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@afghanmayana0: سپلني ورته لوګی کړه زیارت ته تللی چا بې مخ لیدلی وینه #janobipashtonkhwa #foryou #goviral #viral #fyyyyyyyyyyyyyyyy

Afghanmayana 🦢
Afghanmayana 🦢
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Wednesday 21 January 2026 17:36:19 GMT
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|| ¿Seré libre? || . . . . . •Tendencia viejita :D •Las tendencias de ahora son lindas, pero difícil, jaja •No es mi idea (obvis) #gachatrend #gachalife #viral #parati #fpy_tiktok
|| ¿Seré libre? || . . . . . •Tendencia viejita :D •Las tendencias de ahora son lindas, pero difícil, jaja •No es mi idea (obvis) #gachatrend #gachalife #viral #parati #fpy_tiktok
Replying to @C E C E 🪷 we hope this video helps! 🎀🫶🏼 #fyp #menstrualcup #sierrasmenstrualcup #sierrasph
Replying to @C E C E 🪷 we hope this video helps! 🎀🫶🏼 #fyp #menstrualcup #sierrasmenstrualcup #sierrasph
How do you pick your indicators? Let's build a formula. Every systematic strategy is a function of the past path. Rough path theory gives that path a canonical description, its signature: the sequence of iterated integrals, where level one is the increment, level two the areas swept, against time the shape of the trend and between two assets the lead-lag, and higher levels finer shape shrinking like 1/n!. Hambly and Lyons (2010, Annals of Mathematics) proved the signature determines the path, and a universal approximation theorem says any continuous function of the path is, to any precision, a linear combination of signature terms. So every indicator, moving-average crossovers, RSI, Bollinger bands, even the Kalman filter, can be approximated arbitrarily well by a linear combination of signature terms. Futter, Horvath and Wiese (Quantitative Finance, 2025) make this a portfolio method. Regressing a MACD momentum strategy with a sigmoid, on the TLT ETF, onto the signature recovers it with R² of 66% at order 1, 89% at order 3 and 98% at order 11. Because the strategy is linear in the signature, the mean-variance optimum is closed-form: the weight vector is proportional to the inverse covariance of the terms' profit attributions times those attributions, both read off the expected lead-lag signature, with no neural network and no gradient descent. At order zero it is exactly Markowitz; each order adds path dependence and, because the criterion is over a horizon, a built-in drawdown control. For two assets, one signal and time at order two, that is 21 terms. The limits, from the paper: the expected signature is noisy to estimate and robustness is left to future work; the covariance needs signature terms of order six for an order-two strategy; market impact is not modelled; the real-data evidence is a three-ETF frontier and one momentum replication. Libraries: signatory, esig, iisignature. Futter, O., Horvath, B. & Wiese, M. (2025). Signature Trading: A Path-Dependent Extension of the Mean-Variance Framework with Exogenous Signals. Quantitative Finance, 25(2). arXiv 2308.15135. #finance #quant #machinelearning #roughpaths #algotrading
How do you pick your indicators? Let's build a formula. Every systematic strategy is a function of the past path. Rough path theory gives that path a canonical description, its signature: the sequence of iterated integrals, where level one is the increment, level two the areas swept, against time the shape of the trend and between two assets the lead-lag, and higher levels finer shape shrinking like 1/n!. Hambly and Lyons (2010, Annals of Mathematics) proved the signature determines the path, and a universal approximation theorem says any continuous function of the path is, to any precision, a linear combination of signature terms. So every indicator, moving-average crossovers, RSI, Bollinger bands, even the Kalman filter, can be approximated arbitrarily well by a linear combination of signature terms. Futter, Horvath and Wiese (Quantitative Finance, 2025) make this a portfolio method. Regressing a MACD momentum strategy with a sigmoid, on the TLT ETF, onto the signature recovers it with R² of 66% at order 1, 89% at order 3 and 98% at order 11. Because the strategy is linear in the signature, the mean-variance optimum is closed-form: the weight vector is proportional to the inverse covariance of the terms' profit attributions times those attributions, both read off the expected lead-lag signature, with no neural network and no gradient descent. At order zero it is exactly Markowitz; each order adds path dependence and, because the criterion is over a horizon, a built-in drawdown control. For two assets, one signal and time at order two, that is 21 terms. The limits, from the paper: the expected signature is noisy to estimate and robustness is left to future work; the covariance needs signature terms of order six for an order-two strategy; market impact is not modelled; the real-data evidence is a three-ETF frontier and one momentum replication. Libraries: signatory, esig, iisignature. Futter, O., Horvath, B. & Wiese, M. (2025). Signature Trading: A Path-Dependent Extension of the Mean-Variance Framework with Exogenous Signals. Quantitative Finance, 25(2). arXiv 2308.15135. #finance #quant #machinelearning #roughpaths #algotrading
Pack my lunchbox with me 🍱🧑‍🍳 #bento #lunchbox #fyp #viral #asmr
Pack my lunchbox with me 🍱🧑‍🍳 #bento #lunchbox #fyp #viral #asmr
Những ngày gần đây, tại thành phố Hồ Chí Minh đã ghi nhận ngày càng nhiều người dân chuyển sang sử dụng các phương tiện giao thông công cộng. Sự thay đổi này đang mở ra những tín hiệu đáng chú ý cho bức tranh giao thông đô thị. #vtv #vtv4 #news
Những ngày gần đây, tại thành phố Hồ Chí Minh đã ghi nhận ngày càng nhiều người dân chuyển sang sử dụng các phương tiện giao thông công cộng. Sự thay đổi này đang mở ra những tín hiệu đáng chú ý cho bức tranh giao thông đô thị. #vtv #vtv4 #news
WSKEN Magnetic Phone Case | Premium Blue Matte Finish for iPhone 18 PRO MAX and older #iPhoneCase #MagneticPhoneCase #MatteFinish #iPhoneAccessories #TechEssentials
WSKEN Magnetic Phone Case | Premium Blue Matte Finish for iPhone 18 PRO MAX and older #iPhoneCase #MagneticPhoneCase #MatteFinish #iPhoneAccessories #TechEssentials

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