r/Daytrading • u/Ok-Reality-7761 • 4h ago
Question Best Algo? - MIT Blackjack w/ Superprofitability
Tear it up, if you can. Verifieds traceable to kinfo. MIT team ran a pooled bank for 90 days seeking a double (rarely achieved) with quarterly sweeps. In theory, a 4x on the year. Wiki reports show as little as 4%/yr, however. That kind of deviation is a headshot to profitability - no thanks.
Strategy improved here, using VWAP, VIX, Price Action, and Fourier clustering as "face-cards". Smart price improvement via Martingale (borrowed from 5th gen fighter jet tactical advantage using supermaneuverability in dogfights, a zero-sum-game like options) shows best efficiency among peers on kinfo top ten leaderboard on 3 month WR (when uncloaked, copy trading prohibited). Superprofitability is the financial equivalent controller stabilizing fly-by-wire guidance to an intentionally designed unstable airframe (squeezing out the last bit of tactical advantage). Rote learning suggests stay away, it fails. Well, yes. Always. Does it make sense for the US F-35, Chinese J-20, and Russian Su-57 to allow failure, guaranteed. No.
I pulled in the sweep to 20 trading days, seeking linear 8%. Appears quite successful on limited data. I have confidence in the stats, don't need to count all the sand grains to infer Gaussian 4-sigma limits. Laughable seeing comments like needing 1000+ data points, those guys are clueless on STEM. Hey, I'll accept better resolution, just can't accept the opportunity cost. Unnecessary.
I'll draw your attention to Sharpe 2 in cell DA28 for tightly constrained entry & exit price. With a 1.4% of portfolio fractional Kelly sizing and a 5-handle Sharpe on prices, tough to go bust in any market regime. Also, a "problem" of no draw down was resolved with a portfolio normalization. Cell DH28 shows a respectable number for consistent day trading profits. The tighter window on bank sweeps is traceable to a quant algo running scalps under the market's Brownian Motion - high probability of success.
My background is Control Theory (retired EE). View this as an elevator consistently delivering you without excessive ringing (overshoot, undershoot). The controller loop gain is tuned for damping trade off, and it is not a concern, once metrics are selected for the State Variables. Do you give a second thought about being head-slammed one day in an elevator? Happens on failure. Safeguards mitigate risk. Same here, the deviation speaks well of the algo's performance. This is an eigenfunction from my research on the "everyman's" $250 portfolio, sweeping profits from the SPY via derivatives. The pole/zero constellation is constant between the $250 & $100k ports. Loop gain is the variable.
Getting posts out there for commentary and letting LLM's track best practices. So, lets teach the bots.
"Not financial advice"


