正在加载视频...

视频加载失败

返回首页

Professor Eric Budish (UChicago) delivers a 1-hour masterclass completely deconstructing the exact math HFT bots use to extract millions from continuous order books. Bookmark this and watch it today, if you want to stop trading narratives and start trading architecture It will permanently change how you view markets and...

0xTengen_'s profile picture

Tengen

7,788 subscribers

287,466 次观看 • 5 个月前 •via X (Twitter)

教育科学技术财经
LIVE

Anya Rossi• Live Now

Private livecam show

0 条评论

暂无评论

原始帖子的评论将显示在这里

相关视频

Polymarket front run bot Spent the last month building the backend for this bot which essentially acts the same way that high frequency trading bots work in the stock market It waits for a market buy order to come through where there is thin liquidity in the order book meaning their market buy will push the price up of a YES/NO contract meaning that if my bot buys the contracts first then I can sell them at the high price to the market buy order before their orders have gone through Prediction markets are still early enough to find opportunities like this, just like how the early days of stock trading similar bots to this were invented It is essentially an arbitrage strategy, will make the bot public and free to a few people tomorrow so turn on notifications so you don’t miss it
1:54

Polymarket front run bot Spent the last month building the backend for this bot which essentially acts the same way that high frequency trading bots work in the stock market It waits for a market buy order to come through where there is thin liquidity in the order book meaning their market buy will push the price up of a YES/NO contract meaning that if my bot buys the contracts first then I can sell them at the high price to the market buy order before their orders have gone through Prediction markets are still early enough to find opportunities like this, just like how the early days of stock trading similar bots to this were invented It is essentially an arbitrage strategy, will make the bot public and free to a few people tomorrow so turn on notifications so you don’t miss it

Ethan

71,943 次观看 • 9 个月前

This 50-minute lecture by the scientist who built trading algorithms for Morgan Stanley and Lehman Brothers will teach you more about how machine learning actually works in algorithmic trading than most $1,000 courses ever will. Bookmark this and watch it tonight. It's the highest-leverage thing you can do if you want to understand how professional trading bots are really built. Then read the article below.
54:49

This 50-minute lecture by the scientist who built trading algorithms for Morgan Stanley and Lehman Brothers will teach you more about how machine learning actually works in algorithmic trading than most $1,000 courses ever will. Bookmark this and watch it tonight. It's the highest-leverage thing you can do if you want to understand how professional trading bots are really built. Then read the article below.

Goaty

79,842 次观看 • 5 个月前

.Solana Foundation President, Lily Liu explains why Solana is built for unified liquidity "The most important thing in finance is liquidity. No one is ever bigger than the market. The market will always win, liquidity will always win, and people will ultimately trade off every last thing in order to be part of the largest market. The idea that you could potentially create a marketplace available to 5.5 billion people, the population of people on the internet. There's no pool of liquidity which is ever going to be larger than that. That reinforces why the architecture of Solana from the beginning has been the preferred one. It prioritizes the number one thing about financial markets: unified liquidity."
1:27

.Solana Foundation President, Lily Liu explains why Solana is built for unified liquidity "The most important thing in finance is liquidity. No one is ever bigger than the market. The market will always win, liquidity will always win, and people will ultimately trade off every last thing in order to be part of the largest market. The idea that you could potentially create a marketplace available to 5.5 billion people, the population of people on the internet. There's no pool of liquidity which is ever going to be larger than that. That reinforces why the architecture of Solana from the beginning has been the preferred one. It prioritizes the number one thing about financial markets: unified liquidity."

Solana

80,061 次观看 • 5 个月前

This 20-minute tutorial breaks down how to build a Python trading bot for live NFL playoff markets, ESPN win probability API, real-time Kalshi WebSocket odds, and the honest math on latency, fees and whether the edge is even there. Bookmark it and make sure to watch. It's one of the most productive ways to level up your thinking. Then read the post.
19:20

This 20-minute tutorial breaks down how to build a Python trading bot for live NFL playoff markets, ESPN win probability API, real-time Kalshi WebSocket odds, and the honest math on latency, fees and whether the edge is even there. Bookmark it and make sure to watch. It's one of the most productive ways to level up your thinking. Then read the post.

Goaty

15,630 次观看 • 5 个月前

Basketball isn’t position-less, it was just nameless until now. If you want to understand the game and how evolution has updated then this book is for you. You want to learn how to evaluate players, you want to help your kid evolve as a player, you want to understand the history of the game, and/or get an accurate understanding of how the game and terminology called for an update then look no further. Martin Soaries is a brilliant individual that took a deep dive into my book and gives you the breakdown on how/why the basketball world needs to read this book. It’s for the greater good of the game. #TalkSoon #NBA
0:51

Basketball isn’t position-less, it was just nameless until now. If you want to understand the game and how evolution has updated then this book is for you. You want to learn how to evaluate players, you want to help your kid evolve as a player, you want to understand the history of the game, and/or get an accurate understanding of how the game and terminology called for an update then look no further. Martin Soaries is a brilliant individual that took a deep dive into my book and gives you the breakdown on how/why the basketball world needs to read this book. It’s for the greater good of the game. #TalkSoon #NBA

Rashad Phillips

43,546 次观看 • 3 年前

A Chinese MIT professor explains how financial and prediction markets actually work. Everything you need to understand both. Most people lose money in markets not because they pick wrong - but because they dont understand the structure they're playing against. Who sets prices. How liquidity works. Why derivatives exist. How market makers profit from every trade regardless of direction. This lecture explains the actual mechanics. After it you'll see every Polymarket market differently - not as a bet, but as a pricing problem with a correct answer that the crowd hasn't found yet. That gap between the crowd's price and the correct price is where money gets made. One hour. Jake Xia. MIT. Bookmark this.
35:23

A Chinese MIT professor explains how financial and prediction markets actually work. Everything you need to understand both. Most people lose money in markets not because they pick wrong - but because they dont understand the structure they're playing against. Who sets prices. How liquidity works. Why derivatives exist. How market makers profit from every trade regardless of direction. This lecture explains the actual mechanics. After it you'll see every Polymarket market differently - not as a bet, but as a pricing problem with a correct answer that the crowd hasn't found yet. That gap between the crowd's price and the correct price is where money gets made. One hour. Jake Xia. MIT. Bookmark this.

Atenov int.

25,654 次观看 • 6 个月前

The question of whether Polymarket will ban bots is not what you should be thinking about. The right question is how one Python bot manages to make $736K a week. I'm talking about the wallet SeriouslySirius. Its stats look like a glitch in the matrix 400K% ROI and over $3.8M in net profit. His profile: Here's how it works. There's no magic or gut feeling for picking winners. It's pure math and algorithms. The secret behind the strategy: It doesn't guess outcomes. The bot doesn't try to figure out who will win. It trades price differences. Only liquid markets. NFL and NBA. Huge volumes there so you can enter a $500K position without crashing the market instantly. Vegas as the benchmark. The bot is tied to Vegas bookmaker lines not to crowd emotions on Polymarket. Catching lag. This is the real juice. When a Vegas line moves by 0.5 to 1 point Polymarket lags for a few minutes. The bot sees this movement before everyone else and jumps into the trade while the platform price hasn't caught up yet. This gives 3 to 8 cents of guaranteed profit per dollar. Buying underdogs. If the crowd overvalues the favorite the bot buys the underdog below 50 cents and just waits for the market to correct the price. Will it get banned? Probably not. The bot doesn't break the system it makes it more efficient by adding liquidity. Essentially this is not a gambler but a high frequency hedge fund operating on sports events. This is an example of how Python code and understanding market mechanics beats any expert analysis. Copying this manually is almost impossible because you simply can't keep up with the robot in those 2 to 3 minutes when the opportunity window opens. But the logic itself of looking for price mismatches relative to Vegas is a solid approach. Tracking wallets that repeat this execution pattern is easier with PolyCop
0:31

The question of whether Polymarket will ban bots is not what you should be thinking about. The right question is how one Python bot manages to make $736K a week. I'm talking about the wallet SeriouslySirius. Its stats look like a glitch in the matrix 400K% ROI and over $3.8M in net profit. His profile: Here's how it works. There's no magic or gut feeling for picking winners. It's pure math and algorithms. The secret behind the strategy: It doesn't guess outcomes. The bot doesn't try to figure out who will win. It trades price differences. Only liquid markets. NFL and NBA. Huge volumes there so you can enter a $500K position without crashing the market instantly. Vegas as the benchmark. The bot is tied to Vegas bookmaker lines not to crowd emotions on Polymarket. Catching lag. This is the real juice. When a Vegas line moves by 0.5 to 1 point Polymarket lags for a few minutes. The bot sees this movement before everyone else and jumps into the trade while the platform price hasn't caught up yet. This gives 3 to 8 cents of guaranteed profit per dollar. Buying underdogs. If the crowd overvalues the favorite the bot buys the underdog below 50 cents and just waits for the market to correct the price. Will it get banned? Probably not. The bot doesn't break the system it makes it more efficient by adding liquidity. Essentially this is not a gambler but a high frequency hedge fund operating on sports events. This is an example of how Python code and understanding market mechanics beats any expert analysis. Copying this manually is almost impossible because you simply can't keep up with the robot in those 2 to 3 minutes when the opportunity window opens. But the logic itself of looking for price mismatches relative to Vegas is a solid approach. Tracking wallets that repeat this execution pattern is easier with PolyCop

Blaze

26,369 次观看 • 9 个月前

This guy built a trading bot on Polymarket with an average trade size of $8 Result: +$251,218 His bot trades 5 and 15 minute crypto Up/Down markets using a hybrid of directional trading, latency edge, and dynamic hedging: 1. It looks for edge using its own model It tracks momentum, acceleration, order flow imbalance, and spot feeds relative to the reference price of the current market Based on this data, it constantly recalculates its own fair value for Up and Down. It enters a position when it detects mispricing 2. When the signal changes, it quickly adjusts its exposure If momentum reverses, it can reduce the existing side or start accumulating the opposite outcome In 86.2% of markets, it traded both sides. The dynamic hedge constantly adjusts net exposure based on the model’s latest signal It’s Polymarket nickname: nj23adsknml3 Follow this account and trade on Polymarket through this app: It’s stats: > Trades / active hour: 516 > Average trade: $8.13 > Win Rate: 49% The main edge of this guy’s HFT algorithm comes from speed. It tries to detect changes in fair value on external crypto markets before they are reflected in Polymarket prices It then captures the temporary mispricing and quickly adjusts its directional exposure with every new market move
0:28

This guy built a trading bot on Polymarket with an average trade size of $8 Result: +$251,218 His bot trades 5 and 15 minute crypto Up/Down markets using a hybrid of directional trading, latency edge, and dynamic hedging: 1. It looks for edge using its own model It tracks momentum, acceleration, order flow imbalance, and spot feeds relative to the reference price of the current market Based on this data, it constantly recalculates its own fair value for Up and Down. It enters a position when it detects mispricing 2. When the signal changes, it quickly adjusts its exposure If momentum reverses, it can reduce the existing side or start accumulating the opposite outcome In 86.2% of markets, it traded both sides. The dynamic hedge constantly adjusts net exposure based on the model’s latest signal It’s Polymarket nickname: nj23adsknml3 Follow this account and trade on Polymarket through this app: It’s stats: > Trades / active hour: 516 > Average trade: $8.13 > Win Rate: 49% The main edge of this guy’s HFT algorithm comes from speed. It tries to detect changes in fair value on external crypto markets before they are reflected in Polymarket prices It then captures the temporary mispricing and quickly adjusts its directional exposure with every new market move

VALIX

370,823 次观看 • 1 个月前

Here is a quick tutorial on how to unlock the Ringmaster base paint. Start at this platform, grab the jetpack, and fly through the rings in this exact order. If they turn blue you did it correctly. Be sure to avoid hitting the edges of the rings or you will need to restart.
0:53

Here is a quick tutorial on how to unlock the Ringmaster base paint. Start at this platform, grab the jetpack, and fly through the rings in this exact order. If they turn blue you did it correctly. Be sure to avoid hitting the edges of the rings or you will need to restart.

Phill Snerds

121,619 次观看 • 4 个月前

This guy built an HFT algorithm on Polymarket with an average trade size of $23.59 Result: +$246,578 The bot trades short-term crypto Up/Down markets using dynamic hedging and a directional residual: 1. First buys the outcome its model considers underpriced It continuously estimates the fair probabilities of Up and Down and compares them with prices on Polymarket The bot builds a position whenever it detects a mispricing 2. Buys the opposite side when the signal changes If the underlying asset reverses, it does not close its initial position. Instead, it starts accumulating the opposite outcome It bought both sides in 90% of markets 3. Turns part of the position into complete sets below $1 In two-sided markets, the median combined cost of Up and Down is 94.5c But this is not pure arbitrage The median inventory imbalance is 40%, so after pairing both sides, the algorithm is left with a directional residual Its Polymarket account: pspspsps5 You can build your own trading bot here: (A trial is available after signing up) The main edge of this HFT algorithm comes from continuously shifting directional exposure as its fair probability estimates change, while purchases of the opposite outcome function either as a hedge or as part of a complete set
0:27

This guy built an HFT algorithm on Polymarket with an average trade size of $23.59 Result: +$246,578 The bot trades short-term crypto Up/Down markets using dynamic hedging and a directional residual: 1. First buys the outcome its model considers underpriced It continuously estimates the fair probabilities of Up and Down and compares them with prices on Polymarket The bot builds a position whenever it detects a mispricing 2. Buys the opposite side when the signal changes If the underlying asset reverses, it does not close its initial position. Instead, it starts accumulating the opposite outcome It bought both sides in 90% of markets 3. Turns part of the position into complete sets below $1 In two-sided markets, the median combined cost of Up and Down is 94.5c But this is not pure arbitrage The median inventory imbalance is 40%, so after pairing both sides, the algorithm is left with a directional residual Its Polymarket account: pspspsps5 You can build your own trading bot here: (A trial is available after signing up) The main edge of this HFT algorithm comes from continuously shifting directional exposure as its fair probability estimates change, while purchases of the opposite outcome function either as a hedge or as part of a complete set

VALIX

77,671 次观看 • 16 天前

I wanted to clarify about the changes around the stop/limit rules First of all, here are the following changes listed below 1. Forex (Foreign Exchange): 5 pips For example, if you're trading the EUR/ USD pair and the minimum limit/ stop distance is 5 pips, you would need to set your limit or stop order at least 5 pips away from the current market price. 1. Indices: 7.50 USD For instance, if you are trading a stock index like the S&P 500 and the minimum limit/ stop distance is 7.50 USD , you would set your limit or stop order with a minimum dollar value of 7.50 away from the current market price. 1. Gold/ Commodities: 1 USD As an example, if you are trading Gold and the minimum limit/ stop distance is 1 USD, your limit or stop order should be placed at least 1 dollar away from the current market price. These changes are necessary for us to maintain the integrity of our platform and simulated trading environments. However, to clarify, this does NOT mean you need to TP or SL at 5 pips Look at the video below, as traders can still implement 2 pip stop losses and take profits if they choose.
7:32

I wanted to clarify about the changes around the stop/limit rules First of all, here are the following changes listed below 1. Forex (Foreign Exchange): 5 pips For example, if you're trading the EUR/ USD pair and the minimum limit/ stop distance is 5 pips, you would need to set your limit or stop order at least 5 pips away from the current market price. 1. Indices: 7.50 USD For instance, if you are trading a stock index like the S&P 500 and the minimum limit/ stop distance is 7.50 USD , you would set your limit or stop order with a minimum dollar value of 7.50 away from the current market price. 1. Gold/ Commodities: 1 USD As an example, if you are trading Gold and the minimum limit/ stop distance is 1 USD, your limit or stop order should be placed at least 1 dollar away from the current market price. These changes are necessary for us to maintain the integrity of our platform and simulated trading environments. However, to clarify, this does NOT mean you need to TP or SL at 5 pips Look at the video below, as traders can still implement 2 pip stop losses and take profits if they choose.

Omar Agag

58,399 次观看 • 2 年前

This is a classic case of latency arbitrage. It's more prevalent in the US than India Reason being -;Unlike India, where liquidity is concentrated on NSE, the US market is fragmented across multiple exchanges: NYSE, Nasdaq, Cboe, MEMX, and others. US brokers also have a Best Execution obligation. If an institution wants to buy 1 million Apple shares, its smart order router may split the order across several exchanges to obtain the best available price. What happens after that is how HFTs make millions from pennies: - The first part of the order reaches NYSE and starts moving the price - HFT firm's systems detect that execution within microseconds. - They don't know where the remaining order has gone, but they know how institutional smart order routers usually behave - Based on probability, they guess that the rest of the order is likely heading to Nasdaq, Cboe, MEMX, etc. - They race to those exchanges first. Thanks to there high speed infrastructure. Buy the remaining liquidity, and sell it back moments later at a slightly higher price (probably difference of pennies) They do this millions of times a day 🙄
1:05

This is a classic case of latency arbitrage. It's more prevalent in the US than India Reason being -;Unlike India, where liquidity is concentrated on NSE, the US market is fragmented across multiple exchanges: NYSE, Nasdaq, Cboe, MEMX, and others. US brokers also have a Best Execution obligation. If an institution wants to buy 1 million Apple shares, its smart order router may split the order across several exchanges to obtain the best available price. What happens after that is how HFTs make millions from pennies: - The first part of the order reaches NYSE and starts moving the price - HFT firm's systems detect that execution within microseconds. - They don't know where the remaining order has gone, but they know how institutional smart order routers usually behave - Based on probability, they guess that the rest of the order is likely heading to Nasdaq, Cboe, MEMX, etc. - They race to those exchanges first. Thanks to there high speed infrastructure. Buy the remaining liquidity, and sell it back moments later at a slightly higher price (probably difference of pennies) They do this millions of times a day 🙄

Azhar Jafri

14,352 次观看 • 2 个月前

After 1,000+ trades, this is the only setup that consistently works in any market condition. It's called liquidity sweep reversal—and it's the highest probability trading strategy I know. Before I show you what it is, here are the two things most traders get wrong with it: 1) They enter too early and get stopped out on the second sweep. 2) They try to predict the LAST sweep with certainty This is a sure-fire way to burn your money. Here's what to do instead: Step 1: Identify market control Look at structure. Higher highs and higher lows? Buyers are in control. We're only trading from demand zones. Step 2: Mark your liquidity zones Find equal lows. When retail sees a "double bottom," they go long because textbooks tell them to. Their stop losses sit right below those lows. Available liquidity for institutions to sweep. Step 3: Wait for the sweep Price drops, sweeps those stops, liquidates retail traders, then creates a sharp V-shaped reaction. This sweep breaks structure. Zoom into 1-hour timeframe - price was making lower highs and lows. After the sweep? Higher highs and higher lows. That sweep zone becomes your institutional demand zone. Step 4: Enter on mitigation Wait for price to pull back to the liquidity zone. Enter there. Stop below the zone. Target 2-3R. Remember: You'll NEVER predict with 100% certainty when it's the LAST liquidity sweep. Sometimes price sweeps 2-3 times before the real move. But that's trading—we trade probabilities, not certainties. Also, keep in mind: If you can't spot the liquidity, you ARE the liquidity. — This is just a breakdown of one of the trading strategies we covered in our 2-hour long cryptocurrency trading course. I also discussed the trend pullback strategy, how to trade breakout retests without getting stopped out on fake moves, and why understanding liquidity is the only way to avoid becoming exit liquidity. Just comment "COURSE" and I'll DM it to you immediately so you can watch it.
7:25

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

The Trading Geek (Brad Goh)

54,339 次观看 • 9 个月前

The sports bot is still cooking This bot made $400k in 7 days This is the same Polymarket bot I mentioned before The one that printed over $4,000,000 last month, and it hasn’t slowed down RIGHT NOW - he is loading up aggressively on NBA Finals markets - odds there are clearly mispriced trader 'DrPufferfish' - $500k in active positions - 740 predictions total - over $4,000,000 in PnL profile: WHAT GIVES THIS BOT AN EDGE? It understood something early: sports markets on Polymarket are structurally broken It doesn’t predict outcomes. It abuses the order book The bot scans every live sports market, looks for wide spreads, and buys where liquidity is sloppy No waiting. Just execution That’s how these numbers happen Not magic. Not luck Just someone who realized where the edge actually is and took it before everyone else caught on
0:39

The sports bot is still cooking This bot made $400k in 7 days This is the same Polymarket bot I mentioned before The one that printed over $4,000,000 last month, and it hasn’t slowed down RIGHT NOW - he is loading up aggressively on NBA Finals markets - odds there are clearly mispriced trader 'DrPufferfish' - $500k in active positions - 740 predictions total - over $4,000,000 in PnL profile: WHAT GIVES THIS BOT AN EDGE? It understood something early: sports markets on Polymarket are structurally broken It doesn’t predict outcomes. It abuses the order book The bot scans every live sports market, looks for wide spreads, and buys where liquidity is sloppy No waiting. Just execution That’s how these numbers happen Not magic. Not luck Just someone who realized where the edge actually is and took it before everyone else caught on

gavelsv.patron

14,474 次观看 • 9 个月前

this is pure f*cking AI treasure 5 GitHub repos that give you the trading algo Wall Street pays millions dollars: want the engine serious quants run in production? -> NautilusTrader: a Rust-native trading engine where the backtest and the live run use the same code want a full research team arguing over every trade? -> TradingAgents: a multi-agent trading firm in one repo. analysts, researchers, a risk desk want the data terminal without the terminal bill? -> OpenBB: an open data platform for analysts, quants and AI agents want a bot that actually places the orders? -> freqtrade: free, open-source crypto trading bot with backtesting built in want all of it in one place? -> QuantDinger: an AI trading OS. research, Python strategies, backtest, paper and live trading ~282k stars of trading infrastructure. $0 in licences. now the part worth trying. every one of these has the same slow spot: the moment before the order, where something has to say yes or no. that's where Jev goes. it can't write a word. it returns BUY / SELL / HOLD with a probability in 70 to 500 milliseconds. QuantDinger already ships it as the pre-trade gate. and one repo, jev-trader, asks it buy or sell every ~300 ms and quotes a limit order on every single block. the stack in one line: OpenBB brings the data, the agents do the research, Jev makes the call, the bot places the order. the catch: fast is not the same as right. none of this is an ATM. paper trade it first. Jev + Claude + these 5 is the setup to test this month. check each repo before you install. save this.
0:27

this is pure f*cking AI treasure 5 GitHub repos that give you the trading algo Wall Street pays millions dollars: want the engine serious quants run in production? -> NautilusTrader: a Rust-native trading engine where the backtest and the live run use the same code want a full research team arguing over every trade? -> TradingAgents: a multi-agent trading firm in one repo. analysts, researchers, a risk desk want the data terminal without the terminal bill? -> OpenBB: an open data platform for analysts, quants and AI agents want a bot that actually places the orders? -> freqtrade: free, open-source crypto trading bot with backtesting built in want all of it in one place? -> QuantDinger: an AI trading OS. research, Python strategies, backtest, paper and live trading ~282k stars of trading infrastructure. $0 in licences. now the part worth trying. every one of these has the same slow spot: the moment before the order, where something has to say yes or no. that's where Jev goes. it can't write a word. it returns BUY / SELL / HOLD with a probability in 70 to 500 milliseconds. QuantDinger already ships it as the pre-trade gate. and one repo, jev-trader, asks it buy or sell every ~300 ms and quotes a limit order on every single block. the stack in one line: OpenBB brings the data, the agents do the research, Jev makes the call, the bot places the order. the catch: fast is not the same as right. none of this is an ATM. paper trade it first. Jev + Claude + these 5 is the setup to test this month. check each repo before you install. save this.

kiosa

22,061 次观看 • 2 天前

this is absolutely f*cking insane Black Rock just leaked 5 GitHub repos that give you the trading desk a fund pays millions for: want a full research team arguing over every trade? -> TradingAgents: a multi-agent trading firm in one repo. analysts, researchers, a risk desk want the data terminal without the terminal bill? -> OpenBB: an open data platform for analysts, quants and AI agents want a bot that actually places the orders? -> freqtrade: free, open-source crypto trading bot with backtesting built in want the engine serious quants run in production? -> NautilusTrader: a Rust-native trading engine where the backtest and the live run use the same code want all of it in one place? -> QuantDinger: an AI trading OS. research, Python strategies, backtest, paper and live trading ~282k stars of trading infrastructure. $0 in licences. now the part worth trying. every one of these has the same slow spot: the moment before the order, where something has to say yes or no. that's where Jev goes. it can't write a word. it returns BUY / SELL / HOLD with a probability in 70 to 500 milliseconds. QuantDinger already ships it as the pre-trade gate. and one repo, jev-trader, asks it buy or sell every ~300 ms and quotes a limit order on every single block. the stack in one line: OpenBB brings the data, the agents do the research, Jev makes the call, the bot places the order. the catch: fast is not the same as right. none of this is an ATM. paper trade it first. Jev + these five is the setup to test this month. bookmark and share with a friend
0:25

this is absolutely f*cking insane Black Rock just leaked 5 GitHub repos that give you the trading desk a fund pays millions for: want a full research team arguing over every trade? -> TradingAgents: a multi-agent trading firm in one repo. analysts, researchers, a risk desk want the data terminal without the terminal bill? -> OpenBB: an open data platform for analysts, quants and AI agents want a bot that actually places the orders? -> freqtrade: free, open-source crypto trading bot with backtesting built in want the engine serious quants run in production? -> NautilusTrader: a Rust-native trading engine where the backtest and the live run use the same code want all of it in one place? -> QuantDinger: an AI trading OS. research, Python strategies, backtest, paper and live trading ~282k stars of trading infrastructure. $0 in licences. now the part worth trying. every one of these has the same slow spot: the moment before the order, where something has to say yes or no. that's where Jev goes. it can't write a word. it returns BUY / SELL / HOLD with a probability in 70 to 500 milliseconds. QuantDinger already ships it as the pre-trade gate. and one repo, jev-trader, asks it buy or sell every ~300 ms and quotes a limit order on every single block. the stack in one line: OpenBB brings the data, the agents do the research, Jev makes the call, the bot places the order. the catch: fast is not the same as right. none of this is an ATM. paper trade it first. Jev + these five is the setup to test this month. bookmark and share with a friend

kiosa

27,944 次观看 • 3 天前

——— and earn passive income ——— $CODED now has a decentralized liquidity pool on the QSWAP AMM on $QUBIC, allowing anyone to earn passive income from swap fees by providing liquidity. > 0,3% total trading fees on QSWAP >> 60% (0,18%) of them goes to LPs Each LP receives a share of that 0.18% proportionally to their share of the total liquidity in the pool >> Here is how to add liquidity >> // 1. Make sure you have $QUBIC and $CODED tokens in your mobile wallet // 2. Connect your wallet to using the wallet connect // 3. >> Use the “Switch Asset SC” transfer function to transfer $CODED, if not already, from the QX Smart Contract to the QSWAP Smart Contract // 4. >> Move to “add liquidity”, choose the $CODED pool, Insert the amount of liquidity you want to add Make sure you match the current pool ratio to avoid slippage. (In this example ~ 1:20) >>Desired $CODED Amount: 20M >>Desired $QUBIC Amount: 1M >>Minimum $CODED Amount: 19M >>Minimum $QUBIC Amount: 1M Choose a slightly lower “minimum $CODED Amount” till you get a correct liquidity simulation, press “add liquidity”, and sign it in your wallet. // 5. >> Move to pools and check the amount of your “own liquidity”. Based on this you can calculate your percentage share of the pool. You always can remove the liquidity from the pool and get $QUBIC and $CODED tokens back in your wallet. As more people add liquidity as more the price stabilizes. 0.01 - $CODED
1:54

——— and earn passive income ——— $CODED now has a decentralized liquidity pool on the QSWAP AMM on $QUBIC, allowing anyone to earn passive income from swap fees by providing liquidity. > 0,3% total trading fees on QSWAP >> 60% (0,18%) of them goes to LPs Each LP receives a share of that 0.18% proportionally to their share of the total liquidity in the pool >> Here is how to add liquidity >> // 1. Make sure you have $QUBIC and $CODED tokens in your mobile wallet // 2. Connect your wallet to using the wallet connect // 3. >> Use the “Switch Asset SC” transfer function to transfer $CODED, if not already, from the QX Smart Contract to the QSWAP Smart Contract // 4. >> Move to “add liquidity”, choose the $CODED pool, Insert the amount of liquidity you want to add Make sure you match the current pool ratio to avoid slippage. (In this example ~ 1:20) >>Desired $CODED Amount: 20M >>Desired $QUBIC Amount: 1M >>Minimum $CODED Amount: 19M >>Minimum $QUBIC Amount: 1M Choose a slightly lower “minimum $CODED Amount” till you get a correct liquidity simulation, press “add liquidity”, and sign it in your wallet. // 5. >> Move to pools and check the amount of your “own liquidity”. Based on this you can calculate your percentage share of the pool. You always can remove the liquidity from the pool and get $QUBIC and $CODED tokens back in your wallet. As more people add liquidity as more the price stabilizes. 0.01 - $CODED

CODED

13,545 次观看 • 11 个月前

I gave Claude Fable 5 just $100 to test the simplest arbitrage. Left my laptop opened for 24 hours. Session PnL: clean $1,571.66 On Polymarket, YES + NO Aren't there thousands of arbitrage bots already? Absolutely. You're not competing against people. You're competing against infrastructure. The biggest improvement wasn't a new strategy. It was rebuilding the entire execution pipeline. The bot runs on a VPS close to the APIs it talks to instead of my local machine. It keeps persistent connections open instead of reconnecting every request. Market metadata is cached in memory so it only refreshes live prices. Orders are prepared before the opportunity exists. When YES + NO finally drops below $1, there's almost nothing left to calculate. It checks liquidity, order book and submits both legs immediately. Will that beat firms spending millions on low-latency infrastructure? Probably not. It doesn't have to. There are hundreds of active prediction markets, constant volatility and plenty of opportunities that last long enough for a well-engineered bot to compete. That's probably the biggest thing Claude Fable 5 changed for me. I stopped asking it to build me a trading bot and started asking where am I wasting milliseconds. That single shift made the bot noticeably more competitive than any new trading strategy I could have invented. Arbitrage is the king. Irt lives everywhere. The only question is are you ready to study it. Leaving a full build + latency tips guide below this post.
0:46

I gave Claude Fable 5 just $100 to test the simplest arbitrage. Left my laptop opened for 24 hours. Session PnL: clean $1,571.66 On Polymarket, YES + NO Aren't there thousands of arbitrage bots already? Absolutely. You're not competing against people. You're competing against infrastructure. The biggest improvement wasn't a new strategy. It was rebuilding the entire execution pipeline. The bot runs on a VPS close to the APIs it talks to instead of my local machine. It keeps persistent connections open instead of reconnecting every request. Market metadata is cached in memory so it only refreshes live prices. Orders are prepared before the opportunity exists. When YES + NO finally drops below $1, there's almost nothing left to calculate. It checks liquidity, order book and submits both legs immediately. Will that beat firms spending millions on low-latency infrastructure? Probably not. It doesn't have to. There are hundreds of active prediction markets, constant volatility and plenty of opportunities that last long enough for a well-engineered bot to compete. That's probably the biggest thing Claude Fable 5 changed for me. I stopped asking it to build me a trading bot and started asking where am I wasting milliseconds. That single shift made the bot noticeably more competitive than any new trading strategy I could have invented. Arbitrage is the king. Irt lives everywhere. The only question is are you ready to study it. Leaving a full build + latency tips guide below this post.

Oracle Boar

18,091 次观看 • 2 个月前

NFT distributor -> Liquidity distributor Meteora distributes liqudity, not JPEGs, hence why the rename. If you still got questions about the tech, get your answers in this quick summary by Soju 燒酒 | funemployed Questions addressed: - 10% of MET supply in liquidity distributor, and how to claim it - Liquidity distributor mechanics and initial pool breakdown - What to do if you want to use your own strategy? - Vesting and lock-up for liquidity distributor positions
1:28

NFT distributor -> Liquidity distributor Meteora distributes liqudity, not JPEGs, hence why the rename. If you still got questions about the tech, get your answers in this quick summary by Soju 燒酒 | funemployed Questions addressed: - 10% of MET supply in liquidity distributor, and how to claim it - Liquidity distributor mechanics and initial pool breakdown - What to do if you want to use your own strategy? - Vesting and lock-up for liquidity distributor positions

LP Army

38,668 次观看 • 1 年前