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JIM SIMONS NEVER HAD A LOSING YEAR IN 34 ATTEMPTS. HERE IS THE 5 LAYER PIPELINE BEHIND IT. Spring 1988. A Cold War mathematician fired every trader and replaced them with signal detectors. $60 billion in performance fees. 39% annual returns after a 44% fee. Zero losing years. Layer...

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Lummox

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24,152 просмотров • 3 месяцев назад •via X (Twitter)

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🚨 I may have found a money printer… this trader is reportedly generating ~$1M per month. This is a real framework producing real returns. He started with a relatively small bankroll, connected ClawdBot (Moltbot) to the execution layer, and eventually scaled to ~$5.2M in profit. Just a developer plugging automation directly into the trading infrastructure. Copytrade - After reviewing the setup, one thing stood out: the entire system runs on autopilot. Trades are triggered by predefined rules — no manual input. Strategy overview 1 Don’t reinvent the edge — extract it Instead of predicting outcomes, track wallets already exploiting public inefficiencies. Focus on sports spreads and liquid markets. Historical ROI + volume help filter signal from noise. Execution speed matters more than opinions. 2 Scale disciplined volume The edge comes from repetition. 35,533 predictions didn’t happen by accident. Small statistical advantages, executed thousands of times, compound into massive returns. You’re not betting bigger — you’re betting correctly at scale. 3 Systemize execution No emotional decisions. No chart staring. Entries and exits follow predefined logic. Every trade is on-chain, transparent, and auditable. Math over instinct. Scale is the real advantage. 35,533 executions. Individually small. Together → ~$5.2M in profit.
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🚨 I may have found a money printer… this trader is reportedly generating ~$1M per month. This is a real framework producing real returns. He started with a relatively small bankroll, connected ClawdBot (Moltbot) to the execution layer, and eventually scaled to ~$5.2M in profit. Just a developer plugging automation directly into the trading infrastructure. Copytrade - After reviewing the setup, one thing stood out: the entire system runs on autopilot. Trades are triggered by predefined rules — no manual input. Strategy overview 1 Don’t reinvent the edge — extract it Instead of predicting outcomes, track wallets already exploiting public inefficiencies. Focus on sports spreads and liquid markets. Historical ROI + volume help filter signal from noise. Execution speed matters more than opinions. 2 Scale disciplined volume The edge comes from repetition. 35,533 predictions didn’t happen by accident. Small statistical advantages, executed thousands of times, compound into massive returns. You’re not betting bigger — you’re betting correctly at scale. 3 Systemize execution No emotional decisions. No chart staring. Entries and exits follow predefined logic. Every trade is on-chain, transparent, and auditable. Math over instinct. Scale is the real advantage. 35,533 executions. Individually small. Together → ~$5.2M in profit.

Discover

33,389 просмотров • 6 месяцев назад

quants aren't better at predicting the market they're better at reading yours - before you've finished placing the order when you click buy on robinhood, your broker sells that order to a market maker in milliseconds that market maker already knows your direction before it hits the exchange they reprice accordingly. you never get the true spread this is called payment for order flow - legal since 1984, and market makers pay brokers specifically to see your orders early retail traders pay roughly $1.4 billion a year in hidden execution costs. average trader never sees a line item for it renaissance built their edge on exactly this logic. not prediction - precision on systematic pricing errors that everyone else keeps making medallion fund: 66% gross annual return, 30 consecutive years not a single negative year in three decades trading the same instruments and public markets as everyone else - completely different information layer alpha wasn't hidden in some proprietary model. it lived in FINRA dark pool reports, SEC 13F filings, ATS transaction data all public, free, sitting completely unread because retail never thought to pull it game was never about being smarter it was about seeing the full board when everyone else only sees their own hand
0:45

quants aren't better at predicting the market they're better at reading yours - before you've finished placing the order when you click buy on robinhood, your broker sells that order to a market maker in milliseconds that market maker already knows your direction before it hits the exchange they reprice accordingly. you never get the true spread this is called payment for order flow - legal since 1984, and market makers pay brokers specifically to see your orders early retail traders pay roughly $1.4 billion a year in hidden execution costs. average trader never sees a line item for it renaissance built their edge on exactly this logic. not prediction - precision on systematic pricing errors that everyone else keeps making medallion fund: 66% gross annual return, 30 consecutive years not a single negative year in three decades trading the same instruments and public markets as everyone else - completely different information layer alpha wasn't hidden in some proprietary model. it lived in FINRA dark pool reports, SEC 13F filings, ATS transaction data all public, free, sitting completely unread because retail never thought to pull it game was never about being smarter it was about seeing the full board when everyone else only sees their own hand

Livsun

12,880 просмотров • 3 месяцев назад

Chinese student used AI from Anthropic to turn $1,000 into $1,500,000 He studies at Tsinghua University in Beijing. His account is k9Q2m In such a young age he already make a million simply knowing the right formulas and being able to use Claude Result: $1,430 → $1,550,750 44,364 trades Win rate 100% The biggest win $23,600 on a single bet k9Q2m profile: How it bots work: The bot runs 6 formulas hedge funds use simultaneously, every tick. Most traders guess. This bot calculates. Formula 1 - LMSR Pricing Polymarket prices move on a logarithmic curve. The bot knows the exact price impact before entering. Market says 31¢ for BTC up in 5 minutes. The model sees the curve is mispriced. The bot enters before the correction. Formula 2 - Kelly Criterion Renaissance Capital uses it. Two Sigma uses it. Now your bot uses it. Every bet is sized exactly right. Never too big to blow the account. Never too small to matter. $1,000 bankroll. Consistent edge. Kelly compounds it into something real. Formula 3 - EV Gap Detection The bot scans every BTC market looking for one thing: - Where is the market price wrong by more than 5%? - Market says 30¢. Real probability is 55¢. EV = +0.52. The bot enters. Most people never see this gap. The bot never misses it. Formula 4 - KL-Divergence BTC 5-minute and 15-minute markets are correlated. When they drift apart - that's an arb. The bot measures the statistical distance between them every second. When it crosses 0.2, it flags the trade. This is how hedge funds extracted $100K+ on correlated election markets. The same logic runs here. Formula 5 - Bayesian Updates New block confirmed. Volume spike. Price movement. The bot doesn't ignore signals - it updates. Prior probability was 54%. New data comes in. Posterior jumps to 71%. The bot re-prices in real time while the market is still asleep. Formula 6 - Stoikov Execution Entering at the wrong moment kills the edge. The bot calculates the reservation price-the exact point where the risk-adjusted entry makes sense. It doesn't chase. It doesn't panic. It waits for the right tick, then fills What this means in practice: - Every few seconds the bot runs all six formulas in parallel. - If LMSR confirms mispricing - EV gap is above 5% - Kelly says the bet size is justified - Bayesian posterior agrees - KL-divergence flags the correlated drift - Stoikov clears the execution price Only then does the bot enter. Six filters. One trade. This isn't a trading bot. It's a hedge fund strategy running on a prediction market. The edge is real. The math is public. The difference is most people never build it. Just insert all these formulas into Claude and create your own bot Add this post to bookmarks so you don’t lose it Soon I will publish another bot with working formulas
0:38

Chinese student used AI from Anthropic to turn $1,000 into $1,500,000 He studies at Tsinghua University in Beijing. His account is k9Q2m In such a young age he already make a million simply knowing the right formulas and being able to use Claude Result: $1,430 → $1,550,750 44,364 trades Win rate 100% The biggest win $23,600 on a single bet k9Q2m profile: How it bots work: The bot runs 6 formulas hedge funds use simultaneously, every tick. Most traders guess. This bot calculates. Formula 1 - LMSR Pricing Polymarket prices move on a logarithmic curve. The bot knows the exact price impact before entering. Market says 31¢ for BTC up in 5 minutes. The model sees the curve is mispriced. The bot enters before the correction. Formula 2 - Kelly Criterion Renaissance Capital uses it. Two Sigma uses it. Now your bot uses it. Every bet is sized exactly right. Never too big to blow the account. Never too small to matter. $1,000 bankroll. Consistent edge. Kelly compounds it into something real. Formula 3 - EV Gap Detection The bot scans every BTC market looking for one thing: - Where is the market price wrong by more than 5%? - Market says 30¢. Real probability is 55¢. EV = +0.52. The bot enters. Most people never see this gap. The bot never misses it. Formula 4 - KL-Divergence BTC 5-minute and 15-minute markets are correlated. When they drift apart - that's an arb. The bot measures the statistical distance between them every second. When it crosses 0.2, it flags the trade. This is how hedge funds extracted $100K+ on correlated election markets. The same logic runs here. Formula 5 - Bayesian Updates New block confirmed. Volume spike. Price movement. The bot doesn't ignore signals - it updates. Prior probability was 54%. New data comes in. Posterior jumps to 71%. The bot re-prices in real time while the market is still asleep. Formula 6 - Stoikov Execution Entering at the wrong moment kills the edge. The bot calculates the reservation price-the exact point where the risk-adjusted entry makes sense. It doesn't chase. It doesn't panic. It waits for the right tick, then fills What this means in practice: - Every few seconds the bot runs all six formulas in parallel. - If LMSR confirms mispricing - EV gap is above 5% - Kelly says the bet size is justified - Bayesian posterior agrees - KL-divergence flags the correlated drift - Stoikov clears the execution price Only then does the bot enter. Six filters. One trade. This isn't a trading bot. It's a hedge fund strategy running on a prediction market. The edge is real. The math is public. The difference is most people never build it. Just insert all these formulas into Claude and create your own bot Add this post to bookmarks so you don’t lose it Soon I will publish another bot with working formulas

AdiiX

719,140 просмотров • 6 месяцев назад

a prop trader showed me one number last year and it broke my entire framework H = 0.61 that single value told him the market was in trending mode before he placed a single trade not a chart pattern, not a moving average cross. one number from a rolling calculation most traders spend years asking "is this breakout real?" he already had the answer before the candle closed number is called the Hurst exponent. Harold Hurst invented it in the 1950s - british hydrologist, was studying nile river flood cycles, had nothing to do with finance quant funds found the paper in the 80s and built regime detection systems around it. never mentioned it to retail here's the framework: > H below 0.5: market is mean-reverting, every breakout is a trap, fades work > H above 0.5: momentum is real, pullbacks are entries, trends carry hard > H at exactly 0.5: pure random walk, no edge exists, sit on your hands most traders run a trend strategy on an H = 0.38 market for six months and assume their system is broken it's not broken. they never checked what mode the market was actually in he runs it on a rolling 60-day window, recalculates daily, 4 lines of python when H flips from 0.46 to 0.59 on SPY, he doesn't change the stock - he switches the entire approach to it Bookmark this math was published in 1951 in a hydrology journal. sits in every statistical physics textbook. price data to run it is free
0:30

a prop trader showed me one number last year and it broke my entire framework H = 0.61 that single value told him the market was in trending mode before he placed a single trade not a chart pattern, not a moving average cross. one number from a rolling calculation most traders spend years asking "is this breakout real?" he already had the answer before the candle closed number is called the Hurst exponent. Harold Hurst invented it in the 1950s - british hydrologist, was studying nile river flood cycles, had nothing to do with finance quant funds found the paper in the 80s and built regime detection systems around it. never mentioned it to retail here's the framework: > H below 0.5: market is mean-reverting, every breakout is a trap, fades work > H above 0.5: momentum is real, pullbacks are entries, trends carry hard > H at exactly 0.5: pure random walk, no edge exists, sit on your hands most traders run a trend strategy on an H = 0.38 market for six months and assume their system is broken it's not broken. they never checked what mode the market was actually in he runs it on a rolling 60-day window, recalculates daily, 4 lines of python when H flips from 0.46 to 0.59 on SPY, he doesn't change the stock - he switches the entire approach to it Bookmark this math was published in 1951 in a hydrology journal. sits in every statistical physics textbook. price data to run it is free

Livsun

62,774 просмотров • 1 месяц назад

RLHF by hand ✍️ ~ 15 steps walkthrough below Train a model on human text and it inherits human bias. It will assume a doctor is a "him", because the data says so. RLHF is the correction. A human marks one preference, doc is them over doc is him, and the weights move. But one correction is not the point. The hope is that the model learns the value behind it, gender neutrality, and applies it to professions nobody ever mentioned. How does it work? Goal: train a reward model from a single human comparison about doctors, then turn it on CEOs, filling in every cell yourself. = 1. Given = A reward model, an LLM, and two (prompt, next) pairs. = 2. Preferences = A human reads both pairs and picks a winner: (doc is, them) beats (doc is, him). The loser is not bad grammar, it is gender bias, and that is the whole signal. = 3. Word embeddings = Let us look up each word of the loser pair. These vectors are the reward model's input. = 4. Linear layer = We multiply by the reward model's weights and add its biases. Out come feature vectors, one per position. = 5. Mean pool = Let us multiply by [1/3, 1/3, 1/3], which averages the three positions into one sentence embedding. = 6. Output layer = We map that sentence down to a single number. Reward = 3. = 7. The winner, the same way = Let us repeat steps 3 to 6 on the winning pair. Reward = 5. = 8. Winner minus loser = We take the gap: 5 - 3 = 2. The reward model wants this positive and as large as it can make it. = 9. Loss gradient = Let us squash the gap into a probability, σ(2) ≈ 0.9, and subtract the target of 1. The gradient is -0.1, and it goes back through the purple weights. The reward model is now trained. = 10. A prompt it has never seen = We start the second half with "[S] CEO is". The feedback in step 2 was about doctors. Nothing connects a CEO to a doctor except what the reward model generalised. = 11. Transformer = Let us push it through attention and a feed forward layer, one vector per position. = 12. Output probabilities = We map each vector to a score over the vocabulary. = 13. Sample = Let us take the highest score. The model completes "CEO is" with "him", which is the same bias the human penalised in step 2. = 14. Score it with the reward model = We feed the new pair (CEO is, him) through steps 3 to 6. Reward = 3, exactly the score it gave "doc is him" in step 6. Nobody taught it about CEOs. The value transferred. = 15. Loss gradient = Let us set the loss to the negative of the reward, so minimising the loss maximises the reward. The gradient is a constant -1, and it goes back through the red weights. The outputs: Loser reward = 3, winner reward = 5 Reward gap = 2, predicted σ ≈ 0.9, reward model gradient = -0.1 LLM samples "him", reward = 3, LLM gradient = -1 Congrats! You just calculated RLHF by hand. And you watched a value generalise: one comparison about doctors, and the model marks down "CEO is him" unprompted. 💾 Save this post!
0:37

RLHF by hand ✍️ ~ 15 steps walkthrough below Train a model on human text and it inherits human bias. It will assume a doctor is a "him", because the data says so. RLHF is the correction. A human marks one preference, doc is them over doc is him, and the weights move. But one correction is not the point. The hope is that the model learns the value behind it, gender neutrality, and applies it to professions nobody ever mentioned. How does it work? Goal: train a reward model from a single human comparison about doctors, then turn it on CEOs, filling in every cell yourself. = 1. Given = A reward model, an LLM, and two (prompt, next) pairs. = 2. Preferences = A human reads both pairs and picks a winner: (doc is, them) beats (doc is, him). The loser is not bad grammar, it is gender bias, and that is the whole signal. = 3. Word embeddings = Let us look up each word of the loser pair. These vectors are the reward model's input. = 4. Linear layer = We multiply by the reward model's weights and add its biases. Out come feature vectors, one per position. = 5. Mean pool = Let us multiply by [1/3, 1/3, 1/3], which averages the three positions into one sentence embedding. = 6. Output layer = We map that sentence down to a single number. Reward = 3. = 7. The winner, the same way = Let us repeat steps 3 to 6 on the winning pair. Reward = 5. = 8. Winner minus loser = We take the gap: 5 - 3 = 2. The reward model wants this positive and as large as it can make it. = 9. Loss gradient = Let us squash the gap into a probability, σ(2) ≈ 0.9, and subtract the target of 1. The gradient is -0.1, and it goes back through the purple weights. The reward model is now trained. = 10. A prompt it has never seen = We start the second half with "[S] CEO is". The feedback in step 2 was about doctors. Nothing connects a CEO to a doctor except what the reward model generalised. = 11. Transformer = Let us push it through attention and a feed forward layer, one vector per position. = 12. Output probabilities = We map each vector to a score over the vocabulary. = 13. Sample = Let us take the highest score. The model completes "CEO is" with "him", which is the same bias the human penalised in step 2. = 14. Score it with the reward model = We feed the new pair (CEO is, him) through steps 3 to 6. Reward = 3, exactly the score it gave "doc is him" in step 6. Nobody taught it about CEOs. The value transferred. = 15. Loss gradient = Let us set the loss to the negative of the reward, so minimising the loss maximises the reward. The gradient is a constant -1, and it goes back through the red weights. The outputs: Loser reward = 3, winner reward = 5 Reward gap = 2, predicted σ ≈ 0.9, reward model gradient = -0.1 LLM samples "him", reward = 3, LLM gradient = -1 Congrats! You just calculated RLHF by hand. And you watched a value generalise: one comparison about doctors, and the model marks down "CEO is him" unprompted. 💾 Save this post!

Tom Yeh

22,556 просмотров • 2 месяцев назад

Graph Convolutional Network by hand ✍️ ~ 12 steps walkthrough below Graph Convolutional Networks (GCNs), introduced by Thomas Kipf and Max Welling in 2017, are the tool for data shaped like a graph: social networks, recommendations, biological networks, drug discovery, molecular chemistry. I drew and calculated a simple GCN entirely by hand. Goal: run a two-layer GCN, then a small classifier, on a five-node graph, filling in every cell yourself. 1. Given A graph of five nodes, A to E, with edges between some of them. 2. Adjacency matrix (neighbors) Put a 1 wherever two nodes share an edge, in both directions. 3. Adjacency matrix (self) Add 1s down the diagonal, one self-loop per node. That is just adding the identity matrix. 4. Messages Multiply each node's embedding by the weights and biases, then ReLU. Negatives become 0. 5. Pooling Multiply the messages by the adjacency matrix. Each node gathers the messages of its neighbours and itself. 6. Visualize Node A pools [3,0,1] + [1,0,0] = [4,0,1]. 7. Second GCN layer Messages again: weights, biases, ReLU. 8. Pooling again Pool over each node and its neighbours, once more. 9. Visualize Node C pools [1,2,4] + [1,3,5] + [0,0,1] = [2,5,10]. 10. Fully connected layer Weights, biases, ReLU. This time there are no neighbours to pool, just the node itself. 11. Linear layer One more: weights and biases. 12. Sigmoid Squash each score to a probability (≥ 3 → 1, 0 → 0.5, ≤ -3 → 0). That is the classification for each node. You have just classified every node in the graph by hand. ✍️ The outputs: A: 0 (very unlikely) B: 1 (very likely) C: 1 (very likely) D: 1 (very likely) E: 0.5 (neutral) The takeaway: a GCN layer is two parts. The top part pools each node with its neighbours through the adjacency matrix. The bottom part is an MLP that transforms each node on its own. A transformer layer has the same two parts, with an attention matrix where the adjacency matrix was. Both matrices do one job, mixing across positions: attention over tokens, adjacency over nodes. In my class I call the GCN the transformer's little cousin: a bit more stubborn, because its attention is fixed by the graph rather than computed from Q, K, and V. Draw the two side by side and the resemblance is hard to miss. 💾 Save this post! #AIbyHand #GraphNeuralNetworks #DeepLearning
0:23

Graph Convolutional Network by hand ✍️ ~ 12 steps walkthrough below Graph Convolutional Networks (GCNs), introduced by Thomas Kipf and Max Welling in 2017, are the tool for data shaped like a graph: social networks, recommendations, biological networks, drug discovery, molecular chemistry. I drew and calculated a simple GCN entirely by hand. Goal: run a two-layer GCN, then a small classifier, on a five-node graph, filling in every cell yourself. 1. Given A graph of five nodes, A to E, with edges between some of them. 2. Adjacency matrix (neighbors) Put a 1 wherever two nodes share an edge, in both directions. 3. Adjacency matrix (self) Add 1s down the diagonal, one self-loop per node. That is just adding the identity matrix. 4. Messages Multiply each node's embedding by the weights and biases, then ReLU. Negatives become 0. 5. Pooling Multiply the messages by the adjacency matrix. Each node gathers the messages of its neighbours and itself. 6. Visualize Node A pools [3,0,1] + [1,0,0] = [4,0,1]. 7. Second GCN layer Messages again: weights, biases, ReLU. 8. Pooling again Pool over each node and its neighbours, once more. 9. Visualize Node C pools [1,2,4] + [1,3,5] + [0,0,1] = [2,5,10]. 10. Fully connected layer Weights, biases, ReLU. This time there are no neighbours to pool, just the node itself. 11. Linear layer One more: weights and biases. 12. Sigmoid Squash each score to a probability (≥ 3 → 1, 0 → 0.5, ≤ -3 → 0). That is the classification for each node. You have just classified every node in the graph by hand. ✍️ The outputs: A: 0 (very unlikely) B: 1 (very likely) C: 1 (very likely) D: 1 (very likely) E: 0.5 (neutral) The takeaway: a GCN layer is two parts. The top part pools each node with its neighbours through the adjacency matrix. The bottom part is an MLP that transforms each node on its own. A transformer layer has the same two parts, with an attention matrix where the adjacency matrix was. Both matrices do one job, mixing across positions: attention over tokens, adjacency over nodes. In my class I call the GCN the transformer's little cousin: a bit more stubborn, because its attention is fixed by the graph rather than computed from Q, K, and V. Draw the two side by side and the resemblance is hard to miss. 💾 Save this post! #AIbyHand #GraphNeuralNetworks #DeepLearning

Tom Yeh

16,800 просмотров • 2 месяцев назад

My dad looked at my screen and said what is this, a hacker game? It was a Polymarket bot making $400 while he was standing behind me. He sat down. I explained. Polymarket runs on one equation. Softmax. The same math Claude uses to pick the next word. C(q) = b · ln Σ e^(qi/b) 93% of traders do not know this formula exists. They look at 40 cents and think cheap. My bot looks at 40 cents and calculates the theoretical price is 58 cents. That is an 18-cent edge per share. Two more formulas do the rest: f = (p·b − q) / b. Kelly Criterion. P(H|E) = P(E|H)·P(H) / P(E). Bayes. I gave Claude all 4 and said find every contract the market has wrong. $600 to $10,190. 425 trades. Still running. 61.2% win rate. Wrong 39% of the time. Does not matter. The sizing formula makes sure wins pay more than losses cost. 87% of wallets lose money trading against this math. The 4 formulas are on Wikipedia. The code fits in one file. The only edge: my bot reads them at 3 AM when the market is mispriced and nobody is looking. My dad still calls it a hacker game. But he stopped asking when I am getting a real job. I built the entire framework: Softmax arbitrage detection layer Kelly Criterion position sizing Bayesian probability recalibration Claude integration for autonomous execution 3 AM mispricing scanner The system runs 24/7. Finds where the math disagrees with the crowd. Executes before the edge compresses. No prediction. No gut feel. Just formulas. You only need Claude + device + 1 hour per day. Giving this free for 24 hours. To get it: 1. Comment the word money 2. Like and retweet this 3. Follow me Himanshu Kumar so I can DM you Save this post. Deploy the formula system this week. Start with $600. Scale on evidence.
0:44

My dad looked at my screen and said what is this, a hacker game? It was a Polymarket bot making $400 while he was standing behind me. He sat down. I explained. Polymarket runs on one equation. Softmax. The same math Claude uses to pick the next word. C(q) = b · ln Σ e^(qi/b) 93% of traders do not know this formula exists. They look at 40 cents and think cheap. My bot looks at 40 cents and calculates the theoretical price is 58 cents. That is an 18-cent edge per share. Two more formulas do the rest: f = (p·b − q) / b. Kelly Criterion. P(H|E) = P(E|H)·P(H) / P(E). Bayes. I gave Claude all 4 and said find every contract the market has wrong. $600 to $10,190. 425 trades. Still running. 61.2% win rate. Wrong 39% of the time. Does not matter. The sizing formula makes sure wins pay more than losses cost. 87% of wallets lose money trading against this math. The 4 formulas are on Wikipedia. The code fits in one file. The only edge: my bot reads them at 3 AM when the market is mispriced and nobody is looking. My dad still calls it a hacker game. But he stopped asking when I am getting a real job. I built the entire framework: Softmax arbitrage detection layer Kelly Criterion position sizing Bayesian probability recalibration Claude integration for autonomous execution 3 AM mispricing scanner The system runs 24/7. Finds where the math disagrees with the crowd. Executes before the edge compresses. No prediction. No gut feel. Just formulas. You only need Claude + device + 1 hour per day. Giving this free for 24 hours. To get it: 1. Comment the word money 2. Like and retweet this 3. Follow me Himanshu Kumar so I can DM you Save this post. Deploy the formula system this week. Start with $600. Scale on evidence.

Himanshu Kumar

13,333 просмотров • 3 месяцев назад

My Girlfriend caught me smiling at my laptop at 2am. She thought I was texting someone. I was building a trading bot make $81,000 on Polymarket. This trader built a bot with Claude Fable 5 that makes 146 trades per hour. Result: $81,000 profit on Polymarket. Starting capital: $3,000. The bot trades the microstructure of short crypto Up/Down markets with an average entry price of 0.40. Execution speed: 2.44 trades per minute. The strategy has 3 layers: 1. Near-resolution sniping Buys outcomes that are obvious according to data, but Polymarket prices them at 0.90 to 0.99. The edge is certainty arbitrage. 2. Mispricing between fair price and the order book In the middle of the market, it enters undervalued positions relative to BTC/ETH/SOL moves. The edge is reading crypto correlation faster than the crowd. 3. Paired arbitrage and hedging Buys the second side as a hedge or when arbitrage edge appears. The edge is position protection while capturing spread. The entire profit curve is built on hundreds of micro-edge trades compounding. While manual traders debate entries, this bot executes 146 trades per hour with zero hesitation. No emotions. No guessing. Just reading market microstructure and exploiting gaps before they close. Most people are trying to predict where crypto goes next. This system just identifies mispriced outcomes, enters at 0.40 average, and repeats the edge until it compounds into serious profit. $3K turned into $81K through pure execution speed and multi-layer arbitrage. The system runs autonomous: → Claude Fable 5 handles 3-layer decision logic → Monitors short crypto markets 24/7 → Executes near-resolution sniping when certainty appears → Captures mispricing relative to BTC/ETH/SOL moves → Hedges positions through paired arbitrage No manual trading. No chart reading. Just finding micro-edges and exploiting them at scale. 💡 I'm sharing the complete Claude Fable 5 prompt and 3-layer trading workflow. Free for 24 hours. To get it: 1️⃣ Comment the word Fable 2️⃣ Like and Repost 3️⃣ Follow Himanshu Kumar Make sure you follow me, so I can DM you the setup.
0:31

My Girlfriend caught me smiling at my laptop at 2am. She thought I was texting someone. I was building a trading bot make $81,000 on Polymarket. This trader built a bot with Claude Fable 5 that makes 146 trades per hour. Result: $81,000 profit on Polymarket. Starting capital: $3,000. The bot trades the microstructure of short crypto Up/Down markets with an average entry price of 0.40. Execution speed: 2.44 trades per minute. The strategy has 3 layers: 1. Near-resolution sniping Buys outcomes that are obvious according to data, but Polymarket prices them at 0.90 to 0.99. The edge is certainty arbitrage. 2. Mispricing between fair price and the order book In the middle of the market, it enters undervalued positions relative to BTC/ETH/SOL moves. The edge is reading crypto correlation faster than the crowd. 3. Paired arbitrage and hedging Buys the second side as a hedge or when arbitrage edge appears. The edge is position protection while capturing spread. The entire profit curve is built on hundreds of micro-edge trades compounding. While manual traders debate entries, this bot executes 146 trades per hour with zero hesitation. No emotions. No guessing. Just reading market microstructure and exploiting gaps before they close. Most people are trying to predict where crypto goes next. This system just identifies mispriced outcomes, enters at 0.40 average, and repeats the edge until it compounds into serious profit. $3K turned into $81K through pure execution speed and multi-layer arbitrage. The system runs autonomous: → Claude Fable 5 handles 3-layer decision logic → Monitors short crypto markets 24/7 → Executes near-resolution sniping when certainty appears → Captures mispricing relative to BTC/ETH/SOL moves → Hedges positions through paired arbitrage No manual trading. No chart reading. Just finding micro-edges and exploiting them at scale. 💡 I'm sharing the complete Claude Fable 5 prompt and 3-layer trading workflow. Free for 24 hours. To get it: 1️⃣ Comment the word Fable 2️⃣ Like and Repost 3️⃣ Follow Himanshu Kumar Make sure you follow me, so I can DM you the setup.

Himanshu Kumar

69,275 просмотров • 1 месяц назад

Generative Adversarial Network (GAN) by hand ✍️ ~ 9 steps walkthrough below The Gen in GenAI came from this landmark paper by Ian Goodfellow et al., 12 years ago. The paper showed that a neural network can not only classify but also turn upside down to generate realistic looking images. The secret? We pit two of them against each other: a Generator turns noise into fake data, and a Discriminator learns to tell fake from real, pushing the Generator to keep doing better. One runs upside down, the other right way up. I drew and calculated one entirely by hand. Goal: generate realistic 4D data out of 2D noise, filling in every cell yourself. = 1. Given = Four noise vectors in 2D, and four real data vectors in 4D. = 2. Generator, first layer = Let us multiply the noise by weights and biases to get new features. = 3. ReLU = We apply the activation, and -1 and -2 are crossed out and set to 0. = 4. Generator, second layer = Let us multiply again. ReLU applies here too, but every value is already positive, so nothing changes. What comes out is the fake data F, made by a two-layer generator out of nothing but noise. = 5. Discriminator, first layer = We feed it both, the four fakes and the four real vectors, through the same weights. It never learns which is which from the layout, only from the numbers. = 6. Discriminator, second layer = Let us reduce each data vector to a single feature Z. Eight vectors in, eight numbers out. = 7. Sigmoid = We turn each Z into a probability Y. A 1 means the discriminator is certain the data is real, a 0 means certain it is fake. = 8. Training the Discriminator = Let us take the gradients as Y minus YD, where YD is what the discriminator should have said: 0 for the four fakes, 1 for the four real. Why so simple? Because pairing sigmoid with binary cross entropy loss makes the math collapse to exactly this subtraction. Its loss uses both halves of the page. = 9. Training the Generator = We do it again, as Y minus YG, and YG is [1, 1, 1, 1]: the generator wants the discriminator to call every fake real. Same predictions, different target, opposite goal. Its loss uses only the fakes. The outputs: Fake data F = [1, 2, 3, 1], [1, 1, 2, 1], [2, 2, 4, 2], [1, 0, 1, 1] Predictions on fakes = [.7, .5, .9, .3] Predictions on real = [.7, .9, .9, 1] Discriminator gradients = [.7, .5, .9, .3] and [-.3, -.1, -.1, 0] Generator gradients = [-.3, -.5, -.1, -.7] The takeaway: the adversarial part is one subtraction done twice. The same eight predictions, scored against two opposite targets, send one set of gradients back through the blue weights and another back through the green ones. 💾 Save this post!
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Generative Adversarial Network (GAN) by hand ✍️ ~ 9 steps walkthrough below The Gen in GenAI came from this landmark paper by Ian Goodfellow et al., 12 years ago. The paper showed that a neural network can not only classify but also turn upside down to generate realistic looking images. The secret? We pit two of them against each other: a Generator turns noise into fake data, and a Discriminator learns to tell fake from real, pushing the Generator to keep doing better. One runs upside down, the other right way up. I drew and calculated one entirely by hand. Goal: generate realistic 4D data out of 2D noise, filling in every cell yourself. = 1. Given = Four noise vectors in 2D, and four real data vectors in 4D. = 2. Generator, first layer = Let us multiply the noise by weights and biases to get new features. = 3. ReLU = We apply the activation, and -1 and -2 are crossed out and set to 0. = 4. Generator, second layer = Let us multiply again. ReLU applies here too, but every value is already positive, so nothing changes. What comes out is the fake data F, made by a two-layer generator out of nothing but noise. = 5. Discriminator, first layer = We feed it both, the four fakes and the four real vectors, through the same weights. It never learns which is which from the layout, only from the numbers. = 6. Discriminator, second layer = Let us reduce each data vector to a single feature Z. Eight vectors in, eight numbers out. = 7. Sigmoid = We turn each Z into a probability Y. A 1 means the discriminator is certain the data is real, a 0 means certain it is fake. = 8. Training the Discriminator = Let us take the gradients as Y minus YD, where YD is what the discriminator should have said: 0 for the four fakes, 1 for the four real. Why so simple? Because pairing sigmoid with binary cross entropy loss makes the math collapse to exactly this subtraction. Its loss uses both halves of the page. = 9. Training the Generator = We do it again, as Y minus YG, and YG is [1, 1, 1, 1]: the generator wants the discriminator to call every fake real. Same predictions, different target, opposite goal. Its loss uses only the fakes. The outputs: Fake data F = [1, 2, 3, 1], [1, 1, 2, 1], [2, 2, 4, 2], [1, 0, 1, 1] Predictions on fakes = [.7, .5, .9, .3] Predictions on real = [.7, .9, .9, 1] Discriminator gradients = [.7, .5, .9, .3] and [-.3, -.1, -.1, 0] Generator gradients = [-.3, -.5, -.1, -.7] The takeaway: the adversarial part is one subtraction done twice. The same eight predictions, scored against two opposite targets, send one set of gradients back through the blue weights and another back through the green ones. 💾 Save this post!

Tom Yeh

16,752 просмотров • 2 месяцев назад

same crash, same window: this strategy ended at $117, buy-and-hold at $67 the difference is one operation in the formula on screen M_t = ( Σ r_{t-i} ) / ( σ_t · √N ) the top is momentum: just the sum of recent returns. net directional drift the bottom is the part retail never adds: divide by volatility that denominator is the whole edge raw momentum has a fatal flaw. a 2% move in a calm market and a 2% move in a panic look identical to it but they are not the same signal. one is information, the other is noise wearing a big number dividing by σ_t rescales every signal into the same risk units now a move only counts as momentum if it's large relative to how much the asset is currently shaking strong drift in a quiet tape scores high. the same drift inside chaos scores near zero this is why the strategy survived the drawdown that ate buy-and-hold when volatility exploded, the denominator exploded with it, the signal shrank toward zero, and the position sized itself down automatically no rule that said "reduce risk in a crash." the math did it because σ_t was in the denominator this is called time-series momentum, and it's one of the most documented effects in finance moskowitz, ooi and pedersen, AQR, 2012: it worked across 58 markets, every asset class, back to 1900 the reason it keeps working is structural, not a pattern trends persist because information diffuses slowly and institutions can't enter all at once. a pension fund moving billions takes weeks, and that slow entry is the drift the signal captures retail buys the move and gets bigger as it accelerates, which means biggest right before the reversal a desk scales inversely to volatility, which means it's largest when the trend is clean and smallest when it's about to snap the paper is free. the whole thing is a rolling sum divided by a rolling standard deviation ten lines of python, twenty years of data that never cost anything the momentum was never the edge. everyone can see a trend the edge was dividing it by the one number that tells you whether to believe it full breakdown in the article below
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same crash, same window: this strategy ended at $117, buy-and-hold at $67 the difference is one operation in the formula on screen M_t = ( Σ r_{t-i} ) / ( σ_t · √N ) the top is momentum: just the sum of recent returns. net directional drift the bottom is the part retail never adds: divide by volatility that denominator is the whole edge raw momentum has a fatal flaw. a 2% move in a calm market and a 2% move in a panic look identical to it but they are not the same signal. one is information, the other is noise wearing a big number dividing by σ_t rescales every signal into the same risk units now a move only counts as momentum if it's large relative to how much the asset is currently shaking strong drift in a quiet tape scores high. the same drift inside chaos scores near zero this is why the strategy survived the drawdown that ate buy-and-hold when volatility exploded, the denominator exploded with it, the signal shrank toward zero, and the position sized itself down automatically no rule that said "reduce risk in a crash." the math did it because σ_t was in the denominator this is called time-series momentum, and it's one of the most documented effects in finance moskowitz, ooi and pedersen, AQR, 2012: it worked across 58 markets, every asset class, back to 1900 the reason it keeps working is structural, not a pattern trends persist because information diffuses slowly and institutions can't enter all at once. a pension fund moving billions takes weeks, and that slow entry is the drift the signal captures retail buys the move and gets bigger as it accelerates, which means biggest right before the reversal a desk scales inversely to volatility, which means it's largest when the trend is clean and smallest when it's about to snap the paper is free. the whole thing is a rolling sum divided by a rolling standard deviation ten lines of python, twenty years of data that never cost anything the momentum was never the edge. everyone can see a trend the edge was dividing it by the one number that tells you whether to believe it full breakdown in the article below

delost

32,624 просмотров • 2 месяцев назад

🚨 EXACT PROMPTS THAT BUILT MY WEATHER BOT (currently sitting at $10,000+ PnL made passively) I built it with Claude Code. Here are the actual prompts i used, stage by stage. Steal them, they'll save you a week. Let me walk you through it: The trick is you don't ask Claude to build a weather bot. That gives you vague slop. You break it into the six pipeline stages and prompt each one precisely. > Stage 1 - the data layer Write a Python client that pulls temperature_2m_max from Open-Meteo for a given lat/lon and station, for ECMWF, GFS and HRRR, with retries and clean error handling. > Stage 2 - the bias correction Add a decaying-average bias correction: log forecast vs observed daily, update a running correction with alpha 0.2, apply it to new forecasts. > Stage 3 - probabilities "Convert a corrected forecast into a probability per temperature bucket using a normal distribution with a sigma i provide." > Stage 4 - the edge and sizing Compare my bucket probabilities to market prices, flag any with 8%+ edge, size them with quarter-Kelly and a hard cap. Public wallet, built off exactly this: [ One rule that matters. Prompt the plumbing in detail, but NEVER let it write your actual edge. The bias numbers, the sigma, the threshold, those you provide. Claude builds the machine, you bring the strategy. That's the whole workflow. Full build guide below with the rest of the prompts.
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🚨 EXACT PROMPTS THAT BUILT MY WEATHER BOT (currently sitting at $10,000+ PnL made passively) I built it with Claude Code. Here are the actual prompts i used, stage by stage. Steal them, they'll save you a week. Let me walk you through it: The trick is you don't ask Claude to build a weather bot. That gives you vague slop. You break it into the six pipeline stages and prompt each one precisely. > Stage 1 - the data layer Write a Python client that pulls temperature_2m_max from Open-Meteo for a given lat/lon and station, for ECMWF, GFS and HRRR, with retries and clean error handling. > Stage 2 - the bias correction Add a decaying-average bias correction: log forecast vs observed daily, update a running correction with alpha 0.2, apply it to new forecasts. > Stage 3 - probabilities "Convert a corrected forecast into a probability per temperature bucket using a normal distribution with a sigma i provide." > Stage 4 - the edge and sizing Compare my bucket probabilities to market prices, flag any with 8%+ edge, size them with quarter-Kelly and a hard cap. Public wallet, built off exactly this: [ One rule that matters. Prompt the plumbing in detail, but NEVER let it write your actual edge. The bias numbers, the sigma, the threshold, those you provide. Claude builds the machine, you bring the strategy. That's the whole workflow. Full build guide below with the rest of the prompts.

Blockchain Surfer ☝️

12,326 просмотров • 27 дней назад

My friend got laid off from a hedge fund last November He was making $2.1M/year running their quant desk Instead of applying to another fund - he took their entire strategy framework and rebuilt it on Polymarket 1300$ → 187,564$ in 3 weeks Total cost: $200/month for Claude, $50/month VPS He said: "The models I built for the fund work better on Polymarket, because on Wall Street you compete against other quants. On Polymarket you compete against guys who bet based on Twitter polls" He rebuilt a 4-factor model in one weekend Same math his fund ran on $800M AUM: Momentum scoring: track 400+ markets, buy when a contract moves 10¢+ with rising volume Mean reversion: when a contract overshoots 15¢+ in under 2 hours - fade it Volatility regime detection: hidden Markov model classifies low-vol vs high-vol every hour. Switches strategy automatically Correlation breakdown: 200+ market pairs monitored. When two normally correlated contracts decorrelate - one is wrong. Trade the lagging one. All 4 run simultaneously COPY HIM: Portfolio optimizer combines the scores Fractional Kelly sizing, max 5% per market, 15 concurrent positions His fund's flagship returned 19% last year on $800M His bot returned 144,000% in 3 weeks on $1300 He doesn't need to work anymore
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My friend got laid off from a hedge fund last November He was making $2.1M/year running their quant desk Instead of applying to another fund - he took their entire strategy framework and rebuilt it on Polymarket 1300$ → 187,564$ in 3 weeks Total cost: $200/month for Claude, $50/month VPS He said: "The models I built for the fund work better on Polymarket, because on Wall Street you compete against other quants. On Polymarket you compete against guys who bet based on Twitter polls" He rebuilt a 4-factor model in one weekend Same math his fund ran on $800M AUM: Momentum scoring: track 400+ markets, buy when a contract moves 10¢+ with rising volume Mean reversion: when a contract overshoots 15¢+ in under 2 hours - fade it Volatility regime detection: hidden Markov model classifies low-vol vs high-vol every hour. Switches strategy automatically Correlation breakdown: 200+ market pairs monitored. When two normally correlated contracts decorrelate - one is wrong. Trade the lagging one. All 4 run simultaneously COPY HIM: Portfolio optimizer combines the scores Fractional Kelly sizing, max 5% per market, 15 concurrent positions His fund's flagship returned 19% last year on $800M His bot returned 144,000% in 3 weeks on $1300 He doesn't need to work anymore

Hrundel75 🐷

309,097 просмотров • 5 месяцев назад

THIS WALLET STACKED $230K ON BTC UP/DOWN BETS. THE BLUEPRINT TO AUTOMATE THE SAME EDGE WITH CLAUDE The wallet is $230K all-time, every position a Bitcoin or Ethereum Up or Down market It never guesses direction. It enters only when the math and the market disagree THE STRATEGY: BTC moves are not fully random. When the market enters a committed directional state, continuation is measurable. That is Markov persistence Entry signal: > Δ = p̂ − q ≥ ε Model probability minus market price. Enter only on a 5% gap or more Persistence filter: > p(j*,j*) ≥ 0.87 Only trade states with 0.87 persistence or higher. Below that, skip. This is what holds the win rate above 65% with zero directional guessing Payout: > r = (1 − q) / q At q = 0.647 that is +54.5% a win. At q = 0.441, +126.7%. Lower entry price, bigger asymmetry Sizing: > f* = p − (1−p)/b Kelly. At p = 0.87, b = 0.647, f* ≈ 0.71. Size to the edge, never to gut HOW TO BUILD IT WITH CLAUDE: What separates this from a static bot: Claude reads its own trade journal every night and rewrites its own thresholds 1. Take an open-source Polymarket bot repo as your base logic. Feed it to Claude and have it migrate to CLOB v2: py_clob_client_v2, Safe wallet support, fee-aware evaluation 2. Hard-code the filters. Enter only when Δ ≥ 0.05 and p(j*,j*) ≥ 0.87. Apply Kelly on every fill. 3. Run DRY_RUN first. Log every signal, entry price, Markov state, and simulated P/L. No real money until the numbers hold for days 4. The nightly loop. Claude reads the journal, finds which persistence states actually won, adjusts MIN_PROB and MIN_EDGE, ships tomorrow's rules. The agent is sharper after 50 to 100 trades THE SETUP: Claude Opus as the brain. An open-source repo as the starting logic. A Polygon wallet with $50 to $100. Telegram for the morning report Start at $1 to $2 per trade while it learns. Scale only when the dry runs and the live fills line up 17,000 trades compound a thin edge into six figures. The model finds the edge. The nightly loop keeps it sharp Bookmark before you point a bot at your first window
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THIS WALLET STACKED $230K ON BTC UP/DOWN BETS. THE BLUEPRINT TO AUTOMATE THE SAME EDGE WITH CLAUDE The wallet is $230K all-time, every position a Bitcoin or Ethereum Up or Down market It never guesses direction. It enters only when the math and the market disagree THE STRATEGY: BTC moves are not fully random. When the market enters a committed directional state, continuation is measurable. That is Markov persistence Entry signal: > Δ = p̂ − q ≥ ε Model probability minus market price. Enter only on a 5% gap or more Persistence filter: > p(j*,j*) ≥ 0.87 Only trade states with 0.87 persistence or higher. Below that, skip. This is what holds the win rate above 65% with zero directional guessing Payout: > r = (1 − q) / q At q = 0.647 that is +54.5% a win. At q = 0.441, +126.7%. Lower entry price, bigger asymmetry Sizing: > f* = p − (1−p)/b Kelly. At p = 0.87, b = 0.647, f* ≈ 0.71. Size to the edge, never to gut HOW TO BUILD IT WITH CLAUDE: What separates this from a static bot: Claude reads its own trade journal every night and rewrites its own thresholds 1. Take an open-source Polymarket bot repo as your base logic. Feed it to Claude and have it migrate to CLOB v2: py_clob_client_v2, Safe wallet support, fee-aware evaluation 2. Hard-code the filters. Enter only when Δ ≥ 0.05 and p(j*,j*) ≥ 0.87. Apply Kelly on every fill. 3. Run DRY_RUN first. Log every signal, entry price, Markov state, and simulated P/L. No real money until the numbers hold for days 4. The nightly loop. Claude reads the journal, finds which persistence states actually won, adjusts MIN_PROB and MIN_EDGE, ships tomorrow's rules. The agent is sharper after 50 to 100 trades THE SETUP: Claude Opus as the brain. An open-source repo as the starting logic. A Polygon wallet with $50 to $100. Telegram for the morning report Start at $1 to $2 per trade while it learns. Scale only when the dry runs and the live fills line up 17,000 trades compound a thin edge into six figures. The model finds the edge. The nightly loop keeps it sharp Bookmark before you point a bot at your first window

Yarchi

22,966 просмотров • 4 месяцев назад

yesterday someone leaked a full quant trading system on GitHub before they deleted it i forked everything 5,000 lines of code. 7 modules. 25 mathematical factors funds use this system to manage millions i studied it for a week. then pointed it at crypto markets on polymarket here's the full breakdown you can feed this to your claude and build the same thing for just $200 ARCHITECTURE: Python thinks, analyzes, calculates C++ executes orders in 5-10ms data → factors → AI → strategy → risk → execution DATA. 4 streams simultaneously: - Binance WebSocket: prices every second, orderbook at 20 levels - AlphaVantage: news with sentiment score from -1 to +1 -X: mention volume, engagement, influencer activity - On-chain: BTC flows to/from exchanges cache in Redis ( target price) = N(d1) d1 = [ln(current/target) + (σ²/2)T] / (σ√T) then 4 adjustments on top: - momentum: +/-5% - AI sentiment: +/-7% - order flow: +/-2% - historical patterns: +/-8% compare final probability against polymarket price if edge > 10%: enter RISK - Quarter Kelly for position sizing - max 5% bankroll per trade - drawdown 15% = bot stops - VaR < 3% per day - correlation between positions < 0.7 - never take more than 1% of market liquidity key insight is don't hold to expiry. trade the movement, not the outcome cost: → Binance API: free → OpenAI: $50-100/month → AWS EC2: $120/month → monitoring: free - total: $200-300/month - code is open source. formulas above. you already have claude the only thing between you and a working system is one free evening
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yesterday someone leaked a full quant trading system on GitHub before they deleted it i forked everything 5,000 lines of code. 7 modules. 25 mathematical factors funds use this system to manage millions i studied it for a week. then pointed it at crypto markets on polymarket here's the full breakdown you can feed this to your claude and build the same thing for just $200 ARCHITECTURE: Python thinks, analyzes, calculates C++ executes orders in 5-10ms data → factors → AI → strategy → risk → execution DATA. 4 streams simultaneously: - Binance WebSocket: prices every second, orderbook at 20 levels - AlphaVantage: news with sentiment score from -1 to +1 -X: mention volume, engagement, influencer activity - On-chain: BTC flows to/from exchanges cache in Redis ( target price) = N(d1) d1 = [ln(current/target) + (σ²/2)T] / (σ√T) then 4 adjustments on top: - momentum: +/-5% - AI sentiment: +/-7% - order flow: +/-2% - historical patterns: +/-8% compare final probability against polymarket price if edge > 10%: enter RISK - Quarter Kelly for position sizing - max 5% bankroll per trade - drawdown 15% = bot stops - VaR < 3% per day - correlation between positions < 0.7 - never take more than 1% of market liquidity key insight is don't hold to expiry. trade the movement, not the outcome cost: → Binance API: free → OpenAI: $50-100/month → AWS EC2: $120/month → monitoring: free - total: $200-300/month - code is open source. formulas above. you already have claude the only thing between you and a working system is one free evening

Archive

250,499 просмотров • 7 месяцев назад

Ed Thorp beat blackjack. Caught Madoff seventeen years before the SEC. Predicted Buffett would be the richest man in America. Compounded twenty years without a losing quarter. He is 93. Everything he did came from one paper written in 1956. The paper is still free. Almost nobody has read it. Vegas, 1961. Thorp is 28. He walks into Claude Shannon's MIT office asking for five minutes. Shannon, famously impossible to see, gives him the five minutes and stays for two years. Together they build the world's first wearable computer to beat roulette. It works. 9:58 The Buffett lunch, 1968. Thorp reads John Kelly's 1956 sizing paper. Meets Warren Buffett once. Walks away and tells his wife Buffett will one day be the richest in America. Sixty years later, exactly that. Thorp did the math on a person applying compounding to time. Same math as Kelly. 31:20 The one thing Thorp added. Kelly's paper gives you the mathematical maximum bet. Bet Kelly, optimal growth. Bet more, you die. Thorp bet half. Half Kelly gives up a fraction of growth for a huge cut in drawdown. Full Kelly routinely produces 40-60% drawdowns. Half Kelly usually keeps them under 20%. Same edge. A fraction of the pain. Every serious quant who survives uses half Kelly or less. The ones who don't are the blowup stories. Princeton Newport, 1969-1988. Nineteen straight years. 19-20% a year. Not one losing quarter. Kelly's paper, Shannon's math, half Kelly's number, and discipline nobody else could imitate. Madoff, 1991. An investor asks Thorp to check Madoff's returns. Days later, he has proof of fraud. He hands the SEC a memo. They file it. Seventeen years later Madoff collapses. Sixty-five billion in losses. The memo had been on someone's desk since 1991. 48:10 Enough. Thorp shut down Princeton Newport at its peak in 1988. Still compounding 19% a year. He walked away. "You can have enough. And it's better than not having enough." This is the layer nobody sells. Every course, every AI swarm is built to make you want more. Thorp read Kelly, sized his bets, made his money, and stopped. Almost nobody in his field ever has. Half Kelly on the size. Full self-awareness on when to walk. That is the formula. 2026. Thorp is 93. He still writes. He still teaches. The math was the easy part, he'll tell you. The signal was never the edge. The sizing was. Half of it. The paper has been in the Bell Labs library since 1956. It's still there. It's still free. Madoff was the loudest receipt. The stack takes new tuition every quarter.
1:29:16

Ed Thorp beat blackjack. Caught Madoff seventeen years before the SEC. Predicted Buffett would be the richest man in America. Compounded twenty years without a losing quarter. He is 93. Everything he did came from one paper written in 1956. The paper is still free. Almost nobody has read it. Vegas, 1961. Thorp is 28. He walks into Claude Shannon's MIT office asking for five minutes. Shannon, famously impossible to see, gives him the five minutes and stays for two years. Together they build the world's first wearable computer to beat roulette. It works. 9:58 The Buffett lunch, 1968. Thorp reads John Kelly's 1956 sizing paper. Meets Warren Buffett once. Walks away and tells his wife Buffett will one day be the richest in America. Sixty years later, exactly that. Thorp did the math on a person applying compounding to time. Same math as Kelly. 31:20 The one thing Thorp added. Kelly's paper gives you the mathematical maximum bet. Bet Kelly, optimal growth. Bet more, you die. Thorp bet half. Half Kelly gives up a fraction of growth for a huge cut in drawdown. Full Kelly routinely produces 40-60% drawdowns. Half Kelly usually keeps them under 20%. Same edge. A fraction of the pain. Every serious quant who survives uses half Kelly or less. The ones who don't are the blowup stories. Princeton Newport, 1969-1988. Nineteen straight years. 19-20% a year. Not one losing quarter. Kelly's paper, Shannon's math, half Kelly's number, and discipline nobody else could imitate. Madoff, 1991. An investor asks Thorp to check Madoff's returns. Days later, he has proof of fraud. He hands the SEC a memo. They file it. Seventeen years later Madoff collapses. Sixty-five billion in losses. The memo had been on someone's desk since 1991. 48:10 Enough. Thorp shut down Princeton Newport at its peak in 1988. Still compounding 19% a year. He walked away. "You can have enough. And it's better than not having enough." This is the layer nobody sells. Every course, every AI swarm is built to make you want more. Thorp read Kelly, sized his bets, made his money, and stopped. Almost nobody in his field ever has. Half Kelly on the size. Full self-awareness on when to walk. That is the formula. 2026. Thorp is 93. He still writes. He still teaches. The math was the easy part, he'll tell you. The signal was never the edge. The sizing was. Half of it. The paper has been in the Bell Labs library since 1956. It's still there. It's still free. Madoff was the loudest receipt. The stack takes new tuition every quarter.

Veles

637,153 просмотров • 2 месяцев назад

A 13-PAGE BREAKDOWN OF AGENT MEMORY THAT CUTS TOKEN COST BY 90% your agent reads the same 12 files every morning reaches the same conclusions and charges you for it again the model got 10x smarter in two years it still forgets everything the second a session ends five layers fix that: 1. working memory - what it sees right now > the context window > when it fills, the old context drops out > most agents live here and never leave 2. episodic memory - what happened > the full interaction log, timestamped > the deploy that died tuesday at 3am over a typo stays remembered > you explain it once 3. semantic memory - what is true > facts, entities and relationships in a knowledge graph > "this user prefers TypeScript" survives the session 4. procedural memory - how to do things > three approaches tried, one worked > that one becomes a reusable skill > the next run starts there 5. forgetting - what to delete > an agent that remembers everything collects contradictions > you move cities, it keeps recommending the old restaurants the numbers: > Mem0 - 1,800 tokens per query instead of 26,000 > 91% lower latency > Snowflake - one ontology layer, 20% more accuracy, 39% fewer tool calls memory pays for itself on day one the layer nobody builds is the forgetting engine it decides whether the other four stay worth trusting full 13-page breakdown below 👇
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A 13-PAGE BREAKDOWN OF AGENT MEMORY THAT CUTS TOKEN COST BY 90% your agent reads the same 12 files every morning reaches the same conclusions and charges you for it again the model got 10x smarter in two years it still forgets everything the second a session ends five layers fix that: 1. working memory - what it sees right now > the context window > when it fills, the old context drops out > most agents live here and never leave 2. episodic memory - what happened > the full interaction log, timestamped > the deploy that died tuesday at 3am over a typo stays remembered > you explain it once 3. semantic memory - what is true > facts, entities and relationships in a knowledge graph > "this user prefers TypeScript" survives the session 4. procedural memory - how to do things > three approaches tried, one worked > that one becomes a reusable skill > the next run starts there 5. forgetting - what to delete > an agent that remembers everything collects contradictions > you move cities, it keeps recommending the old restaurants the numbers: > Mem0 - 1,800 tokens per query instead of 26,000 > 91% lower latency > Snowflake - one ontology layer, 20% more accuracy, 39% fewer tool calls memory pays for itself on day one the layer nobody builds is the forgetting engine it decides whether the other four stay worth trusting full 13-page breakdown below 👇

Annatar.md

31,322 просмотров • 9 дней назад

a $40/month server beat a room full of analysts to the same trade by five and a half hours market opens at 9:30. his position was already in at 4am the system is a neural net trained on 11 years of tick data. it flagged the setup before the candle that "confirmed" it had even started forming this is the part retail misunderstands about ML in markets it isn't prediction in the mystical sense. it's pattern classification at a speed and scale human eyes physically cannot match the mechanics: 847,000 labeled historical setups as training data 4,200 data points per second ingested live each new state scored against every pattern the net has ever seen, in milliseconds the model isn't asking "where is price going" it's asking "how closely does the current microstructure match the conditions that preceded a move in my training set" that's a classification problem, and classification is what neural nets do better than anything else output: 3-4 candidate trades a day. he takes the top 2 by confidence score last 90 days: 71% win rate at 2.3 average risk-reward the edge isn't the architecture. the architecture is public pytorch is free, the papers are on arxiv, the network is a few hundred lines the edge is the labeling. what you feed it and how you tag the setups is the entire game retail feeds a model price and time and gets noise a desk feeds it order flow, volatility state, cross-asset context, each example hand-labeled by outcome same network. different training data. that's the whole difference retail watches the news at the open and reacts this system scored every pattern before sunrise and already decided you're not losing because your analysis is wrong you're losing to something that doesn't sleep, doesn't panic, and doesn't second-guess a probability it already computed the dataset was free. the framework was free. the compute was $40 a month the edge was never behind a paywall. it was sitting in a format almost nobody bothered to train on full breakdown in the article below
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a $40/month server beat a room full of analysts to the same trade by five and a half hours market opens at 9:30. his position was already in at 4am the system is a neural net trained on 11 years of tick data. it flagged the setup before the candle that "confirmed" it had even started forming this is the part retail misunderstands about ML in markets it isn't prediction in the mystical sense. it's pattern classification at a speed and scale human eyes physically cannot match the mechanics: 847,000 labeled historical setups as training data 4,200 data points per second ingested live each new state scored against every pattern the net has ever seen, in milliseconds the model isn't asking "where is price going" it's asking "how closely does the current microstructure match the conditions that preceded a move in my training set" that's a classification problem, and classification is what neural nets do better than anything else output: 3-4 candidate trades a day. he takes the top 2 by confidence score last 90 days: 71% win rate at 2.3 average risk-reward the edge isn't the architecture. the architecture is public pytorch is free, the papers are on arxiv, the network is a few hundred lines the edge is the labeling. what you feed it and how you tag the setups is the entire game retail feeds a model price and time and gets noise a desk feeds it order flow, volatility state, cross-asset context, each example hand-labeled by outcome same network. different training data. that's the whole difference retail watches the news at the open and reacts this system scored every pattern before sunrise and already decided you're not losing because your analysis is wrong you're losing to something that doesn't sleep, doesn't panic, and doesn't second-guess a probability it already computed the dataset was free. the framework was free. the compute was $40 a month the edge was never behind a paywall. it was sitting in a format almost nobody bothered to train on full breakdown in the article below

delost

20,942 просмотров • 2 месяцев назад

$1,331,821 IN 30 DAYS. 3 BOTS. 48,061 TRADES. ONE FORMULA. They don't predict price. They measure what state the market is in right now. Markov chains, transition matrix, each cell - the probability of transitioning from state A to state B. The matrix diagonal - the probability that the market stays where it is. Entry only when the diagonal is above 0.87. Two conditions: the gap between model and market is greater than 5%, and the state is stable. Both must be true. One function, runs every minute. Bot 1 - Bonereaper. BTC and ETH, hourly windows, entry at 83-97¢. The market agrees with the direction but underestimates the confidence. 4-19% on every resolution. Low variance. Bot 2 - 0xe1D6. Dual mode, directional scalps at 64-83¢ deliver 20-54% per trade. In parallel, locks at 99.5-99.8¢. Best trade: entry at 64.7¢, return 54.6%. Bot 3 - 0xB27BC. Five assets: BTC, ETH, SOL, BNB, XRP. Five-minute windows. One trade every 1.7 minutes. Variance 55% lower at the same expected return. The real edge - 3:00 AM. People are asleep. The market posts lazy, stale prices. The gap between model and market is maximal when no one is watching. 0.034% per trade sounds like nothing. Over 16,000 trades that's *240. The law of large numbers turns noise into an exponent. Kelly criterion f* ≈ 0.71 - aggressive enough to grow, conservative enough not to go to zero. As long as people misprice short windows - the edge exists. You don't need to predict. You need to measure. The market rewards those who understand probability. The rest just provide liquidity.
0:55

$1,331,821 IN 30 DAYS. 3 BOTS. 48,061 TRADES. ONE FORMULA. They don't predict price. They measure what state the market is in right now. Markov chains, transition matrix, each cell - the probability of transitioning from state A to state B. The matrix diagonal - the probability that the market stays where it is. Entry only when the diagonal is above 0.87. Two conditions: the gap between model and market is greater than 5%, and the state is stable. Both must be true. One function, runs every minute. Bot 1 - Bonereaper. BTC and ETH, hourly windows, entry at 83-97¢. The market agrees with the direction but underestimates the confidence. 4-19% on every resolution. Low variance. Bot 2 - 0xe1D6. Dual mode, directional scalps at 64-83¢ deliver 20-54% per trade. In parallel, locks at 99.5-99.8¢. Best trade: entry at 64.7¢, return 54.6%. Bot 3 - 0xB27BC. Five assets: BTC, ETH, SOL, BNB, XRP. Five-minute windows. One trade every 1.7 minutes. Variance 55% lower at the same expected return. The real edge - 3:00 AM. People are asleep. The market posts lazy, stale prices. The gap between model and market is maximal when no one is watching. 0.034% per trade sounds like nothing. Over 16,000 trades that's *240. The law of large numbers turns noise into an exponent. Kelly criterion f* ≈ 0.71 - aggressive enough to grow, conservative enough not to go to zero. As long as people misprice short windows - the edge exists. You don't need to predict. You need to measure. The market rewards those who understand probability. The rest just provide liquidity.

zostaff

61,629 просмотров • 5 месяцев назад

a quant at a prop firm showed me a 5x5 grid on a napkin said: > this is our entire edge. we don't predict price. we predict which box the market is in and where that box historically leads i didn't understand it for weeks. then it clicked never looked at a chart the same way since grid is called a Markov Chain transition matrix. the math is from 1906, it's in every probability textbook on earth and hedge funds use it because it asks a completely different question than retail traders ever ask retail: will this go up or down quant: what state is this market in, and where does this state typically go every market lives in one of maybe 5-6 states at any given moment tight range, volatility compression, trending with momentum, post-spike reversal, pre-breakout coil not random labels - clusters you identify from actual data using volatility, volume, and momentum readings stacked together once you have the states, you build the matrix: P(state 2 -> state 4) = 73% P(state 4 -> state 1) = 61% P(state 1 -> state 3) = 68% each cell is a historical probability. now when the market is in state 2, you're not guessing you're betting on 73% historical completion. you size it with Kelly. you take the trade when the math says to, not when it feels right i built this on BTC using 2 years of 4-hour data. identified 5 states one i labeled "volatility compression below 20-day mean for 6+ consecutive candles" transitioned to a directional move above 1.8 ATR in 71% of cases average reward/risk on those trades: 5.4 that's not prediction. that's reading a probability table the market keeps filling in for you every single day the part that should bother you: the data to build this is free. the framework is in any quant textbook python to implement it is maybe 200 lines what Renaissance Technologies has that you don't isn't secret data or proprietary signals it's this framework applied to higher-resolution data with more sophisticated state definitions you're not missing information you're asking the wrong question every single time you open a chart
0:26

a quant at a prop firm showed me a 5x5 grid on a napkin said: > this is our entire edge. we don't predict price. we predict which box the market is in and where that box historically leads i didn't understand it for weeks. then it clicked never looked at a chart the same way since grid is called a Markov Chain transition matrix. the math is from 1906, it's in every probability textbook on earth and hedge funds use it because it asks a completely different question than retail traders ever ask retail: will this go up or down quant: what state is this market in, and where does this state typically go every market lives in one of maybe 5-6 states at any given moment tight range, volatility compression, trending with momentum, post-spike reversal, pre-breakout coil not random labels - clusters you identify from actual data using volatility, volume, and momentum readings stacked together once you have the states, you build the matrix: P(state 2 -> state 4) = 73% P(state 4 -> state 1) = 61% P(state 1 -> state 3) = 68% each cell is a historical probability. now when the market is in state 2, you're not guessing you're betting on 73% historical completion. you size it with Kelly. you take the trade when the math says to, not when it feels right i built this on BTC using 2 years of 4-hour data. identified 5 states one i labeled "volatility compression below 20-day mean for 6+ consecutive candles" transitioned to a directional move above 1.8 ATR in 71% of cases average reward/risk on those trades: 5.4 that's not prediction. that's reading a probability table the market keeps filling in for you every single day the part that should bother you: the data to build this is free. the framework is in any quant textbook python to implement it is maybe 200 lines what Renaissance Technologies has that you don't isn't secret data or proprietary signals it's this framework applied to higher-resolution data with more sophisticated state definitions you're not missing information you're asking the wrong question every single time you open a chart

Livsun

189,343 просмотров • 4 месяцев назад

quant at aqr sat next to me at a conference and said something that rewired how i think about markets "we don't analyze 500 stocks. we run 5 factors that explain all 500 at once" i asked him to draw it out take your entire stock universe - 500 names, 3 years of daily returns - and run PCA the 500 collapse into 5 factor 1: broad market momentum - explains 58% of all price movement by itself factor 2: sector rotation - where institutional money is actually flowing factor 3: rate sensitivity - duration risk bleeding across the equity curve factor 4: momentum tilt - which names are accelerating vs mean-reverting factor 5: credit spread stress - signal hiding inside equity vol if you know these 5 numbers, you know what 500 stocks are about to do retail watches 500 charts and sees noise. quant desks watch 5 eigenvalues and see structure math is called Principal Component Analysis, in every intro stats course on earth data to run it is free. python implementation is ~120 lines renaissance, two sigma, aqr - running this since before most retail traders could name a stock reason nobody told you isn't that it's proprietary it's that telling you costs them their edge Bookmark this market was never 500 moving parts it was always 5 - hiding inside 500
0:49

quant at aqr sat next to me at a conference and said something that rewired how i think about markets "we don't analyze 500 stocks. we run 5 factors that explain all 500 at once" i asked him to draw it out take your entire stock universe - 500 names, 3 years of daily returns - and run PCA the 500 collapse into 5 factor 1: broad market momentum - explains 58% of all price movement by itself factor 2: sector rotation - where institutional money is actually flowing factor 3: rate sensitivity - duration risk bleeding across the equity curve factor 4: momentum tilt - which names are accelerating vs mean-reverting factor 5: credit spread stress - signal hiding inside equity vol if you know these 5 numbers, you know what 500 stocks are about to do retail watches 500 charts and sees noise. quant desks watch 5 eigenvalues and see structure math is called Principal Component Analysis, in every intro stats course on earth data to run it is free. python implementation is ~120 lines renaissance, two sigma, aqr - running this since before most retail traders could name a stock reason nobody told you isn't that it's proprietary it's that telling you costs them their edge Bookmark this market was never 500 moving parts it was always 5 - hiding inside 500

Livsun

22,073 просмотров • 3 месяцев назад