@sorif.rana8: কাউকে সন্দেহ করা খুবই সহজ😭😭#tiktokshop #foryou #sad #emprendimiento @Af.afzal official page @A,Dilip Raj @abuj Pakhi

Sorif Rana

Open In TikTok:

Region: BD

Wednesday 23 September 2026 13:43:23 GMT

542

116

21

18

Music

Download

No Watermark .mp4 (3.4MB) No Watermark(HD) .mp4 (3.4MB) Watermark .mp4 (0MB) Music .mp3

Comments

Rabbi Islam :

hmmmm rit

2026-09-23 13:55:06

2

Hanif Nilphamari 📸📸 :

onek sundor vai caliye jan

2026-09-23 13:46:31

1

নীরব💔, কবি🥰 :

monar moto vayej 🥰🥰🥰

2026-09-24 03:40:06

2

@ Sumaiya akter :

ঠিক

2026-09-24 04:14:09

1

💥 Mσʂƚαƙιɳ Kԋαɳ 💥 :

ঠিক বলছেন ভাই

2026-09-23 14:18:23

1

★★❤️A❤️R❤️M❤️I❤️N❤️★★ :

রাইট

2026-09-23 17:36:43

1

mdmohidulislam5889 :

তাই নাকি

2026-09-23 15:54:25

1

নীরব💔, কবি🥰 :

Love you vaiya

2026-09-24 03:39:37

2

Nilufa Aktar :

ঠিক

2026-10-27 16:49:51

1

Beauty Akter :

🌹🌹🌹

2026-10-08 14:33:46

1

Md Morsalin Saha :

[Heartwarming][Heartwarming][Heartwarming]

2026-10-08 00:08:06

1

Sagor Vai💥 :

❤️❤️❤️

2026-09-24 02:12:27

1

Mȡ Mễřẫĵuĺ Išĺẫḿ Miřẫz :

❤️❤️❤️

2026-09-24 00:52:21

1

MD Samim Khan 🌹🌹 :

🥰🥰🥰

2026-09-23 15:53:22

1

亗 𝐁𝐌🔥𝐁𝐈𝐓𝐔𝐋 亗 :

❤️❤️❤️

2026-09-23 15:01:48

1

অপূর্ণতা ভালোবাসা💔🫶🫶 :

[Heartwarming][Heartwarming][Heartwarming]

2026-09-23 15:49:02

1

💔💔মিথ্যে মায়া 💔💔 :

🥰🥰🥰

2026-09-23 14:04:55

1

Rahat islam :

👌👌👌👌

2026-09-24 13:42:55

2

To see more videos from user @sorif.rana8, please go to the Tikwm homepage.

Other Videos

@Innoss’B @Gaz Fabilouss @Gaz Mawete @gally_garvey_officiel @Ach’B @✨️GOLBINE ✨️ #Mukolocopeflohka @Herman Amisi

@Innoss’B @Gaz Fabilouss @Gaz Mawete @gally_garvey_officiel @Ach’B @✨️GOLBINE ✨️ #Mukolocopeflohka @Herman Amisi

Ma takabir ni kar rha || 🥺❤️🕊️ #tariqjamil #viralvideo #viral #foryoupage #emotionalbayan

Ma takabir ni kar rha || 🥺❤️🕊️ #tariqjamil #viralvideo #viral #foryoupage #emotionalbayan

Wow Beautiful Meme 😂, #meme #funny

Wow Beautiful Meme 😂, #meme #funny

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

كيف أنقذ كريستيانو رونالدو نجم ريال مدريد الجديد؟ ⚽🔥 #ريال_مدريد #رونالدو #ديوماندي #الرياضة_على_التيك_توك #football

كيف أنقذ كريستيانو رونالدو نجم ريال مدريد الجديد؟ ⚽🔥 #ريال_مدريد #رونالدو #ديوماندي #الرياضة_على_التيك_توك #football

#CapCut #explore #fyp بينحذففف

#CapCut #explore #fyp بينحذففف

About

Robot
API

Legal

Privacy Policy