Alex

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From 25 Years of Data to 79% Winrate on Prediction Markets Indicators [38] - Moving averages: MA10, MA20, MA30 - RSI (overbought/oversold) - MACD / MACD_Signal (trend) - Bollinger Upper/Lower (volatility) - Volatility 10/20/30, OBV, momentum, sentiment, insider activity, etc Humans can't do that -> AI processes everything in seconds Here's how it works. The purpose of the model Not to predict the price It answers one question: “Will there be growth tomorrow?” Target1 = (close.shift(-1) > close).astype(int) # 1 → BUY, 0 → HOLD For the complete structure, you also need: - Neural network architecture - Monte Carlo Dropout (key point) - Trading logic Result: - Win rate 59–78% - 100+ trades per month → consistently profitable - The human brain is incapable of analyzing 38 indicators × 30 markets × 24/7 Therefore, this trader has already made $1.4 million in profit in 4 months You can find a full explanation of how this works at Noisy Original reading time: 15 min

From 25 Years of Data to 79% Winrate on Prediction Markets Indicators [38] - Moving averages: MA10, MA20, MA30 - RSI (overbought/oversold) - MACD / MACD_Signal (trend) - Bollinger Upper/Lower (volatility) - Volatility 10/20/30, OBV, momentum, sentiment, insider activity, etc Humans can't do that -> AI processes everything in seconds Here's how it works. The purpose of the model Not to predict the price It answers one question: “Will there be growth tomorrow?” Target1 = (close.shift(-1) > close).astype(int) # 1 → BUY, 0 → HOLD For the complete structure, you also need: - Neural network architecture - Monte Carlo Dropout (key point) - Trading logic Result: - Win rate 59–78% - 100+ trades per month → consistently profitable - The human brain is incapable of analyzing 38 indicators × 30 markets × 24/7 Therefore, this trader has already made $1.4 million in profit in 4 months You can find a full explanation of how this works at Noisy Original reading time: 15 min

95,486 次观看

A Polymarket weather trader turned $6 → $2990 on a single Atlanta temperature bet a 49,744% return in 24 hours He built a system that snipes ultra-rare weather mispricings in the {0.2¢ - 11¢} range before the crowd checks the forecast GFS says Atlanta 38°F → 65% likely. Market prices it at $0.002. He buys Result: $0.002 → $1.00 when it hits = +49,744% return His best trades: $6 → $2,990 (Atlanta 38-39°F at 0.2¢ +49,744%) $40 → $1,871 (NYC 34-35°F at 2¢ +4,578%) $536 → $4,639 (Seattle 52-53°F at 11.6¢ +764%) Start copy-trading any trader even with $10 → Stat: > $57,136 all-time profit > 1,504 trades > Biggest win: $4,103

A Polymarket weather trader turned $6 → $2990 on a single Atlanta temperature bet a 49,744% return in 24 hours He built a system that snipes ultra-rare weather mispricings in the {0.2¢ - 11¢} range before the crowd checks the forecast GFS says Atlanta 38°F → 65% likely. Market prices it at $0.002. He buys Result: $0.002 → $1.00 when it hits = +49,744% return His best trades: $6 → $2,990 (Atlanta 38-39°F at 0.2¢ +49,744%) $40 → $1,871 (NYC 34-35°F at 2¢ +4,578%) $536 → $4,639 (Seattle 52-53°F at 11.6¢ +764%) Start copy-trading any trader even with $10 → Stat: > $57,136 all-time profit > 1,504 trades > Biggest win: $4,103

50,691 次观看

I found a Polymarket account that made $393k profit in 20 days with a single strategy: 5-minute Up/Down His entire strategy sits in one range: {7¢ – 22¢} Every entry between 7.6¢ and 21.7¢ outcomes the market gives less than 22% chance but the oracle already confirms at 80% Here's how: > Chainlink oracle updates BTC price > bot compares to Polymarket 5-min odds > sees divergence real probability 82%, market still at 7.6¢ > enters in milliseconds > 5 minutes later resolved > $918 becomes $5,732 > bot resets. next window in 5 minutes Start copy-trading any trader even with $10 → The math: 12,184 trades ÷ ~20 days = 609 trades/day $393,045 ÷ 20 = $19,652/day $19,652 ÷ 609 = $32.27 avg profit per trade

I found a Polymarket account that made $393k profit in 20 days with a single strategy: 5-minute Up/Down His entire strategy sits in one range: {7¢ – 22¢} Every entry between 7.6¢ and 21.7¢ outcomes the market gives less than 22% chance but the oracle already confirms at 80% Here's how: > Chainlink oracle updates BTC price > bot compares to Polymarket 5-min odds > sees divergence real probability 82%, market still at 7.6¢ > enters in milliseconds > 5 minutes later resolved > $918 becomes $5,732 > bot resets. next window in 5 minutes Start copy-trading any trader even with $10 → The math: 12,184 trades ÷ ~20 days = 609 trades/day $393,045 ÷ 20 = $19,652/day $19,652 ÷ 609 = $32.27 avg profit per trade

18,796 次观看

HondaCivic turned $37 → $15,182 on a Hong Kong temperature bet at 0.2¢ $63,788 P&L. 4,689 trades. Biggest single win: $15,144 He finds the mispricing. He doesn't care if it's at 0.2¢ or 99.9¢ How it works: Most weather quants pick one tick range and camp it. He picks a city, then asks: where is this market wrong? Follow his profile: Sometimes the answer is "0.2¢ on a tail nobody priced" buy and wait for a 500× resolve Sometimes the answer is "99.9¢ on a market that's already resolved in your favor but the order book is asleep" buy a dollar's worth of certainty for 99.9¢ and pocket the difference 4,000 times His winning trades cover the full price spectrum: $37 → $15,182 (Hong Kong 15°C YES at 0.2¢, +40,511%) $3,675 → $5,337 (London 12°C YES at 99.9¢, +45%) $3,703 → $8,759 (London 18°C NO at 42.2¢, +136%) $580 → $2,270 (NYC 56-57°F YES at 16.8¢, +291%) Notice the spread: 0.2¢, 16.8¢, 42.2¢, 99.9¢. Every tick on the order book has a setup He doesn't have a specialty. He has a scanner 4 cities. 4 different price ranges. One simple rule: find the gap, size the bet

12,562 次观看

Weather sniper on Polymarket turned $37 → $15,182 by placing a single bet on the temperature in Hong Kong He buys YES at any price from 0.2¢ tails to 99.9¢ near-resolves wherever the market lags the forecast Forecast says Hong Kong 15°C → 8%. Market prices it at $0.002. He buys Result: $0.002 → $1.00 when it hits = +40,511% return His best trades: $37 → $15,182 (Hong Kong 15°C at 0.2¢, +40,511%) $580 → $2,270 (NYC 56-57°F at 16.8¢, +291%) $3,675 → $5,337 (London 12°C at 99.9¢, +45%) Three trades. Three different prices. All YES. All resolved $1.00 Edge ratio = (1 − q) / q At q = 0.002 → 499× per winning trade At q = 0.168 → 4.95× per trade At q = 0.999 → 0.001× per trade but with 99% hit rate Cornwall edge at 0.2¢. Simons edge at 99.9¢. Most weather traders pick one. HondaCivic runs both $56,504 all-time profit · 4,104 predictions · biggest win $15.1K

Weather sniper on Polymarket turned $37 → $15,182 by placing a single bet on the temperature in Hong Kong He buys YES at any price from 0.2¢ tails to 99.9¢ near-resolves wherever the market lags the forecast Forecast says Hong Kong 15°C → 8%. Market prices it at $0.002. He buys Result: $0.002 → $1.00 when it hits = +40,511% return His best trades: $37 → $15,182 (Hong Kong 15°C at 0.2¢, +40,511%) $580 → $2,270 (NYC 56-57°F at 16.8¢, +291%) $3,675 → $5,337 (London 12°C at 99.9¢, +45%) Three trades. Three different prices. All YES. All resolved $1.00 Edge ratio = (1 − q) / q At q = 0.002 → 499× per winning trade At q = 0.168 → 4.95× per trade At q = 0.999 → 0.001× per trade but with 99% hit rate Cornwall edge at 0.2¢. Simons edge at 99.9¢. Most weather traders pick one. HondaCivic runs both $56,504 all-time profit · 4,104 predictions · biggest win $15.1K

12,680 次观看

How to build your own weather trading bot Every weather trader making money on Polymarket follows the same 7 steps. Free data. Simple formula. Repeat daily Traders making $20k-$180k all started here free APIs, basic math the crowd ignores, and patience Here's the playbook: Step 1: Pick 2-3 cities Don't try to trade everything. Start with NYC, Chicago, and one international city. Learn how the forecast behaves for those exact locations. Fewer cities = faster feedback loop Start copy-trading any trader even with $10 → Step 2: Pull free forecast data Open-Meteo API free, no API key, updates every 6 hours. NOAA official US government forecasts, 85-90% accurate at 1-2 days. Windy / Ventusky, visual model viewers to sanity-check your data These are the same data sources the top weather traders use. All free. All public Step 3: Turn forecasts into probabilities The market shows 8-12 temperature buckets (25°C, 26°C, 27°C). Your job: calculate the real probability for each bucket based on the forecast Simple method: > take the forecast daily max > assume ±1.5°C error range > simulate 50,000 scenarios > count how many land in each bucket > that's your probability per bucket

How to build your own weather trading bot Every weather trader making money on Polymarket follows the same 7 steps. Free data. Simple formula. Repeat daily Traders making $20k-$180k all started here free APIs, basic math the crowd ignores, and patience Here's the playbook: Step 1: Pick 2-3 cities Don't try to trade everything. Start with NYC, Chicago, and one international city. Learn how the forecast behaves for those exact locations. Fewer cities = faster feedback loop Start copy-trading any trader even with $10 → Step 2: Pull free forecast data Open-Meteo API free, no API key, updates every 6 hours. NOAA official US government forecasts, 85-90% accurate at 1-2 days. Windy / Ventusky, visual model viewers to sanity-check your data These are the same data sources the top weather traders use. All free. All public Step 3: Turn forecasts into probabilities The market shows 8-12 temperature buckets (25°C, 26°C, 27°C). Your job: calculate the real probability for each bucket based on the forecast Simple method: > take the forecast daily max > assume ±1.5°C error range > simulate 50,000 scenarios > count how many land in each bucket > that's your probability per bucket

13,953 次观看

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Andrej Karpathy just made the most contrarian bet in autonomous AI: "Tesla has a software problem. Waymo has a hardware problem. Software problems are much easier." Waymo: ~3,500 robotaxis, 500K rides a week. Tesla: 9 million cars, all collecting data Karpathy bets the 2,500x gap decides everything ↓

Andrej Karpathy just made the most contrarian bet in autonomous AI: "Tesla has a software problem. Waymo has a hardware problem. Software problems are much easier." Waymo: ~3,500 robotaxis, 500K rides a week. Tesla: 9 million cars, all collecting data Karpathy bets the 2,500x gap decides everything ↓

99,153 次观看 • 1 个月前

Stop Guessing the Market. Start Running Simulations Most models try to predict the future. MiroFish does it differently: it simulates it p ≈ 320 / 1000 → EV = pW − (1 − p)L Instead of giving you one forecast, MiroFish builds a digital world from news, policy drafts, earnings reports, and market signals then fills it with thousands of AI agents and lets them react > They argue > They set up camps > They amplify narratives > They change their beliefs under pressure And after hundreds or thousands of runs, you don’t get a prophecy you get scenario frequencies. > If one outcome happens 320 times out of 1000, then p ≈ 0.32 From there, it becomes a decision problem: - calculate Expected Value - compare your probability to the market’s - use Kelly to size the bet correct

Stop Guessing the Market. Start Running Simulations Most models try to predict the future. MiroFish does it differently: it simulates it p ≈ 320 / 1000 → EV = pW − (1 − p)L Instead of giving you one forecast, MiroFish builds a digital world from news, policy drafts, earnings reports, and market signals then fills it with thousands of AI agents and lets them react > They argue > They set up camps > They amplify narratives > They change their beliefs under pressure And after hundreds or thousands of runs, you don’t get a prophecy you get scenario frequencies. > If one outcome happens 320 times out of 1000, then p ≈ 0.32 From there, it becomes a decision problem: - calculate Expected Value - compare your probability to the market’s - use Kelly to size the bet correct

188,393 次观看 • 5 个月前

Anthropic just released 5 Claude workshops on the new skill every AI builder needs: Loop Engineering: 00:00 - Stop babysitting your agents 37:07 - Build a proactive Claude Code loop 1:11:23 - Build a production-ready Managed Agent 1:38:46 - Teach agents to learn from your team 2:07:15 - Hill-climb your agent with eval loops It is a 2 hour and 25 minute blueprint for Loop Engineering: proactive workflows, managed agents, team feedback, evals, and self-improvement. Watch it today, and read the article below to improve your skills with Fable 5.

Anthropic just released 5 Claude workshops on the new skill every AI builder needs: Loop Engineering: 00:00 - Stop babysitting your agents 37:07 - Build a proactive Claude Code loop 1:11:23 - Build a production-ready Managed Agent 1:38:46 - Teach agents to learn from your team 2:07:15 - Hill-climb your agent with eval loops It is a 2 hour and 25 minute blueprint for Loop Engineering: proactive workflows, managed agents, team feedback, evals, and self-improvement. Watch it today, and read the article below to improve your skills with Fable 5.

47,411 次观看 • 2 个月前

1 PROMPT + 30 MINUTES = A $500/MONTH LOCAL SAAS most people spend months building apps before finding a customer. this setup starts with one problem every local business already has: getting more Google reviews. > Emergent builds the full SaaS, database, login and dashboard > automated SMS asks every customer for a review > personalized messages make each request feel human > analytics show the owner exactly how many reviews were generated > Zapier connects Square or Calendly to trigger the workflow automatically a customer pays or books. 2 hours later, the review request is sent without the owner touching anything. 1 app. 0 code. $500/month per business. the goal is not to build a billion-dollar startup. it is to solve one painful problem and sell the automated outcome. bookmark this before building your first AI SaaS

1 PROMPT + 30 MINUTES = A $500/MONTH LOCAL SAAS most people spend months building apps before finding a customer. this setup starts with one problem every local business already has: getting more Google reviews. > Emergent builds the full SaaS, database, login and dashboard > automated SMS asks every customer for a review > personalized messages make each request feel human > analytics show the owner exactly how many reviews were generated > Zapier connects Square or Calendly to trigger the workflow automatically a customer pays or books. 2 hours later, the review request is sent without the owner touching anything. 1 app. 0 code. $500/month per business. the goal is not to build a billion-dollar startup. it is to solve one painful problem and sell the automated outcome. bookmark this before building your first AI SaaS

13,949 次观看 • 2 个月前

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