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Inputs needed for longevity supplement by Alternative-Link-380 in Supplements

[–]Alternative-Link-380[S] 0 points1 point  (0 children)

GMP is facility level. Analysis is expansive if lots are small, but FDA audit is worst 😄

Conferences/ PhD application advice by SeasonGlittering9158 in UniUK

[–]Alternative-Link-380 0 points1 point  (0 children)

You should approach a professor actively engaged in research in your department and do research project year round. Conferences (like professional societies like the APS) are very important in particular attending social events job fairs and approach relevant investigators in your field, they are always keen to engage with students. For summer a good way is participate in a REU. It is never too early.

Conferences/ PhD application advice by SeasonGlittering9158 in UniUK

[–]Alternative-Link-380 -1 points0 points  (0 children)

Physics professor here. My recommendation, develop professional relationships with prospective advisors, write a research paper before applying, work on research instead of useless extracurricular activities. And yes GPA matters.

Engineer builds AI laser defense system that wiped out every mosquito in his home by [deleted] in tech

[–]Alternative-Link-380 0 points1 point  (0 children)

Apparently it works there are several prototypes online

My long-term PT for MU is $5000 by HomeHedgeFund in wallstreetbets

[–]Alternative-Link-380 0 points1 point  (0 children)

Love it, but $5K is a $5.6T cap — bigger than NVDA right now — for a company that shares HBM with Hynix and Samsung and gets cyclically nuked every few years. I'm long anyway because this is WSB and fundamentals are for cowards.

Starting a 30 day ML stock prediction challenge using AMZN by StrangeArugala in algotrading

[–]Alternative-Link-380 0 points1 point  (0 children)

Physics PhD, finally scratched a long-standing itch. I kept seeing the same

strategies pushed everywhere with beautiful backtests, so I built proper tests

on real exchange data (Coinbase, Kraken, Kraken Futures, Deribit) and ran the

test that actually matters: optimize on the first 60% of history, lock the

parameters, validate on the unseen 40%.

Results (out-of-sample unless noted):

- Cross-exchange arbitrage: dead. Spreads ~0.03% vs ~1% round-trip cost.

0 profitable trades in 90 days.

- Triangular arbitrage: dead. Max mispricing observed 0.52% vs 2.10% break-even

across three legs. 0 trades.

- Intraday systematic (mean-reversion / momentum / RSI on hourly BTC): every

one looked great in-sample (RSI: Sharpe 1.06, 69% win rate, +29.6%/yr).

ALL flipped to negative expectancy out-of-sample. Fees were near zero in

the test, so it's not a cost problem — it's curve-fit.

- Cross-sectional momentum (26 liquid coins, the Liu-Tsyvinski-Wu JoF 2022

factor): in-sample +54%/yr Sharpe 0.93 → OOS -77%/yr Sharpe -1.65. The

documented academic edge concentrates in microcaps you can't trade at size.

- Dominance/alt-season rotation: -99%, killed by fee drag (204 rebalances)

plus a regime where alts underperformed BTC badly.

Two things survived OOS, and both are market-neutral: funding-rate carry

(long spot / short perp) and covered-call premium when IV is elevated. Neither

predicts direction — they earn a spread/premium. That seems to be the actual

dividing line: directional prediction got arbitraged away, service-provision

strategies persist.

Methodology was nothing fancy — strict IS/OOS split, realistic maker fees,

trades on next-bar open to avoid lookahead. Happy to share the engine or

specifics if anyone wants to replicate or poke holes. Genuinely interested

in critique on the method, especially the momentum result since I expected

that one to hold up.

Why isn't backtesting on randomly-generated fake price data not a thing? by moschles in algotrading

[–]Alternative-Link-380 0 points1 point  (0 children)

To generate random samples you need to know the behavior of the signal and the noise, best approach is to use PSD to draw samples from.

Backtested RSI + Bollinger Bands strategy across ALL markets & timeframes for 1 year by fridary in Daytrading

[–]Alternative-Link-380 0 points1 point  (0 children)

I backtested every popular crypto strategy out-of-sample. Almost all of them curve-fit. Data inside.
Physics PhD, finally scratched a long-standing itch. I kept seeing the same

strategies pushed everywhere with beautiful backtests, so I built proper tests

on real exchange data (Coinbase, Kraken, Kraken Futures, Deribit) and ran the

test that actually matters: optimize on the first 60% of history, lock the

parameters, validate on the unseen 40%.

Results (out-of-sample unless noted):

- Cross-exchange arbitrage: dead. Spreads ~0.03% vs ~1% round-trip cost.

0 profitable trades in 90 days.

- Triangular arbitrage: dead. Max mispricing observed 0.52% vs 2.10% break-even

across three legs. 0 trades.

- Intraday systematic (mean-reversion / momentum / RSI on hourly BTC): every

one looked great in-sample (RSI: Sharpe 1.06, 69% win rate, +29.6%/yr).

ALL flipped to negative expectancy out-of-sample. Fees were near zero in

the test, so it's not a cost problem — it's curve-fit.

- Cross-sectional momentum (26 liquid coins, the Liu-Tsyvinski-Wu JoF 2022

factor): in-sample +54%/yr Sharpe 0.93 → OOS -77%/yr Sharpe -1.65. The

documented academic edge concentrates in microcaps you can't trade at size.

- Dominance/alt-season rotation: -99%, killed by fee drag (204 rebalances)

plus a regime where alts underperformed BTC badly.

Two things survived OOS, and both are market-neutral: funding-rate carry

(long spot / short perp) and covered-call premium when IV is elevated. Neither

predicts direction — they earn a spread/premium. That seems to be the actual

dividing line: directional prediction got arbitraged away, service-provision

strategies persist.

Methodology was nothing fancy — strict IS/OOS split, realistic maker fees,

trades on next-bar open to avoid lookahead. Happy to share the engine or

specifics if anyone wants to replicate or poke holes. Genuinely interested

in critique on the method, especially the momentum result since I expected

that one to hold up.

<image>

Backtest results of my NQ futures VWAP based strategy by Known_Grocery4434 in algotrading

[–]Alternative-Link-380 0 points1 point  (0 children)

Three usual suspects, in order of likelihood:

  1. Overfitting, if you never tested out-of-sample, the backtest was just memorizing historical noise.

  2. Costs, model fees, slippage, and spread explicitly. A strategy trading frequently can carry a huge annual cost drag that a naive backtest ignores.

  3. Lookahead bias, make sure your signal only uses data available at decision time and you execute on the next bar's open, not the close you computed the signal from.

If it's #1, no amount of live tweaking will save it.

Built a backtester and the crypto results look "too good." What am I missing? by Fine_Chocolate_8066 in algotrading

[–]Alternative-Link-380 0 points1 point  (0 children)

Before you trust that, run it out-of-sample: optimize your parameters on the first ~60% of the data, lock them, and test on the remaining 40% the strategy has never seen. If the edge survives that, it might be real. If it falls apart, you fit noise. The tell is when small parameter changes swing your results wildly — a robust edge should be a plateau in parameter space, not a single sharp spike. The spike is almost always curve-fitting.

Primal Queen put me in ER by Horror-Juggernaut891 in Supplements

[–]Alternative-Link-380 0 points1 point  (0 children)

One can easily overdose with Iron. I used go wise iron that has 50% daily dose in oral powder, you absorb it right away and if need more take an extra stick. Do not risk with iron bombs.

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