@hay.dee339: Siap tampil maksimal ketua🙌 #⭐️39racingpigeonss #merpatibalapsprint #ppmbsiindonesia #semuaorangbermaintiktok #fyp

⭐️39 RACING PIGEONSS
⭐️39 RACING PIGEONSS
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Tuesday 23 June 2026 10:40:30 GMT
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hamzahbe2
. :
maen seprin apa kolong mas
2026-06-27 09:07:57
2
aming123.meong123
Aming123 Meong123 :
jangan liat harga burung gede nya yang kecil juga belum juga burung nya jelek giman perawatan dan sama milik rezeki nya a
2026-07-22 11:39:23
0
ibat375
𝕚𝕓𝕒𝕥 070 :
2026-07-16 06:22:23
0
dekskip
~dEk CIO MuDA :
jangan ya
2026-07-02 04:19:32
0
alfiyano.saputra
pino :
dapet kah 300 di situ tahh boss
2026-06-23 17:08:54
0
billlliyy
bil dibacok mati :
salam dari tiem hore lapak Bojong
2026-07-03 11:04:53
0
om.heris86
HERIS86🇮🇩 :
300 k seprin yg bagus adahkah🤣😃
2026-06-29 00:19:01
0
king4k5ng4
kingko4g🪽 :
wah sesama bintang nihh 😁
2026-06-26 18:17:03
1
zakiuin
NEYMAR JR :
gw beli 100 RB abis itu gw tuker tambah 50 rb
2026-06-26 17:19:04
0
rawrrhamzz_4
_ham?™️ :
ring PMTI ya kaka
2026-06-25 05:01:49
0
inuwamar
Nu Ana Inu :
postur badanya idaman bang🔥😍
2026-06-25 12:57:11
0
shiba_hakkai28
Hakai Team :
modal dari merpati keramba sampe kebeli player, day one bikin akun, follback bang
2026-07-02 19:21:25
0
kdjxkdjxkdjxkdjx2
kdjxkdjxkdjxkdjx2 :
pasti di ketawain ommm
2026-06-23 11:35:22
0
haikalishere3
Haikal is here :
piyikan lar 6 aja harga nya 500 bg apalagi player😂
2026-06-25 06:34:04
0
dekran0210
🪬Aidil :
bukan soal harga mass tapi tergantung burungnya contoh nyaa merpati pasar 150k Alhamdulillah nggak ngecewain
2026-06-25 06:07:17
0
niky.idgaf
—p¡n` :
dapet jepati berapa?
2026-06-27 03:58:40
0
rafiandra.l
GAMERS :
100k aja udah bisa dapat merpati yang udah bisa juara
2026-06-26 07:32:59
0
budiman.saki
ini nh jakik :
kita pd sama kandang sendiri
2026-06-29 13:04:27
0
dekskip
~dEk CIO MuDA :
ya Jang dek nya
2026-07-02 04:19:24
0
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How do quants detect a market regime change in real time? The textbook approach fits a hidden Markov model on daily returns, but the raw online signal flips roughly nine times a year in the tests below. The statistical jump model (Bemporad, Breschi, Piga and Boyd, 2018, Automatica) reframes regime detection as clustering with memory: k-means on exponentially weighted downside deviation and Sortino features, plus a fixed penalty for every state transition, fitted by coordinate descent with a dynamic programming step. Shu, Yu and Mulvey (2024, Journal of Asset Management) tune the penalty through walk-forward cross-validation on the strategy's Sharpe ratio and test out-of-sample on the S&P 500, DAX and Nikkei 225 from 1990 to 2023, with 10 bps transaction costs and a one-day trading delay: on the S&P 500 the signal switched about once a year, and in their backtest the strategy reduced volatility and maximum drawdown versus buy-and-hold, with a higher Sharpe ratio than the HMM version, and held up better under longer trading delays. Limitations: backtests on three equity indices, two-state model, one small feature set; results depend on the jump penalty, tuned by cross-validation in the paper; detection latency was around half a month in their COVID-19 example, so the model confirms regime shifts, it does not predict them. Not investment advice. Papers: Shu, Y., Yu, C., Mulvey, J.M. (2024).
How do quants detect a market regime change in real time? The textbook approach fits a hidden Markov model on daily returns, but the raw online signal flips roughly nine times a year in the tests below. The statistical jump model (Bemporad, Breschi, Piga and Boyd, 2018, Automatica) reframes regime detection as clustering with memory: k-means on exponentially weighted downside deviation and Sortino features, plus a fixed penalty for every state transition, fitted by coordinate descent with a dynamic programming step. Shu, Yu and Mulvey (2024, Journal of Asset Management) tune the penalty through walk-forward cross-validation on the strategy's Sharpe ratio and test out-of-sample on the S&P 500, DAX and Nikkei 225 from 1990 to 2023, with 10 bps transaction costs and a one-day trading delay: on the S&P 500 the signal switched about once a year, and in their backtest the strategy reduced volatility and maximum drawdown versus buy-and-hold, with a higher Sharpe ratio than the HMM version, and held up better under longer trading delays. Limitations: backtests on three equity indices, two-state model, one small feature set; results depend on the jump penalty, tuned by cross-validation in the paper; detection latency was around half a month in their COVID-19 example, so the model confirms regime shifts, it does not predict them. Not investment advice. Papers: Shu, Y., Yu, C., Mulvey, J.M. (2024). "Downside Risk Reduction Using Regime-Switching Signals: A Statistical Jump Model Approach." Journal of Asset Management, 25(5), 493-507. DOI: 10.1057/s41260-024-00376-x. Open access: arXiv:2402.05272. Bemporad, A., Breschi, V., Piga, D., Boyd, S.P. (2018). "Fitting Jump Models." Automatica, 96, 11-21. Preprint: arXiv:1711.09220. Hamilton, J.D. (1989). "A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle." Econometrica, 57(2), 357-384. DOI: 10.2307/1912559. #finance #quant #trading #algotrading #stocks

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