the development of IPython itself. The firm that built the tool is now giving you the enhancement for free. Here is what this list actually represents. These seven firms collectively employ thousands of engineers earning $300,000 to $1,000,000 per year. The tools they built to solve their hardest problems are the same tools you now have access to for free. The information asymmetry that used to separate a quant at Goldman from a developer at home just narrowed significantly. The infrastructure is free. The edge now belongs to whoever knows how to use it. Bookmark this before you pay for another financial data tool. Follow CyrilXBT for every elite engineering resource the moment it surfaces.

THE MOST EXPENSIVE ENGINEERING TEAMS ON EARTH JUST PUT THEIR FINANCIAL TOOLS ON GITHUB FOR FREE. Jane Street. Goldman Sachs. JP Morgan. BlackRock. Hudson River Trading. Two Sigma. D.E. Shaw. Seven firms. Seven repos. Billions in engineering talent open sourced. Save this before you scroll past it. 1. Jane Street — magic-trace 5,300 stars. Process tracer powered by Intel PT. When your profiler is blind this sees every CPU instruction. 2. Goldman Sachs — gs-quant Derivative pricing the GS traders use at their actual desks. MIT licensed. Free. 3. JP Morgan — perspective What JPMorgan traders use to watch markets in real time. A $24,000 per year terminal. Available to anyone with a GitHub account. 4. BlackRock — lcso Rust optimizer for portfolio problems. Where scipy gives up this works. Built for problems that break standard optimization libraries. 5. Hudson River Trading — corral Structured concurrency for C++20. The foundation of HFT infrastructure at one of the largest US trading firms. 6. Two Sigma — flint Time-series joins on Apache Spark with temporal tolerance. Built for billions of ticks. The data infrastructure layer behind systematic trading at scale. 7. D.E. Shaw — pyflyby Auto-import for IPython and Jupyter. D.E. Shaw also funded the development of IPython itself. The firm that built the tool is now giving you the enhancement for free. Here is what this list actually represents. These seven firms collectively employ thousands of engineers earning $300,000 to $1,000,000 per year. The tools they built to solve their hardest problems are the same tools you now have access to for free. The information asymmetry that used to separate a quant at Goldman from a developer at home just narrowed significantly. The infrastructure is free. The edge now belongs to whoever knows how to use it. Bookmark this before you pay for another financial data tool. Follow CyrilXBT for every elite engineering resource the moment it surfaces.

CyrilXBT

42,965 views • 4 months ago

Banks Smash Record Trading Revenues as Global Volatility Ignites Q1 2026 Bonanza Wall Street’s biggest banks delivered a trading revenue explosion in the first quarter of 2026, powered by intense market volatility that sent client activity through the roof. JPMorgan Chase $JPM led the pack with a record $11.6 billion in markets revenue, up 20% year-over-year. Fixed-income, currencies and commodities (FICC) surged 21% to $7.1 billion, while equities trading climbed 17% to $4.5 billion. The bank’s overall profit hit $16.5 billion, or $5.94 per share, crushing estimates and marking its second-best quarter ever. Not to be outdone, Goldman Sachs $GS posted record equities trading revenue of $5.33 billion, up 27%, as hedge-fund prime brokerage and derivatives desks lit up amid wild swings. The firm’s markets business contributed to a 14% jump in total revenue to $17.23 billion and a 19% rise in profit to $5.63 billion. Citigroup $C wasn’t far behind. Its markets revenue soared 19% to $7.2 billion its highest quarterly haul in over a decade. FICC revenue climbed 13% to $5.2 billion on strong commodities, credit and currency flows, while equities trading exploded 39% to a record $2.1 billion. Bank of America $BAC saw sales and trading revenue rise 13% to $6.4 billion, with equities trading jumping 30% to $2.83 billion on heightened client hedging and positioning. Morgan Stanley capped the blowout with its own record quarter: total revenue hit $20.58 billion, up 16%, driven by a record $5.15 billion in equities trading, up 25%. Analysts had called for the largest banks to deliver around $18 billion in stock-trading revenue alone for the quarter. The numbers show Wall Street is on pace to smash that target, with combined trading revenues across the majors exceeding $40 billion. The driver was unmistakable: extreme volatility from geopolitical tensions in the Middle East, energy-price swings and anxious investor flows. Traders racked up gains facilitating hedges in commodities and currencies while capitalizing on rapid moves across rates, credit and equities. Jamie Dimon himself highlighted the “increasingly complex set of global economic risks” even as his bank’s trading desks delivered the goods. Surging client activity in commodities, credit, emerging markets and energy futures turned uncertainty into revenue for the desks. Fixed-income units thrived on credit and currency flows; equities desks saw strong prime brokerage and derivatives demand. For the first time in years, every major player reported double-digit gains in sales and trading. The numbers confirm, the big banks thrive in high volatility environments served on a gold platter by President Trump. Q1 2026 will go down as one of the strongest trading quarters on record, setting a high bar for the rest of the year as volatility shows no signs of fading.

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Financelot

18,129 views • 5 months ago

hedge funds spent $2M to build what you could replicate in a weekend they just knew it was possible first - that was their only advantage a researcher at d.e. shaw handed me a paper in 2023 two years of quant labor, internal "discovery" - original source was an MIT lecture from 1998, public, free, searchable mechanism is cointegration two assets drift independently, but their spread behaves like a rubber band - stationary, mean-reverting, measurable retail asks: will this go up quant asks: is this spread 2.3 standard deviations outside its 5-year mean - and what's historical reversion rate from here completely different question. second one has an answer run it on GLD/GDX, SPY/QQQ, XLE/XOM - data goes back 20 years, free on yahoo finance spread reverts within 8 trading days 71% of the time when it crosses 2 sigma size with Kelly on that 71%, take the trade when math clears, exit at mean paper they spent two years on is engle & granger, 1987 - won a nobel prize Bookmark before it deleted it's in every econometrics textbook on earth, chapter 4 they didn't hide the math from you they just never told you it existed

hedge funds spent $2M to build what you could replicate in a weekend they just knew it was possible first - that was their only advantage a researcher at d.e. shaw handed me a paper in 2023 two years of quant labor, internal "discovery" - original source was an MIT lecture from 1998, public, free, searchable mechanism is cointegration two assets drift independently, but their spread behaves like a rubber band - stationary, mean-reverting, measurable retail asks: will this go up quant asks: is this spread 2.3 standard deviations outside its 5-year mean - and what's historical reversion rate from here completely different question. second one has an answer run it on GLD/GDX, SPY/QQQ, XLE/XOM - data goes back 20 years, free on yahoo finance spread reverts within 8 trading days 71% of the time when it crosses 2 sigma size with Kelly on that 71%, take the trade when math clears, exit at mean paper they spent two years on is engle & granger, 1987 - won a nobel prize Bookmark before it deleted it's in every econometrics textbook on earth, chapter 4 they didn't hide the math from you they just never told you it existed

Livsun

26,816 views • 3 months ago

what happens to trading if AI keeps getting better? Dwarkesh Patel asked Jane Street's head of technology the obvious question: if we get AGI, can't it just replace what you do? his answer: "trading feels to me like AGI-complete." "all of the different problems of the world end up influencing what you're doing in a trading context. trading involves figuring out what things are worth. which means making predictions about the future. and lots of different things flow into that." in other words, to fully automate trading you'd need to fully automate understanding the entire world. every geopolitical event, every supply chain shift, every behavioral pattern, every regulatory change, it all feeds into price. and even at a firm running tens of thousands of GPUs with sub-100-nanosecond execution: "many of your most profitable days happen when weird stuff happens and nobody knows what's going on. doing that well involves human judgment. we think humans work better than models through phase transitions." his conclusion: "i have never been more desperate to hire more engineers and traders than i am today. everything people are doing is more valuable than it was." so what does this mean for the rest of us? if the most automated trading firm on earth is saying humans matter more not less. the implication is that AI doesn't shrink the opportunity in trading. it raises the bar for what each person can do. the traders who learn to use these tools will pull further ahead. the ones who don't will fall further behind.

what happens to trading if AI keeps getting better? Dwarkesh Patel asked Jane Street's head of technology the obvious question: if we get AGI, can't it just replace what you do? his answer: "trading feels to me like AGI-complete." "all of the different problems of the world end up influencing what you're doing in a trading context. trading involves figuring out what things are worth. which means making predictions about the future. and lots of different things flow into that." in other words, to fully automate trading you'd need to fully automate understanding the entire world. every geopolitical event, every supply chain shift, every behavioral pattern, every regulatory change, it all feeds into price. and even at a firm running tens of thousands of GPUs with sub-100-nanosecond execution: "many of your most profitable days happen when weird stuff happens and nobody knows what's going on. doing that well involves human judgment. we think humans work better than models through phase transitions." his conclusion: "i have never been more desperate to hire more engineers and traders than i am today. everything people are doing is more valuable than it was." so what does this mean for the rest of us? if the most automated trading firm on earth is saying humans matter more not less. the implication is that AI doesn't shrink the opportunity in trading. it raises the bar for what each person can do. the traders who learn to use these tools will pull further ahead. the ones who don't will fall further behind.

Goshawk Trades

57,547 views • 4 months ago

Prop trading is a $10B+ industry in traditional finance. In crypto, it barely exists. Most firms change the rules the moment a trader starts winning. Deny payouts they can't afford. Hide their books. Propr built the whole thing onchain w/ every position, every payout, every P&L is public and auditable in real time. We sat down with Lou, founder of Propr, to break down how it works: Timestamps: 0:00 Propr Overview 1:05 Why Now Is the Right Time for Onchain Prop 2:33 Liquidity as the Breakthrough Constraint 4:01 The Black Box Problem in Prop Firms 5:40 Onchain Transparency and Verifiability 7:34 Why Propr Chose Hyperliquid 10:12 Lighter vs. Hyperliquid — Two Cohorts 14:22 Polymarket Integration and Prediction Markets 18:10 AI Agents Trading With Funded Capital 22:44 Who Are the AI Agent Traders 25:58 Propr as the Funding Layer for Other Apps 27:32 Transparency Dashboard and Where to Start

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Crypto Coin Show

18,834 views • 2 months ago

Wall Street doesn't predict prices - it prices the randomness in them. That is where the billions are, and it runs on one equation. The model trading desks actually use is Geometric Brownian Motion: dS = μS·dt + σS·dW μ (drift) is the slow expected trend. σ·dW is the random shock - a coin flip scaled by volatility. It is the engine under Black-Scholes, the formula that prices trillions in options. The idea is 125 years old. Louis Bachelier modeled prices as a random walk in 1900 - five years before Einstein used the same math for particles. His verdict: "the mathematical expectation of the speculator is zero." Sixty years later Eugene Fama won a Nobel for the same finding: prices are "no more predictable than the path of a series of cumulated random numbers." And the math is brutal. Drift grows with time (t); noise grows only with its square root (√t). Over one day the shock buries the trend - so daily direction is a coin flip, and traders invent reasons after the candle prints. The trap hides in one assumption: that returns are "normal." Real markets have fat tails. Black Monday 1987 (-22.6% in a day) was a 20-sigma event under the bell curve - impossible in billions of years. Nassim Taleb calls that bell curve "the great intellectual fraud." The equation that prices everything is blind to the one day it matters most. the edge was never the trend inside the wiggle. it is knowing how much of the wiggle is nothing. the full quant build is in the article below.

Wall Street doesn't predict prices - it prices the randomness in them. That is where the billions are, and it runs on one equation. The model trading desks actually use is Geometric Brownian Motion: dS = μS·dt + σS·dW μ (drift) is the slow expected trend. σ·dW is the random shock - a coin flip scaled by volatility. It is the engine under Black-Scholes, the formula that prices trillions in options. The idea is 125 years old. Louis Bachelier modeled prices as a random walk in 1900 - five years before Einstein used the same math for particles. His verdict: "the mathematical expectation of the speculator is zero." Sixty years later Eugene Fama won a Nobel for the same finding: prices are "no more predictable than the path of a series of cumulated random numbers." And the math is brutal. Drift grows with time (t); noise grows only with its square root (√t). Over one day the shock buries the trend - so daily direction is a coin flip, and traders invent reasons after the candle prints. The trap hides in one assumption: that returns are "normal." Real markets have fat tails. Black Monday 1987 (-22.6% in a day) was a 20-sigma event under the bell curve - impossible in billions of years. Nassim Taleb calls that bell curve "the great intellectual fraud." The equation that prices everything is blind to the one day it matters most. the edge was never the trend inside the wiggle. it is knowing how much of the wiggle is nothing. the full quant build is in the article below.

Di Krass

256,741 views • 1 month ago

POLYMARKET COPY TRADING SETUP (or how to make your bot print passively) Most traders consider mirroring as a passive income. And... They are RIGHT. But only if you set up your tools right. Copy trading bots have multiple features most traders skip. And that’s why they lose even when the trader is profitable. Here's how to set up your bot right: 1) Go to This is the fastest and the cheapest bot for both trading and copy trading on Polymarket 2) Click on Start -> Copy Trade -> Add 3) Click on Target Wallet and paste the wallet address 4) Click on Tag to name it (for example "Elections insider") 5) Click the next button to set up fixed amount or % I use a fixed amount for traders with a small trade count per day, and % for those who trade a lot and use limit orders. 6) Set slippage to 10% 7) Set up TP and SL if needed I don’t use them and fully rely on trader’s actions. 8) Skip the next four buttons and just set the Total Spend Limit. 9) Set Min and Max price if needed If you don’t want to take too much risk, set min $0.30 per share. 10) Click on Ignore Target Wallet Trades Under and set it to $5 This will help the bot avoid unnecessary trades. Now your setup is ready so click Create and watch your portfolio grow (if you picked decent wallets). I'll share BEST wallets to follow in my next post. Turn your notis on so you don't miss this.

POLYMARKET COPY TRADING SETUP (or how to make your bot print passively) Most traders consider mirroring as a passive income. And... They are RIGHT. But only if you set up your tools right. Copy trading bots have multiple features most traders skip. And that’s why they lose even when the trader is profitable. Here's how to set up your bot right: 1) Go to This is the fastest and the cheapest bot for both trading and copy trading on Polymarket 2) Click on Start -> Copy Trade -> Add 3) Click on Target Wallet and paste the wallet address 4) Click on Tag to name it (for example "Elections insider") 5) Click the next button to set up fixed amount or % I use a fixed amount for traders with a small trade count per day, and % for those who trade a lot and use limit orders. 6) Set slippage to 10% 7) Set up TP and SL if needed I don’t use them and fully rely on trader’s actions. 8) Skip the next four buttons and just set the Total Spend Limit. 9) Set Min and Max price if needed If you don’t want to take too much risk, set min $0.30 per share. 10) Click on Ignore Target Wallet Trades Under and set it to $5 This will help the bot avoid unnecessary trades. Now your setup is ready so click Create and watch your portfolio grow (if you picked decent wallets). I'll share BEST wallets to follow in my next post. Turn your notis on so you don't miss this.

Oracle Boar

442,993 views • 7 months ago

the entire math roadmap for quant trading fits on a single page and this video breaks it down in 60 seconds probability. linear algebra. calculus. statistics four subjects. every quant strategy ever built sits on top of these four probability comes first because everything else depends on it conditional probability, Bayes' theorem, expected value this is the actual language trading models are written in linear algebra is how you stop thinking about one stock and start thinking about 5,000 at once factor models, PCA, portfolio optimization Bridgewater, AQR, Two Sigma all run on matrix math calculus is derivatives pricing. Black-Scholes is a PDE if you trade options without understanding the equation underneath them, you're reading the answer key without knowing the question statistics is the filter regression, hypothesis testing, distribution fitting this is how Renaissance decides if a signal is real or if a backtest got lucky here's what nobody tells retail: every indicator you've ever used is a dumbed-down version of one of these four subjects RSI is momentum statistics. Bollinger Bands are standard deviation. MACD is signal processing they took the math, removed the part that makes it useful, and sold you the wrapper > this roadmap: free, 60 seconds, in this video > same four subjects tested at every quant interview since the 1990s > time to learn properly: 6-8 months with free resources > what they unlock: the actual language Wall Street runs on the information was never gated it was just never packaged for the people who needed it most full breakdown in the video below

the entire math roadmap for quant trading fits on a single page and this video breaks it down in 60 seconds probability. linear algebra. calculus. statistics four subjects. every quant strategy ever built sits on top of these four probability comes first because everything else depends on it conditional probability, Bayes' theorem, expected value this is the actual language trading models are written in linear algebra is how you stop thinking about one stock and start thinking about 5,000 at once factor models, PCA, portfolio optimization Bridgewater, AQR, Two Sigma all run on matrix math calculus is derivatives pricing. Black-Scholes is a PDE if you trade options without understanding the equation underneath them, you're reading the answer key without knowing the question statistics is the filter regression, hypothesis testing, distribution fitting this is how Renaissance decides if a signal is real or if a backtest got lucky here's what nobody tells retail: every indicator you've ever used is a dumbed-down version of one of these four subjects RSI is momentum statistics. Bollinger Bands are standard deviation. MACD is signal processing they took the math, removed the part that makes it useful, and sold you the wrapper > this roadmap: free, 60 seconds, in this video > same four subjects tested at every quant interview since the 1990s > time to learn properly: 6-8 months with free resources > what they unlock: the actual language Wall Street runs on the information was never gated it was just never packaged for the people who needed it most full breakdown in the video below

delost

34,884 views • 3 months ago

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:

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

171,069 views • 11 days ago

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.

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

108,121 views • 5 months ago

A finance professor manages $200M with AI agents, and he told everyone why: "Large language models are at the level of a fourth-year PhD student in every field" Alejandro Lopez-Lira's AI fund, Autopilot, returned 56% last year. The S&P did 16%. There are 52,000 people with money in it, and most of them just watch the machine work. What he automated is the same six-step loop every fund on earth runs: find an idea, code it, backtest it, deploy it, read the autopsy, learn from it. A quant at Two Sigma runs that loop once a month, and the salary time alone costs around $50,000 per hypothesis. All steps from this loop now fit in AI trading text box. Plain English in, executable strategy out, five-year backtest in 12 seconds, live on a broker 90 seconds after you typed the sentence. He runs $200M with AI. You can run same AI fund in two clicks, free to try: Step 6 on this loop is where everyone is stuck. Your agent has no memory. Every strategy it kills goes into a log nobody reads, and the next one starts from zero. Nobody keeps negative results. Not Citadel, not Man Group, not a single repo on GitHub. Fix that and the agent remembers every hypothesis it killed and the regime it died in. It stops burning cycles on your old mistakes. Jane Street pays 3,500 people to run this cycle and made $39.6 billion doing it. Five sixths of it is now free. Bookmark & read full map of this loop in the article below. Most people still think AI trading is out of reach for them - it isn't. Don't want to spend a dollar for testing this? Kalshi just opened a perps exchange and gives US users $25 free to start ->

A finance professor manages $200M with AI agents, and he told everyone why: "Large language models are at the level of a fourth-year PhD student in every field" Alejandro Lopez-Lira's AI fund, Autopilot, returned 56% last year. The S&P did 16%. There are 52,000 people with money in it, and most of them just watch the machine work. What he automated is the same six-step loop every fund on earth runs: find an idea, code it, backtest it, deploy it, read the autopsy, learn from it. A quant at Two Sigma runs that loop once a month, and the salary time alone costs around $50,000 per hypothesis. All steps from this loop now fit in AI trading text box. Plain English in, executable strategy out, five-year backtest in 12 seconds, live on a broker 90 seconds after you typed the sentence. He runs $200M with AI. You can run same AI fund in two clicks, free to try: Step 6 on this loop is where everyone is stuck. Your agent has no memory. Every strategy it kills goes into a log nobody reads, and the next one starts from zero. Nobody keeps negative results. Not Citadel, not Man Group, not a single repo on GitHub. Fix that and the agent remembers every hypothesis it killed and the regime it died in. It stops burning cycles on your old mistakes. Jane Street pays 3,500 people to run this cycle and made $39.6 billion doing it. Five sixths of it is now free. Bookmark & read full map of this loop in the article below. Most people still think AI trading is out of reach for them - it isn't. Don't want to spend a dollar for testing this? Kalshi just opened a perps exchange and gives US users $25 free to start ->

cvxv666

83,134 views • 1 month ago

Inside the mind of an ex-SIG quant trader who can't turn off the EV brain - even for his kid's school choice Andrew Courtney (Andrew Courtney) ran the International ETFs Trading Desk at Susquehanna International Group for ~15 years before leaving in 2023. He now runs Kalshionomics (Kalshinomics), a prediction markets analytics tool, and writes the Whirligig Bear, one of the sharpest prediction markets Substacks out there. "I think of everything as a bet. I kind of don't understand how you talk to normal people — they do not do that." SIG trains their junior traders with poker, spending 2hrs/day turning over cards after every hand, justifying every decision quantitatively AND qualitatively. 15 years later, Andrew views prediction markets the same way: read who's on the other side, size accordingly, fold when the whale comes back at you 10x. We cover: - Why SIG pays junior traders to play poker for 2hrs/day — & what happens after every single hand - The "one eye on the market, always" attention tax that destroys most people's careers - How to find edge in prediction markets by asking: who am I actually trading against? - Why meme-heavy, overhyped markets (Taylor Swift at the Super Bowl) might be the juiciest trades - The insider trading debate in prediction markets — & why it's "socially corrosive" - Floor trading vs. upstairs quant: why the transition saved his career - 40 connections after ~15 years at one of the world's best firms — the hidden cost of prop trading - Why he doesn't have collision insurance on his car (& the EV math behind it) Thank you so much Andrew Courtney for coming on the pod! Timestamps: 00:00 Intro 05:00 Floor trading vs. electronic trading 06:28 What makes an upstairs trader 10:16 Poker as trader training 13:00 Thinking in bets as a mental framework 15:11 Decision trees in real life 16:40 Where prediction markets actually have edge 19:00 Why the LLM forecasting layer falls short 19:40 Liquidity incentives and trading low-volume markets 22:00 Limiting downside even when the model is wrong 24:32 Executing in illiquid markets 25:44 Fair value vs. directional conviction 27:11 Bayesian updating when liquidity responds 28:40 Fading hype and crowded narratives 31:07 Longshot bias vs. fanbase bias 34:20 How to judge whether you really have edge 36:40 Building analytics tools for prediction markets 38:20 The temporary edge for smart amateurs 40:35 Where prediction markets fit best 41:20 Markets that shouldn’t exist 43:20 Why insider trading corrodes incentives 46:52 Are prediction markets a net good or bad 50:47 Minimizing degeneracy and maximizing signal 53:32 A simple EV mindset anyone can use

Inside the mind of an ex-SIG quant trader who can't turn off the EV brain - even for his kid's school choice Andrew Courtney (Andrew Courtney) ran the International ETFs Trading Desk at Susquehanna International Group for ~15 years before leaving in 2023. He now runs Kalshionomics (Kalshinomics), a prediction markets analytics tool, and writes the Whirligig Bear, one of the sharpest prediction markets Substacks out there. "I think of everything as a bet. I kind of don't understand how you talk to normal people — they do not do that." SIG trains their junior traders with poker, spending 2hrs/day turning over cards after every hand, justifying every decision quantitatively AND qualitatively. 15 years later, Andrew views prediction markets the same way: read who's on the other side, size accordingly, fold when the whale comes back at you 10x. We cover: - Why SIG pays junior traders to play poker for 2hrs/day — & what happens after every single hand - The "one eye on the market, always" attention tax that destroys most people's careers - How to find edge in prediction markets by asking: who am I actually trading against? - Why meme-heavy, overhyped markets (Taylor Swift at the Super Bowl) might be the juiciest trades - The insider trading debate in prediction markets — & why it's "socially corrosive" - Floor trading vs. upstairs quant: why the transition saved his career - 40 connections after ~15 years at one of the world's best firms — the hidden cost of prop trading - Why he doesn't have collision insurance on his car (& the EV math behind it) Thank you so much Andrew Courtney for coming on the pod! Timestamps: 00:00 Intro 05:00 Floor trading vs. electronic trading 06:28 What makes an upstairs trader 10:16 Poker as trader training 13:00 Thinking in bets as a mental framework 15:11 Decision trees in real life 16:40 Where prediction markets actually have edge 19:00 Why the LLM forecasting layer falls short 19:40 Liquidity incentives and trading low-volume markets 22:00 Limiting downside even when the model is wrong 24:32 Executing in illiquid markets 25:44 Fair value vs. directional conviction 27:11 Bayesian updating when liquidity responds 28:40 Fading hype and crowded narratives 31:07 Longshot bias vs. fanbase bias 34:20 How to judge whether you really have edge 36:40 Building analytics tools for prediction markets 38:20 The temporary edge for smart amateurs 40:35 Where prediction markets fit best 41:20 Markets that shouldn’t exist 43:20 Why insider trading corrodes incentives 46:52 Are prediction markets a net good or bad 50:47 Minimizing degeneracy and maximizing signal 53:32 A simple EV mindset anyone can use

Ethan Kho

436,074 views • 7 months ago

He ran a desk competing head-on with Jane Street and Jump at Genesis. Asked what the secret sauce was, he says the tech wasn't the edge: Lucas Schuermann built the electronic market-making system at Genesis. Now founder & CEO of Variational. Lucas Schuermann (Lucas) explains: "Genesis, in many cases, was some of the best pricing you could get in the crypto market. But we all know the other players trading these assets at the time: the Jumps and the Jane Streets and many others of the world." "People like to ask me: how were you guys competitive? What was the edge? What was the secret sauce?" "While I like to think our infrastructure was great, and we built some very smart pricing systems, one of the biggest advantages a desk or a market maker of any kind can have is flow." "Flow is just the size of the client base. It's the size and the economy of scale of the book of business you have." "The bigger the economy of scale on external venues, the more you get. Maybe lower fee tiers, maybe better margin rates, maybe in some cases even preferential access to latency and matching." "Most importantly, with natural incoming flow you can internalize: match risk on your own book without ever going out to hedge it externally, and manage portfolio-level risk much more intelligently." "Having flow also gives you information about where the price might be going on a very, very short time horizon." "This is the biggest misconception: that HFT firms or market makers have to win purely by nature of being fast. It's definitely a component. But having flow, just having an economy of scale, is one of the biggest reasons Genesis was winning at the time in that space."

He ran a desk competing head-on with Jane Street and Jump at Genesis. Asked what the secret sauce was, he says the tech wasn't the edge: Lucas Schuermann built the electronic market-making system at Genesis. Now founder & CEO of Variational. Lucas Schuermann (Lucas) explains: "Genesis, in many cases, was some of the best pricing you could get in the crypto market. But we all know the other players trading these assets at the time: the Jumps and the Jane Streets and many others of the world." "People like to ask me: how were you guys competitive? What was the edge? What was the secret sauce?" "While I like to think our infrastructure was great, and we built some very smart pricing systems, one of the biggest advantages a desk or a market maker of any kind can have is flow." "Flow is just the size of the client base. It's the size and the economy of scale of the book of business you have." "The bigger the economy of scale on external venues, the more you get. Maybe lower fee tiers, maybe better margin rates, maybe in some cases even preferential access to latency and matching." "Most importantly, with natural incoming flow you can internalize: match risk on your own book without ever going out to hedge it externally, and manage portfolio-level risk much more intelligently." "Having flow also gives you information about where the price might be going on a very, very short time horizon." "This is the biggest misconception: that HFT firms or market makers have to win purely by nature of being fast. It's definitely a component. But having flow, just having an economy of scale, is one of the biggest reasons Genesis was winning at the time in that space."

Ethan Kho

16,708 views • 1 month ago

Fired Jane Street quant walked out with ten years of private BTC trading data - and turned it into $1.5M He didn't build a bot. He built a simulator that sees the matrix - it runs every move Bitcoin can make before it makes one. His wallet: Here's what he actually built. A swarm of agents feeds ten years of stolen tick data into MiroFish - a god-tier agentic simulator. It doesn't forecast the next candle. It spins up a virtual market and plays Bitcoin forward through thousands of scenarios at once. Six agents each validate their own call. A trade only fires when they converge. They collect data 24/7, re-run the sim, and remember every pattern, every reaction, every signal they've ever seen. He doesn't predict the future. The math already knows it. He just reads the numbers and takes the money. I've been copying him for a week and PnL prints like a money printer on steroids. Here's the part the firms don't want public: MiroFish just broke algo trading. The desks are quietly building their own simulators right now. The window where one solo wallet can run this is still open - barely. Save this and build your own - or skip the homework and copy his with this bot:

Fired Jane Street quant walked out with ten years of private BTC trading data - and turned it into $1.5M He didn't build a bot. He built a simulator that sees the matrix - it runs every move Bitcoin can make before it makes one. His wallet: Here's what he actually built. A swarm of agents feeds ten years of stolen tick data into MiroFish - a god-tier agentic simulator. It doesn't forecast the next candle. It spins up a virtual market and plays Bitcoin forward through thousands of scenarios at once. Six agents each validate their own call. A trade only fires when they converge. They collect data 24/7, re-run the sim, and remember every pattern, every reaction, every signal they've ever seen. He doesn't predict the future. The math already knows it. He just reads the numbers and takes the money. I've been copying him for a week and PnL prints like a money printer on steroids. Here's the part the firms don't want public: MiroFish just broke algo trading. The desks are quietly building their own simulators right now. The window where one solo wallet can run this is still open - barely. Save this and build your own - or skip the homework and copy his with this bot:

cvxv666

132,166 views • 3 months ago

Wall Street has PhDs, billions in infrastructure, and mathematical models. Retail traders had YouTube and hope. This is why so many feel like they’re always “right” yet still not getting paid. You've seen this evolution firsthand. 233% growth in futures traders, but failure rates still >90%.That gap has just closed. Most trading education sells you their system. We teach you to build your own using institutional tools. Use our Backtesting Engine to test ideas, profile probability scenarios, and enforce risk automatically. You become self-reliant, not dependent. Starts Jan 17th. The Daily Profiler Bootcamp: → Not another "watch me trade" course → Not another "trust me bro, it's high probability but won't show you the numbers" seminar It's 12 weeks of building your trading infrastructure: - Daily Profiler Framework (P12 scenarios + Four Steps execution + HOD/LOD times and probabilities) - Candle Science (OHLC pattern probabilities) - Trading Journal (track your actual performance with the correct measurements for sustainability and survivability of edge) - Business Plan (use our system to build your risk plan, business plan, and trading plan) - Risk Profiling (using the tool to save hours of manual backtesting; understand which metrics to use to determine where your stop and TP should go based on data, not what you were told) Limited spots. First come, first served. Enrollment closes Jan 16th until next quarter. Retweet if you found value in the video. Sign up in comments Austin Clark

The Daily Profiler

17,160 views • 8 months ago

Inside Two Sigma & AQR with Bill Mann: How Early Quants Built Edge Before the Modern Tools Existed Bill Mann spent nearly 11 years across two of the world's most elite quant funds — AQR & Two Sigma — rising to Senior Vice President while building alpha models, establishing quantamental research teams, & designing the ML/AI systems that powered their forecasts. "A real edge you used to have 15 years ago was creating your own version of someone else's data." We cover: - How Two Sigma's fundamentals team built proprietary data pipelines before vendors existed - Why point-in-time databases were a secret weapon — & how look-ahead bias destroyed competitors - The crowding problem hiding inside everyone's favorite value factor - LLMs in quant research: what agents can already replace & what still requires human intuition - Why junior quants are at risk — & the one mindset that keeps senior researchers irreplaceable - How HarmoniQ Insights pivoted from advising buy-side firms to backing fintech startups with sweat equity - The New Barbarians thesis: crypto natives meeting old Wall Street, & why both sides need each other - Bill's one piece of advice for aspiring quants: build your own model, put real money behind it, learn from the losses Timestamps: 00:00 Intro 00:57 Life as a Quant at Two Sigma 02:36 Finding Edge in Fundamental Data 07:04 Creating a Creative Quant Research Culture 11:19 How LLMs Change Quantitative Trading 15:52 AI’s Impact on Junior Quant Careers 22:56 Using AI Tools for Learning 23:57 HarmoniQ Insights: Advising Fintech Startups 30:47 The New Barbarians Podcast Explained 33:26 Crypto and Market Makers vs TradFi 34:54 Career Advice for Aspiring Quants 38:46 Final Takeaways

Inside Two Sigma & AQR with Bill Mann: How Early Quants Built Edge Before the Modern Tools Existed Bill Mann spent nearly 11 years across two of the world's most elite quant funds — AQR & Two Sigma — rising to Senior Vice President while building alpha models, establishing quantamental research teams, & designing the ML/AI systems that powered their forecasts. "A real edge you used to have 15 years ago was creating your own version of someone else's data." We cover: - How Two Sigma's fundamentals team built proprietary data pipelines before vendors existed - Why point-in-time databases were a secret weapon — & how look-ahead bias destroyed competitors - The crowding problem hiding inside everyone's favorite value factor - LLMs in quant research: what agents can already replace & what still requires human intuition - Why junior quants are at risk — & the one mindset that keeps senior researchers irreplaceable - How HarmoniQ Insights pivoted from advising buy-side firms to backing fintech startups with sweat equity - The New Barbarians thesis: crypto natives meeting old Wall Street, & why both sides need each other - Bill's one piece of advice for aspiring quants: build your own model, put real money behind it, learn from the losses Timestamps: 00:00 Intro 00:57 Life as a Quant at Two Sigma 02:36 Finding Edge in Fundamental Data 07:04 Creating a Creative Quant Research Culture 11:19 How LLMs Change Quantitative Trading 15:52 AI’s Impact on Junior Quant Careers 22:56 Using AI Tools for Learning 23:57 HarmoniQ Insights: Advising Fintech Startups 30:47 The New Barbarians Podcast Explained 33:26 Crypto and Market Makers vs TradFi 34:54 Career Advice for Aspiring Quants 38:46 Final Takeaways

Ethan Kho

23,460 views • 7 months ago

COPY TRADING GUIDE FOR POLYMARKET (full video + exact settings included) I spent months testing different tools, setups and configs. Lost everything at the start. Then made it back and started earning once the setup was right. This cost me a lot of time, you need ~3 minutes: 1) Open this bot: I tried different tools, but this one has more advanced features and the fastest execution. 2. Click Copy trade -> Add Task -> W1 3. Paste wallet address and name it Use particular names like "AI trader", "sports 85% win rate whale" etc. 4. After entering the amount choose the exact setup: • Fixed (same trading amount every time) • Exact (each trade is the same as main trader's) • Portfolio based (proportional to your balance) You have to use the one that fits trader's beaviour. How to choose? • Use Fixed for traders with Start Congrats, now your bot runs automatically. This setup has worked best for me over the last 3 months. Hope this will help you too.

COPY TRADING GUIDE FOR POLYMARKET (full video + exact settings included) I spent months testing different tools, setups and configs. Lost everything at the start. Then made it back and started earning once the setup was right. This cost me a lot of time, you need ~3 minutes: 1) Open this bot: I tried different tools, but this one has more advanced features and the fastest execution. 2. Click Copy trade -> Add Task -> W1 3. Paste wallet address and name it Use particular names like "AI trader", "sports 85% win rate whale" etc. 4. After entering the amount choose the exact setup: • Fixed (same trading amount every time) • Exact (each trade is the same as main trader's) • Portfolio based (proportional to your balance) You have to use the one that fits trader's beaviour. How to choose? • Use Fixed for traders with Start Congrats, now your bot runs automatically. This setup has worked best for me over the last 3 months. Hope this will help you too.

Oracle Boar

207,014 views • 7 months ago

After 1,000+ trades, this is the only setup that consistently works in any market condition. It's called liquidity sweep reversal—and it's the highest probability trading strategy I know. Before I show you what it is, here are the two things most traders get wrong with it: 1) They enter too early and get stopped out on the second sweep. 2) They try to predict the LAST sweep with certainty This is a sure-fire way to burn your money. Here's what to do instead: Step 1: Identify market control Look at structure. Higher highs and higher lows? Buyers are in control. We're only trading from demand zones. Step 2: Mark your liquidity zones Find equal lows. When retail sees a "double bottom," they go long because textbooks tell them to. Their stop losses sit right below those lows. Available liquidity for institutions to sweep. Step 3: Wait for the sweep Price drops, sweeps those stops, liquidates retail traders, then creates a sharp V-shaped reaction. This sweep breaks structure. Zoom into 1-hour timeframe - price was making lower highs and lows. After the sweep? Higher highs and higher lows. That sweep zone becomes your institutional demand zone. Step 4: Enter on mitigation Wait for price to pull back to the liquidity zone. Enter there. Stop below the zone. Target 2-3R. Remember: You'll NEVER predict with 100% certainty when it's the LAST liquidity sweep. Sometimes price sweeps 2-3 times before the real move. But that's trading—we trade probabilities, not certainties. Also, keep in mind: If you can't spot the liquidity, you ARE the liquidity. — This is just a breakdown of one of the trading strategies we covered in our 2-hour long cryptocurrency trading course. I also discussed the trend pullback strategy, how to trade breakout retests without getting stopped out on fake moves, and why understanding liquidity is the only way to avoid becoming exit liquidity. Just comment "COURSE" and I'll DM it to you immediately so you can watch it.

After 1,000+ trades, this is the only setup that consistently works in any market condition. It's called liquidity sweep reversal—and it's the highest probability trading strategy I know. Before I show you what it is, here are the two things most traders get wrong with it: 1) They enter too early and get stopped out on the second sweep. 2) They try to predict the LAST sweep with certainty This is a sure-fire way to burn your money. Here's what to do instead: Step 1: Identify market control Look at structure. Higher highs and higher lows? Buyers are in control. We're only trading from demand zones. Step 2: Mark your liquidity zones Find equal lows. When retail sees a "double bottom," they go long because textbooks tell them to. Their stop losses sit right below those lows. Available liquidity for institutions to sweep. Step 3: Wait for the sweep Price drops, sweeps those stops, liquidates retail traders, then creates a sharp V-shaped reaction. This sweep breaks structure. Zoom into 1-hour timeframe - price was making lower highs and lows. After the sweep? Higher highs and higher lows. That sweep zone becomes your institutional demand zone. Step 4: Enter on mitigation Wait for price to pull back to the liquidity zone. Enter there. Stop below the zone. Target 2-3R. Remember: You'll NEVER predict with 100% certainty when it's the LAST liquidity sweep. Sometimes price sweeps 2-3 times before the real move. But that's trading—we trade probabilities, not certainties. Also, keep in mind: If you can't spot the liquidity, you ARE the liquidity. — This is just a breakdown of one of the trading strategies we covered in our 2-hour long cryptocurrency trading course. I also discussed the trend pullback strategy, how to trade breakout retests without getting stopped out on fake moves, and why understanding liquidity is the only way to avoid becoming exit liquidity. Just comment "COURSE" and I'll DM it to you immediately so you can watch it.

The Trading Geek (Brad Goh)

54,308 views • 9 months ago

This trader built a bot with Claude Fable 5 and made $96,000 on Polymarket. Average trade size: $3. The bot trades crypto Up/Down markets using a hybrid strategy that combines two-sided market making, arbitrage, and directional trading. The strategy has 5 steps: 1. Monitors multiple crypto markets continuously The bot tracks BTC, ETH, and SOL price action in real-time across different time windows. 2. Calculates its own fair probability for Up and Down No reliance on Polymarket prices. The bot builds its own model and compares it to the order book. 3. Places orders at prices that keep the combined cost of Up + Down below $1 This is the arbitrage edge. If Up costs 0.52 and Down costs 0.49, total is 1.01. The bot waits. When total drops below 1.00, it enters. 4. Manages the unhedged portion of the position After capturing arbitrage, the bot actively manages remaining exposure instead of holding passively. 5. Keeps directional exposure when its model identifies one outcome as undervalued When the bot sees clear mispricing, it leans into the undervalued side and holds until the edge closes. The entire edge is repeating small wins thousands of times. With an average trade size of $3, the bot captures micro-edges that manual traders ignore. No emotions. No hesitation. Just calculated entries repeated at scale until they compound into serious profit. Most people try to make big bets and predict major moves. This system just identifies tiny mispricings, enters with $3 average positions, and repeats the process until it grows the account. $96K profit built on thousands of $3 trades. The system runs autonomous: → Claude Fable 5 handles 5-step decision logic → Monitors multiple crypto markets 24/7 → Calculates fair probability independent of Polymarket prices → Enters when combined Up + Down cost drops below $1 → Manages unhedged positions actively → Holds directional exposure when model sees clear edge No manual trading. No guessing. Just finding micro-edges and exploiting them thousands of times. 💡 I'm sharing the complete Claude Fable 5 prompt and 5-step hybrid trading workflow. Free for 24 hours. To get it: 1️⃣ Comment the "Claude" 2️⃣ Like and Repost 3️⃣ Follow Himanshu Kumar I'll DM you the setup.

This trader built a bot with Claude Fable 5 and made $96,000 on Polymarket. Average trade size: $3. The bot trades crypto Up/Down markets using a hybrid strategy that combines two-sided market making, arbitrage, and directional trading. The strategy has 5 steps: 1. Monitors multiple crypto markets continuously The bot tracks BTC, ETH, and SOL price action in real-time across different time windows. 2. Calculates its own fair probability for Up and Down No reliance on Polymarket prices. The bot builds its own model and compares it to the order book. 3. Places orders at prices that keep the combined cost of Up + Down below $1 This is the arbitrage edge. If Up costs 0.52 and Down costs 0.49, total is 1.01. The bot waits. When total drops below 1.00, it enters. 4. Manages the unhedged portion of the position After capturing arbitrage, the bot actively manages remaining exposure instead of holding passively. 5. Keeps directional exposure when its model identifies one outcome as undervalued When the bot sees clear mispricing, it leans into the undervalued side and holds until the edge closes. The entire edge is repeating small wins thousands of times. With an average trade size of $3, the bot captures micro-edges that manual traders ignore. No emotions. No hesitation. Just calculated entries repeated at scale until they compound into serious profit. Most people try to make big bets and predict major moves. This system just identifies tiny mispricings, enters with $3 average positions, and repeats the process until it grows the account. $96K profit built on thousands of $3 trades. The system runs autonomous: → Claude Fable 5 handles 5-step decision logic → Monitors multiple crypto markets 24/7 → Calculates fair probability independent of Polymarket prices → Enters when combined Up + Down cost drops below $1 → Manages unhedged positions actively → Holds directional exposure when model sees clear edge No manual trading. No guessing. Just finding micro-edges and exploiting them thousands of times. 💡 I'm sharing the complete Claude Fable 5 prompt and 5-step hybrid trading workflow. Free for 24 hours. To get it: 1️⃣ Comment the "Claude" 2️⃣ Like and Repost 3️⃣ Follow Himanshu Kumar I'll DM you the setup.

Himanshu Kumar

102,805 views • 1 month ago