Video wird geladen...

Video konnte nicht geladen werden

Beim Laden dieses Videos ist ein Problem aufgetreten. Dies könnte an einem vorübergehenden Netzwerkproblem liegen oder das Video ist möglicherweise nicht verfügbar.

Latency Arbitrage Explained!📈 Latency arbitrage is a trading strategy that exploits tiny differences in how quickly market information reaches different exchanges or participants. If the price of a stock moves on one venue but another venue hasn’t updated yet, a faster trader can detect the change and trade against... show more

julia

34,900 subscribers

23,500 Aufrufe • vor 2 Monaten •via TikTok

Finanzen #finance #quantfinance #stock #money

Anya Rossi• Live Now

Private livecam show

0 Kommentare

Keine Kommentare verfügbar

Kommentare vom Original-Post werden hier angezeigt

Ähnliche Videos

CryptoCopy beta is live! The solution allows you to trade on multiple exchanges at the same time. Over 20 top exchanges will be available in total. You can apply here. In the video, you can see an example of copying a trade from a #Bybit futures account to #Binance and #Bitget exchanges. In this example, it is clear how opening a trade on one exchange immediately executes the trade on the other exchanges! This is essential for diversifying your funds across different exchanges to reduce non-trading risks, improve execution, and enhance liquidity, especially when trading altcoins. Copying trades via CryptoCopy occurs at a speed of 30 milliseconds, faster than what it takes for exchanges to execute your market order!

CryptoCopy beta is live! The solution allows you to trade on multiple exchanges at the same time. Over 20 top exchanges will be available in total. You can apply here. In the video, you can see an example of copying a trade from a #Bybit futures account to #Binance and #Bitget exchanges. In this example, it is clear how opening a trade on one exchange immediately executes the trade on the other exchanges! This is essential for diversifying your funds across different exchanges to reduce non-trading risks, improve execution, and enhance liquidity, especially when trading altcoins. Copying trades via CryptoCopy occurs at a speed of 30 milliseconds, faster than what it takes for exchanges to execute your market order!

CryptoCopy

105,761 Aufrufe • vor 2 Jahren

A CHINESE STUDENT CREATED A TRADING BOT THAT TURNED $0.90 INTO $408,000 IN 2 DAYS 🤯 The bot scanned decentralized exchanges for tiny price differences and executed trades in milliseconds, moving faster than any human trader possibly could. That's what makes on-chain arbitrage so interesting. In crypto markets, prices across platforms don't always stay perfectly aligned, especially during volatile periods. Bots can exploit those gaps automatically, buying low on one exchange and selling higher on another almost instantly. The bigger story isn't only the profit. It's how software, automation, and blockchain infrastructure are creating entirely new forms of financial competition where speed, code, and execution matter more than human reaction time.

A CHINESE STUDENT CREATED A TRADING BOT THAT TURNED $0.90 INTO $408,000 IN 2 DAYS 🤯 The bot scanned decentralized exchanges for tiny price differences and executed trades in milliseconds, moving faster than any human trader possibly could. That's what makes on-chain arbitrage so interesting. In crypto markets, prices across platforms don't always stay perfectly aligned, especially during volatile periods. Bots can exploit those gaps automatically, buying low on one exchange and selling higher on another almost instantly. The bigger story isn't only the profit. It's how software, automation, and blockchain infrastructure are creating entirely new forms of financial competition where speed, code, and execution matter more than human reaction time.

Crypto with Haris ₿

18,235 Aufrufe • vor 5 Monaten

➡️ Quantitative Finance from Scratch, part 45: Statistical Arbitrage. The arbitrage from the last part was a profit without any risk. Statistical arbitrage is the practical version: many small bets that can each lose, but win on average. One of the best known and simplest forms is pairs trading: two stocks in the same business that move together. The gap between them, the spread = P_A − β · P_B (P_A price of stock A, P_B price of stock B, β the hedge ratio), keeps coming back to its average. That is the edge: the trade treats this return almost like a law, so the further the spread moves away, the more it should come back, and that way back is the profit. The rule: z = (spread − average) / standard deviation. When z reaches +2 or −2, you sell the expensive stock, buy the cheap one, and close both when the gap is back at its average. Because one stock is long and the other short, a move of the whole market mostly cancels out. News about one company can still hit a single pair, so funds make many such trades at once, and the surprises average out. The big risks: the link between two stocks can break for good, for example after a merger, and when many funds hold the same trades, they can all lose at once. The code (statarb_reel.py, 16 lines of plain Python without packages) is general on purpose: the spread from two prices and the hedge ratio, the z score, and the signal that sells the expensive stock and buys the cheap one at two standard deviations and closes at the average. If you want to go deeper, the link to my website is in my bio. #quantfinance #statisticalarbitrage #pairstrading #meanreversion #python Education only, not financial advice.

➡️ Quantitative Finance from Scratch, part 45: Statistical Arbitrage. The arbitrage from the last part was a profit without any risk. Statistical arbitrage is the practical version: many small bets that can each lose, but win on average. One of the best known and simplest forms is pairs trading: two stocks in the same business that move together. The gap between them, the spread = P_A − β · P_B (P_A price of stock A, P_B price of stock B, β the hedge ratio), keeps coming back to its average. That is the edge: the trade treats this return almost like a law, so the further the spread moves away, the more it should come back, and that way back is the profit. The rule: z = (spread − average) / standard deviation. When z reaches +2 or −2, you sell the expensive stock, buy the cheap one, and close both when the gap is back at its average. Because one stock is long and the other short, a move of the whole market mostly cancels out. News about one company can still hit a single pair, so funds make many such trades at once, and the surprises average out. The big risks: the link between two stocks can break for good, for example after a merger, and when many funds hold the same trades, they can all lose at once. The code (statarb_reel.py, 16 lines of plain Python without packages) is general on purpose: the spread from two prices and the hedge ratio, the z score, and the signal that sells the expensive stock and buys the cheap one at two standard deviations and closes at the average. If you want to go deeper, the link to my website is in my bio. #quantfinance #statisticalarbitrage #pairstrading #meanreversion #python Education only, not financial advice.

QuantFinanceToGo

37,500 Aufrufe • vor 3 Tagen

Kairos Open Beta is here. Your edge is how quickly you turn information into a trade. Live market data, satellite data feeds, and ultra-fast sports updates, alongside best-price execution across venues. From spotting the opportunity to trading it, all in one terminal. Trade on Kairos.

Kairos Open Beta is here. Your edge is how quickly you turn information into a trade. Live market data, satellite data feeds, and ultra-fast sports updates, alongside best-price execution across venues. From spotting the opportunity to trading it, all in one terminal. Trade on Kairos.

Kairos

53,575 Aufrufe • vor 9 Tagen

Prediction markets are inefficient. We detect price discrepancies across platforms and execute arbitrage in milliseconds. 400ms execution on Solana. Fully automated. 24/7. PolySOL Trading !

Prediction markets are inefficient. We detect price discrepancies across platforms and execute arbitrage in milliseconds. 400ms execution on Solana. Fully automated. 24/7. PolySOL Trading !

PolySOL Trading

31,036 Aufrufe • vor 7 Monaten

Introducing Trailing Stoploss on Kite web. A regular stop loss stays fixed at the price you set. This works when you enter a trade, but it can become a problem if the trade moves in your favour. Say you buy a stock at ₹100 and set a stop loss at ₹90. The stock then moves to ₹140, but your stop loss remains at ₹90. If the price reverses, you could give back a large part of the move. The obvious solution is to keep moving the stop loss higher manually. But once the trade is live, it is easy to delay the decision, second-guess the move, or miss the price action altogether. With trailing stoploss, this adjustment happens automatically. The stop-loss trigger moves as the trade moves in your direction and stays unchanged when the price moves against you. Here's all you need to know👇

Introducing Trailing Stoploss on Kite web. A regular stop loss stays fixed at the price you set. This works when you enter a trade, but it can become a problem if the trade moves in your favour. Say you buy a stock at ₹100 and set a stop loss at ₹90. The stock then moves to ₹140, but your stop loss remains at ₹90. If the price reverses, you could give back a large part of the move. The obvious solution is to keep moving the stop loss higher manually. But once the trade is live, it is easy to delay the decision, second-guess the move, or miss the price action altogether. With trailing stoploss, this adjustment happens automatically. The stop-loss trigger moves as the trade moves in your direction and stays unchanged when the price moves against you. Here's all you need to know👇

Zerodha

48,768 Aufrufe • vor 4 Monaten

An order book is a real-time list of all the buy and sell orders waiting to be filled for a stock, crypto, or other traded asset. Level 1 data shows the highest bid and lowest ask, along with their sizes. Level 2 data goes deeper, showing multiple price levels of bids and asks and how much is available at each price, giving you a better picture of market depth. Level 3 data is even more detailed, showing the individual orders that make up those price levels, often including information such as order size, position in the queue, and order updates. Together, these levels provide increasingly detailed views of supply, demand, liquidity, and how orders are positioned in the market. #finance #quantfinance #quant #optionstrading #stock

An order book is a real-time list of all the buy and sell orders waiting to be filled for a stock, crypto, or other traded asset. Level 1 data shows the highest bid and lowest ask, along with their sizes. Level 2 data goes deeper, showing multiple price levels of bids and asks and how much is available at each price, giving you a better picture of market depth. Level 3 data is even more detailed, showing the individual orders that make up those price levels, often including information such as order size, position in the queue, and order updates. Together, these levels provide increasingly detailed views of supply, demand, liquidity, and how orders are positioned in the market. #finance #quantfinance #quant #optionstrading #stock

julia

47,600 Aufrufe • vor 1 Monat

This guy built a trading bot with Claude and made $87,000 on Polymarket His bot trades the microstructure of short-term crypto Up/Down markets using high-frequency scalping, with elements of swing arbitrage and hedged trading His strategy is simple: > Compares the underlying asset price with the price on Polymarket and trades mispricings > First buys one side, then after the price moves, buys the opposite side so that Up + Down stays below $1 > Uses asymmetric sizing: small size on cheap tails, while most of the volume goes into the 60-97c range This guy’s Polymarket account: Using this strategy and clean execution, he turned $19.7k into $87.1k in one month of trading

This guy built a trading bot with Claude and made $87,000 on Polymarket His bot trades the microstructure of short-term crypto Up/Down markets using high-frequency scalping, with elements of swing arbitrage and hedged trading His strategy is simple: > Compares the underlying asset price with the price on Polymarket and trades mispricings > First buys one side, then after the price moves, buys the opposite side so that Up + Down stays below $1 > Uses asymmetric sizing: small size on cheap tails, while most of the volume goes into the 60-97c range This guy’s Polymarket account: Using this strategy and clean execution, he turned $19.7k into $87.1k in one month of trading

VALIX

21,254 Aufrufe • vor 4 Monaten

$💰 A TRADER just ROBBED POLYMARKET for $600,000! Started with ONLY $500 and EXPLOITED the BITCOIN PRICE DELAY - placing bets BEFORE the odds UPDATED! $500 → $600K in hours PURE GENIUS HACK! Who’s NEXT?! Trader Turns $500 into $600,000 on Polymarket in Hours – The Bitcoin Price Delay “Life Hack” That’s Going Viral A mysterious trader may have just pulled off what many in the crypto world are calling the ultimate prediction‑market coup — turning a modest $500 into roughly $600,000 in a single session by exploiting a subtle timing gap in Bitcoin price updates on Polymarket. Social posts that have erupted across X today describe how the trader repeatedly capitalised on a tiny latency in Polymarket’s short‑term Bitcoin markets, placing bets a split second before the platform adjusted prices to reflect real‑time market moves. How the “Glitch in the Matrix” Worked Polymarket’s popular ultra‑short “Bitcoin Up or Down” markets, which resolve in 5‑ or 15‑minute intervals, pull price data from major exchanges such as Binance. There is a slight delay — typically a few hundred milliseconds to a couple of seconds — between live exchange price movements and when those changes are reflected in Polymarket’s odds. According to observers, the trader (widely believed to be operating an automated bot) exploited this delay by watching the live price on external feeds, submitting bets on the direction of Bitcoin before Polymarket’s internal prices had fully adjusted, and repeating the process many times in rapid succession. One viral post captured the dynamic this way: “He was seeing the move a fraction of a second earlier, basically seeing the future. He entered positions before Polymarket odds could adjust. Over and over again. Not luck. Just speed.” Another read: “This is a man who found a glitch in the matrix and ran it until someone noticed.” Screenshots and wallet growth charts circulating online show an account balance rocketing from an initial $500 to mid‑six figures over a few hours, a dramatic ascent that has captured widespread attention. Not the First Time — But Still Wildly Profitable Polymarket has previously acknowledged the challenges of ultra‑short crypto markets and in early 2026 introduced dynamic fees intended to reduce latency arbitrage opportunities. The recent run suggests either the fix was incomplete or the trader has found a new way to capture fleeting price disparities faster than the protocol can adjust. Importantly, this was not a smart‑contract exploit or a rug pull. The activity described aligns with classic high‑frequency trading principles: exploit microsecond advantages in price information and execution. On Wall Street, quant funds have built entire businesses around similar edges. Here, that edge allegedly appeared accessible to an individual with sufficiently fast infrastructure. The Internet’s Reaction The story has ignited discussion across X, with both Russian and English accounts sharing wallet screenshots and short videos of the trades. Commentary ranges from admiration to criticism of the Polymarket mechanism. One widely shared post states: “A trader found a loophole and robbed the bookmaker Polymarket for $600,000. He exploited the delay in Bitcoin price updates… Started with just $500.” Replies to that thread span “genius” and “Polymarket is cooked,” reflecting a split between those whispering admiration for the execution and those questioning the sustainability of the platform’s design. Whether Polymarket will attempt to retroactively claw back profits, adjust rules or simply patch the vulnerability again remains unsettled. For the moment, the anonymous trader is alternately being hailed as a folk hero and criticised as a parasite of an imperfect system. Moral of the Story In prediction markets where time and information intersect, speed can be money. Today’s episode may turn into a cautionary tale about the refinement of oracle feeds and the race to eliminate latency advantages — or it may become part of crypto folklore, a dramatic demonstration of how imperfect markets can be bent by those who read the technical seams most closely.$

💰 A TRADER just ROBBED POLYMARKET for $600,000! Started with ONLY $500 and EXPLOITED the BITCOIN PRICE DELAY - placing bets BEFORE the odds UPDATED! $500 → $600K in hours PURE GENIUS HACK! Who’s NEXT?! Trader Turns $500 into $600,000 on Polymarket in Hours – The Bitcoin Price Delay “Life Hack” That’s Going Viral A mysterious trader may have just pulled off what many in the crypto world are calling the ultimate prediction‑market coup — turning a modest $500 into roughly $600,000 in a single session by exploiting a subtle timing gap in Bitcoin price updates on Polymarket. Social posts that have erupted across X today describe how the trader repeatedly capitalised on a tiny latency in Polymarket’s short‑term Bitcoin markets, placing bets a split second before the platform adjusted prices to reflect real‑time market moves. How the “Glitch in the Matrix” Worked Polymarket’s popular ultra‑short “Bitcoin Up or Down” markets, which resolve in 5‑ or 15‑minute intervals, pull price data from major exchanges such as Binance. There is a slight delay — typically a few hundred milliseconds to a couple of seconds — between live exchange price movements and when those changes are reflected in Polymarket’s odds. According to observers, the trader (widely believed to be operating an automated bot) exploited this delay by watching the live price on external feeds, submitting bets on the direction of Bitcoin before Polymarket’s internal prices had fully adjusted, and repeating the process many times in rapid succession. One viral post captured the dynamic this way: “He was seeing the move a fraction of a second earlier, basically seeing the future. He entered positions before Polymarket odds could adjust. Over and over again. Not luck. Just speed.” Another read: “This is a man who found a glitch in the matrix and ran it until someone noticed.” Screenshots and wallet growth charts circulating online show an account balance rocketing from an initial $500 to mid‑six figures over a few hours, a dramatic ascent that has captured widespread attention. Not the First Time — But Still Wildly Profitable Polymarket has previously acknowledged the challenges of ultra‑short crypto markets and in early 2026 introduced dynamic fees intended to reduce latency arbitrage opportunities. The recent run suggests either the fix was incomplete or the trader has found a new way to capture fleeting price disparities faster than the protocol can adjust. Importantly, this was not a smart‑contract exploit or a rug pull. The activity described aligns with classic high‑frequency trading principles: exploit microsecond advantages in price information and execution. On Wall Street, quant funds have built entire businesses around similar edges. Here, that edge allegedly appeared accessible to an individual with sufficiently fast infrastructure. The Internet’s Reaction The story has ignited discussion across X, with both Russian and English accounts sharing wallet screenshots and short videos of the trades. Commentary ranges from admiration to criticism of the Polymarket mechanism. One widely shared post states: “A trader found a loophole and robbed the bookmaker Polymarket for $600,000. He exploited the delay in Bitcoin price updates… Started with just $500.” Replies to that thread span “genius” and “Polymarket is cooked,” reflecting a split between those whispering admiration for the execution and those questioning the sustainability of the platform’s design. Whether Polymarket will attempt to retroactively claw back profits, adjust rules or simply patch the vulnerability again remains unsettled. For the moment, the anonymous trader is alternately being hailed as a folk hero and criticised as a parasite of an imperfect system. Moral of the Story In prediction markets where time and information intersect, speed can be money. Today’s episode may turn into a cautionary tale about the refinement of oracle feeds and the race to eliminate latency advantages — or it may become part of crypto folklore, a dramatic demonstration of how imperfect markets can be bent by those who read the technical seams most closely.

Russian Market

36,649 Aufrufe • vor 7 Monaten

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

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

72,134 Aufrufe • vor 10 Monaten

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 🙄

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

20,102 Aufrufe • vor 3 Monaten

AI won't make you Jane Street overnight. But it's closing the gap fast. DoubleZero co-founder Austin Federa on how AI-built programs, faster data and affordable servers are lowering the barriers for retail traders "AI inference is still too slow to really trade on. But you can have AI build you a trading program. Then you can hook that up to DoubleZero data." "You're not Jane Street overnight, but you are closing the gap tremendously between click trading on a UI and what a market making firm is doing." "Go back 5 years ago, and if you had an alpha trade and you didn't work at a big firm, good luck actually executing that trade. The execution and the data access was such a barrier. That's coming down very quickly." "AI can build the program for you. DoubleZero can get you the data. Through projects like the Hub, you can get a bare metal server located in NY4 at a pretty reasonable price." "It's not exactly the same as being co-located with Nasdaq, but it's much better than having your agent trade through Robinhood. So the access barrier is getting very low very quickly."

AI won't make you Jane Street overnight. But it's closing the gap fast. DoubleZero co-founder Austin Federa on how AI-built programs, faster data and affordable servers are lowering the barriers for retail traders "AI inference is still too slow to really trade on. But you can have AI build you a trading program. Then you can hook that up to DoubleZero data." "You're not Jane Street overnight, but you are closing the gap tremendously between click trading on a UI and what a market making firm is doing." "Go back 5 years ago, and if you had an alpha trade and you didn't work at a big firm, good luck actually executing that trade. The execution and the data access was such a barrier. That's coming down very quickly." "AI can build the program for you. DoubleZero can get you the data. Through projects like the Hub, you can get a bare metal server located in NY4 at a pretty reasonable price." "It's not exactly the same as being co-located with Nasdaq, but it's much better than having your agent trade through Robinhood. So the access barrier is getting very low very quickly."

The Wolf Of All Streets

15,863 Aufrufe • vor 16 Tagen

The value of the work we're doing at Optimum is encapsulated quite well by the phrase "speed is money". In modern markets there are real economic advantages to latency reduction. This is nothing new. Wall Street firms have long been optimizing on latency, primarily through colocation and top of the line hardware. However, when it comes to decentralized systems, expensive hardware and geographic concentration are antithetical to their purpose. Therefore we should optimize decentralized network latency through software, which I'm thrilled about because it's exactly what I've spent the better part of the past 2 decades working on with Random Linear Network Coding. Now let’s talk about networking economics, the relationship between speed and money. First, it's important to note that users will only pay for low latency if it can be consistently guaranteed. Second, you can only make that latency guarantee for a certain number of users. This is a universal law of networking. We can model this relationship on a delay curve, shown below. The delay curve is determined by the utilization rate of the network, meaning how much traffic is flowing through the network divided by the network's throughput. As you approach a level of traffic equal to the available throughput, latency trends infinitely higher. On this delay curve we can impose some utility thresholds. These thresholds are the levels of latency which are important to different groups of users because of how that latency guarantee improves their economic outcomes. Finding the point on the curve where each threshold intersects will tell us what level of traffic we can guarantee that level of latency for. Essentially, there exists a finite supply of speed on a network and the highest utility users of that speed are willing to pay more for it. I like to think of this similarly to expedited shipping options on Amazon. This is why we say speed is money, and why we can create a Latency Marketplace. The only way to increase the supply of speed is to fundamentally increase network throughput. This is what we work on at Optimum by using Random Linear Network Coding. The same relationship between traffic and throughput still applies, but now the delay curve is shifted out further to the right. Now more traffic can be processed at the same latency, or the same traffic can be processed at a lower latency. More speed available to the network. More value unlocked for the network’s users. Crucially, that value is no longer only reserved for those who can afford to sit closest to the machine. Expanding the supply of speed widens who can reach each latency threshold, keeping the network's advantage decentralized rather than concentrated in the hands of a few. When nodes join Optimum and participate, they reap the benefits, but they also add to the capacity. Rather than vying against each other in a zero-sum game, nodes help themselves and others.

The value of the work we're doing at Optimum is encapsulated quite well by the phrase "speed is money". In modern markets there are real economic advantages to latency reduction. This is nothing new. Wall Street firms have long been optimizing on latency, primarily through colocation and top of the line hardware. However, when it comes to decentralized systems, expensive hardware and geographic concentration are antithetical to their purpose. Therefore we should optimize decentralized network latency through software, which I'm thrilled about because it's exactly what I've spent the better part of the past 2 decades working on with Random Linear Network Coding. Now let’s talk about networking economics, the relationship between speed and money. First, it's important to note that users will only pay for low latency if it can be consistently guaranteed. Second, you can only make that latency guarantee for a certain number of users. This is a universal law of networking. We can model this relationship on a delay curve, shown below. The delay curve is determined by the utilization rate of the network, meaning how much traffic is flowing through the network divided by the network's throughput. As you approach a level of traffic equal to the available throughput, latency trends infinitely higher. On this delay curve we can impose some utility thresholds. These thresholds are the levels of latency which are important to different groups of users because of how that latency guarantee improves their economic outcomes. Finding the point on the curve where each threshold intersects will tell us what level of traffic we can guarantee that level of latency for. Essentially, there exists a finite supply of speed on a network and the highest utility users of that speed are willing to pay more for it. I like to think of this similarly to expedited shipping options on Amazon. This is why we say speed is money, and why we can create a Latency Marketplace. The only way to increase the supply of speed is to fundamentally increase network throughput. This is what we work on at Optimum by using Random Linear Network Coding. The same relationship between traffic and throughput still applies, but now the delay curve is shifted out further to the right. Now more traffic can be processed at the same latency, or the same traffic can be processed at a lower latency. More speed available to the network. More value unlocked for the network’s users. Crucially, that value is no longer only reserved for those who can afford to sit closest to the machine. Expanding the supply of speed widens who can reach each latency threshold, keeping the network's advantage decentralized rather than concentrated in the hands of a few. When nodes join Optimum and participate, they reap the benefits, but they also add to the capacity. Rather than vying against each other in a zero-sum game, nodes help themselves and others.

Muriel Medard

46,226 Aufrufe • vor 4 Monaten

The market moves in seconds. Can your trading app keep up? 📈 Upgrade to Scalper Pro on Rupeezy and experience a faster, smarter way to trade. Trade with confidence. Stay ahead of every move. Download the Rupeezy app now.

The market moves in seconds. Can your trading app keep up? 📈 Upgrade to Scalper Pro on Rupeezy and experience a faster, smarter way to trade. Trade with confidence. Stay ahead of every move. Download the Rupeezy app now.

Rupeezy

3,683,530 Aufrufe • vor 3 Monaten

I recently came across this video of an analyst claiming a specific Situational Analysis pattern, with more than 90% accuracy. Instead of taking it at face value, I fed the video and years of historical Nifty data into Gemini AI. By the time my coffee was ready, the AI backtested 20+ years of data. ✅ Scenario 1 - If the high price on Friday is lower than the high price on Thursday. The low price reached on Friday will be revisited during the regular trading session on Monday. Accuracy of this condition is 57.2%. Not 90% as mentioned by the expert, but a statistically significant advantage you can actually build a strategy around. ❌ Scenario 2: If the high price on Wednesday is lower than the high price on Monday. The low price reached on Wednesday will be revisited on Thursday. A literal 50.8% accuracy only. We live in an era where you can validate any trading idea instantly.

I recently came across this video of an analyst claiming a specific Situational Analysis pattern, with more than 90% accuracy. Instead of taking it at face value, I fed the video and years of historical Nifty data into Gemini AI. By the time my coffee was ready, the AI backtested 20+ years of data. ✅ Scenario 1 - If the high price on Friday is lower than the high price on Thursday. The low price reached on Friday will be revisited during the regular trading session on Monday. Accuracy of this condition is 57.2%. Not 90% as mentioned by the expert, but a statistically significant advantage you can actually build a strategy around. ❌ Scenario 2: If the high price on Wednesday is lower than the high price on Monday. The low price reached on Wednesday will be revisited on Thursday. A literal 50.8% accuracy only. We live in an era where you can validate any trading idea instantly.

Kirubakaran Rajendran

33,208 Aufrufe • vor 9 Monaten

This trader built a trading bot with Claude and made $197,000 on Polymarket His bot makes 167.4 trades per hour or 2.79 trades per minute and combines arbitrage, market making, and scalping on price repricing His strategy is simple: > Tracks live BTC, ETH, SOL, and XRP moves and catches when Polymarket lags > When an imbalance appears in the order book, buys the cheap side > If it sees arbitrage edge, buys the opposite side and turns the position into an arbitrage pair His Polymarket account: Using this strategy, he generates consistent profit and grows his capital

This trader built a trading bot with Claude and made $197,000 on Polymarket His bot makes 167.4 trades per hour or 2.79 trades per minute and combines arbitrage, market making, and scalping on price repricing His strategy is simple: > Tracks live BTC, ETH, SOL, and XRP moves and catches when Polymarket lags > When an imbalance appears in the order book, buys the cheap side > If it sees arbitrage edge, buys the opposite side and turns the position into an arbitrage pair His Polymarket account: Using this strategy, he generates consistent profit and grows his capital

VALIX

82,817 Aufrufe • vor 5 Monaten

This guy built an HFT bot on Polymarket with an average trade size of $8 Result: +$243,619 His bot trades 5- and 15-minute crypto Up/Down markets using a directional latency strategy with fast position switching: 1. It looks for latency between external crypto markets and Polymarket It gets the underlying price from external sources and tracks momentum, acceleration, order-flow imbalance, and distance from the starting price of the current window Based on this data, it constantly recalculates its own fair probability for Up and Down 2. It trades mispricing before Polymarket fully reprices If the external market has already moved, but the outcome price is still lagging, the algorithm sees an edge and starts building a position 3. It quickly changes direction and dynamically hedges the old position If the signal reverses, it can sell the old side or start accumulating the opposite outcome, constantly rebuilding its net exposure His Polymarket nickname: nj23adsknml3 You can build your own trading bot here: (A trial period will be available after registration) This guy’s edge is built on speed. The bot tries to detect changes in external market prices before they are fully reflected on Polymarket, then quickly rebuilds its position after every new signal It repeats this micro edge thousands of times and keeps growing its capital

This guy built an HFT bot on Polymarket with an average trade size of $8 Result: +$243,619 His bot trades 5- and 15-minute crypto Up/Down markets using a directional latency strategy with fast position switching: 1. It looks for latency between external crypto markets and Polymarket It gets the underlying price from external sources and tracks momentum, acceleration, order-flow imbalance, and distance from the starting price of the current window Based on this data, it constantly recalculates its own fair probability for Up and Down 2. It trades mispricing before Polymarket fully reprices If the external market has already moved, but the outcome price is still lagging, the algorithm sees an edge and starts building a position 3. It quickly changes direction and dynamically hedges the old position If the signal reverses, it can sell the old side or start accumulating the opposite outcome, constantly rebuilding its net exposure His Polymarket nickname: nj23adsknml3 You can build your own trading bot here: (A trial period will be available after registration) This guy’s edge is built on speed. The bot tries to detect changes in external market prices before they are fully reflected on Polymarket, then quickly rebuilds its position after every new signal It repeats this micro edge thousands of times and keeps growing its capital

VALIX

85,048 Aufrufe • vor 2 Monaten

This trader built a trading bot with Claude and made $101,220 on Polymarket His bot uses a hybrid system that combines temporal arbitrage between Up and Down with a controlled directional skew His strategy has 4 steps: 1. The bot starts by buying one side of the market 2. After the underlying asset moves and the market quotes change, it accumulates the opposite side so that Up + Down is below $1 3. The equal part of the Up and Down position becomes a hedged set 4. The unequal part of the position stays directional and captures extra edge This guy’s Polymarket username: almach Using this strategy and fast, high-quality execution, he turned $33k into $101k in 2 months of trading

This trader built a trading bot with Claude and made $101,220 on Polymarket His bot uses a hybrid system that combines temporal arbitrage between Up and Down with a controlled directional skew His strategy has 4 steps: 1. The bot starts by buying one side of the market 2. After the underlying asset moves and the market quotes change, it accumulates the opposite side so that Up + Down is below $1 3. The equal part of the Up and Down position becomes a hedged set 4. The unequal part of the position stays directional and captures extra edge This guy’s Polymarket username: almach Using this strategy and fast, high-quality execution, he turned $33k into $101k in 2 months of trading

VALIX

14,972 Aufrufe • vor 3 Monaten

I gave Claude Opus 5 full access to my laptop and turned on bypass permission mode. Asked him tuild me a Polymarket arbitrage bot. With no complicated strategy. Just this: Only open a trade when YES + NO < $1. I put $100 into the account and let it run. 2 hours: +$78.40 5 hours: +$479 Current balance: $1,791.30 in just 24 hours. Total Claude usage: roughly 7.5M tokens. But here's the part I find interesting: The bot doesn't care if Bitcoin goes up or down. It cares when the market prices both outcomes incorrectly. A BTC Up/Down market has two contracts: YES or NO At settlement, exactly one pays $1. The other pays $0. So buying both should cost $1. But during fast market moves, the two orderbooks can temporarily drift apart. For example: YES = $0.48, NO = $0.49 Combined = $0.97 The bot buys both. You're paying 97 cents for a position that settles to $1. That's a 3-cent gross spread without needing to predict BTC direction. And this isn't some complicated quant strategy. It's basic arbitrage. The difficult part is capturing it. These gaps can disappear almost instantly. A few cents of edge can also disappear after fees, slippage, or a bad fill. So the bot doesn't blindly buy every time the equation looks good. It checks the available liquidity, estimates the actual executable price, accounts for costs, and only fires when the remaining edge is large enough. It also has to handle the ugly scenario: One leg fills. The other doesn't. That's where a theoretical arbitrage can turn into a very real directional position. This is why I care much more about execution than adding another indicator. A human can find one opportunity. A bot can monitor hundreds of markets continuously. A human needs to click. The bot can react immediately. The strategy itself is almost boring. That's exactly why I like it. The interesting part is turning a simple mathematical rule into software that can actually execute it reliably. I shared the exact build process and prompts in my last article. Leaving it below if you want to build your own. The future of trading might just be better execution. Think bout that...

I gave Claude Opus 5 full access to my laptop and turned on bypass permission mode. Asked him tuild me a Polymarket arbitrage bot. With no complicated strategy. Just this: Only open a trade when YES + NO < $1. I put $100 into the account and let it run. 2 hours: +$78.40 5 hours: +$479 Current balance: $1,791.30 in just 24 hours. Total Claude usage: roughly 7.5M tokens. But here's the part I find interesting: The bot doesn't care if Bitcoin goes up or down. It cares when the market prices both outcomes incorrectly. A BTC Up/Down market has two contracts: YES or NO At settlement, exactly one pays $1. The other pays $0. So buying both should cost $1. But during fast market moves, the two orderbooks can temporarily drift apart. For example: YES = $0.48, NO = $0.49 Combined = $0.97 The bot buys both. You're paying 97 cents for a position that settles to $1. That's a 3-cent gross spread without needing to predict BTC direction. And this isn't some complicated quant strategy. It's basic arbitrage. The difficult part is capturing it. These gaps can disappear almost instantly. A few cents of edge can also disappear after fees, slippage, or a bad fill. So the bot doesn't blindly buy every time the equation looks good. It checks the available liquidity, estimates the actual executable price, accounts for costs, and only fires when the remaining edge is large enough. It also has to handle the ugly scenario: One leg fills. The other doesn't. That's where a theoretical arbitrage can turn into a very real directional position. This is why I care much more about execution than adding another indicator. A human can find one opportunity. A bot can monitor hundreds of markets continuously. A human needs to click. The bot can react immediately. The strategy itself is almost boring. That's exactly why I like it. The interesting part is turning a simple mathematical rule into software that can actually execute it reliably. I shared the exact build process and prompts in my last article. Leaving it below if you want to build your own. The future of trading might just be better execution. Think bout that...

Oracle Boar

22,315 Aufrufe • vor 3 Monaten