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Tuesday 11 August 2026 22:58:45 GMT

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#CapCut Try lang po hehe

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Replying to @user68135676230421 Simple answer: because I made it that way. Comprehensive answer: this video. Here’s a caption for your video based on the transcript you’ve uploaded: Great question. Here’s the answer: The chart was generated using a stochastic jump diffusion process, which is a refinement of the classic geometric Brownian motion (GBM). This is not just a made-up model — it’s one that institutions use to price options and model asset prices. GBM models the log returns of an asset as the sum of a deterministic drift term (the expected return) and a random term (a Wiener process). But GBM alone is too smooth — real markets jump, especially around news and illiquidity events. So we add a jump term: arrival modeled by a Poisson distribution (rare, discrete events), and jump size by a log-normal distribution (since returns aren’t normally distributed — they have fat tails). Here’s the key: Even though the chart is random and non-tradable for alpha, I deliberately set the drift and jump parameters to have positive expected value. So yes — it trends up. But that trend is entirely explainable by the predictable drift and the positive expected value of the jumps. And that’s the point: Just because a chart trends, doesn’t mean it’s tradable for alpha. What looks like “smart money concepts” playing out… can be recreated by pure math. This is why understanding stochastic processes, expected value, and randomness is critical. You can’t just eyeball patterns and assume they have predictive power — that’s survivorship bias at work. #q#quantfinances#stochasticprocessg#geometricbrownianmotionj#jumpdiffusiond#daytradingmythsf#fairvaluegapi#icttradingf#financeeducationt#tradingtruthst#technicalanalysisf#financialengineeringo#optionpricingm#mathinfinancef#fatTailsr#randomwalkt#tradingstrategyr#retailtraderq#quantitativefinancet#tradingpsychologym#marketmythstradingalpha

Replying to @user68135676230421 Simple answer: because I made it that way. Comprehensive answer: this video. Here’s a caption for your video based on the transcript you’ve uploaded: Great question. Here’s the answer: The chart was generated using a stochastic jump diffusion process, which is a refinement of the classic geometric Brownian motion (GBM). This is not just a made-up model — it’s one that institutions use to price options and model asset prices. GBM models the log returns of an asset as the sum of a deterministic drift term (the expected return) and a random term (a Wiener process). But GBM alone is too smooth — real markets jump, especially around news and illiquidity events. So we add a jump term: arrival modeled by a Poisson distribution (rare, discrete events), and jump size by a log-normal distribution (since returns aren’t normally distributed — they have fat tails). Here’s the key: Even though the chart is random and non-tradable for alpha, I deliberately set the drift and jump parameters to have positive expected value. So yes — it trends up. But that trend is entirely explainable by the predictable drift and the positive expected value of the jumps. And that’s the point: Just because a chart trends, doesn’t mean it’s tradable for alpha. What looks like “smart money concepts” playing out… can be recreated by pure math. This is why understanding stochastic processes, expected value, and randomness is critical. You can’t just eyeball patterns and assume they have predictive power — that’s survivorship bias at work. #q#quantfinances#stochasticprocessg#geometricbrownianmotionj#jumpdiffusiond#daytradingmythsf#fairvaluegapi#icttradingf#financeeducationt#tradingtruthst#technicalanalysisf#financialengineeringo#optionpricingm#mathinfinancef#fatTailsr#randomwalkt#tradingstrategyr#retailtraderq#quantitativefinancet#tradingpsychologym#marketmythstradingalpha

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