u/Optimal_Emu3624

I’m researching how traders think about the future with AI. I’ve noticed algorithmic trading and AI are getting exponentially better at finding patterns and predicting markets.
I’m building tools to help humans stay relevant and make better decisions as AI gets better at trading. So I’m asking:
How do YOU think about staying competitive when AI can do this better? What do you think is the human edge that machines don’t have?
Would love to hear from people actually trading about what the future looks like for humans in this industry.
Any thoughts?

Before anything fancy, I pressure-tested the boring baselines over a real multi-regime window — and I'm posting the failures, not just the wins.

Rules (one fixed rule each, nothing fit to the data):

• Trend: long while price > 200-day SMA, else cash.
• Momentum: long while trailing 6-month return > 0, else cash.
• Mean-reversion: long when price < lower 20-day Bollinger band, else flat. All act on the next day's close (no lookahead).

Data: Yahoo daily adjusted, ~20 years (equities since 2005, BTC since 2014) — spans 2008, 2020, 2022. Fees: equities 0.03%/side, crypto 0.40%/side, charged only on trade days. Block-bootstrap Monte Carlo (2,000 paths) for path-luck bands.

What held up:

• The trend filter roughly halved max drawdown on every equity/crypto market over 20 years (SPY −21% vs −55% B&H, QQQ −27% vs −53%, BTC −71% vs −83%) — at the cost of lagging raw return. Classic risk reduction, across real crashes.
• The one return-and-risk win: trend on NVDA, +100,038% vs +84,447% buy & hold, −54% vs −85% drawdown. (Single-name survivorship — flagged.)

Where it failed (shown, not hidden):

• Long Treasuries (TLT): the overlay barely cut drawdown and missed the bull (+2% vs +85%).
• Mean-reversion got run over on gold and lost −66% on BTC.
• Momentum lagged badly on small caps.

Caveats I know (tell me the ones I don't): hand-picked liquid survivors (selection bias), daily-close fills + flat per-side cost (no intraday slippage), no parameter optimization, and 20yr is still short for strong claims.

What I'm asking:

1. For long/flat systems, your preferred risk-adjusted metric when cash days deflate vol — Sortino, Calmar, exposure-adjusted?
2. Reasonable crypto cost model — flat taker per switch, or maker/limit fills?
3. What would make you trust a "halves drawdown" claim — rolling windows, regime splits, walk-forward OOS?

Before anything fancy, I pressure-tested the boring baselines over a real multi-regime window — and I’m posting the failures, not just the wins.
Rules (one fixed rule each, nothing fit to the data):
• Trend: long while price > 200-day SMA, else cash.
• Momentum: long while trailing 6-month return > 0, else cash.
• Mean-reversion: long when price < lower 20-day Bollinger band, else flat.
All act on the next day’s close (no lookahead).
Data: Yahoo daily adjusted, \~20 years (equities since 2005, BTC since 2014) — spans 2008, 2020, 2022. Fees: equities 0.03%/side, crypto 0.40%/side, charged only on trade days. Block-bootstrap Monte Carlo (2,000 paths) for path-luck bands.
What held up:
• The trend filter roughly halved max drawdown on every equity/crypto market over 20 years (SPY −21% vs −55% B&H, QQQ −27% vs −53%, BTC −71% vs −83%) — at the cost of lagging raw return. Classic risk reduction, across real crashes.
• The one return-and-risk win: trend on NVDA, +100,038% vs +84,447% buy & hold, −54% vs −85% drawdown. (Single-name survivorship — flagged.)
Where it failed (shown, not hidden):
• Long Treasuries (TLT): the overlay barely cut drawdown and missed the bull (+2% vs +85%).
• Mean-reversion got run over on gold and lost −66% on BTC.
• Momentum lagged badly on small caps.
Caveats I know (tell me the ones I don’t): hand-picked liquid survivors (selection bias), daily-close fills + flat per-side cost (no intraday slippage), no parameter optimization, and 20yr is still short for strong claims.
What I’m asking:
1. For long/flat systems, your preferred risk-adjusted metric when cash days deflate vol — Sortino, Calmar, exposure-adjusted?
2. Reasonable crypto cost model — flat taker per switch, or maker/limit fills?
3. What would make you trust a “halves drawdown” claim — rolling windows, regime splits, walk-forward OOS?
4. What can be done about the slippage and fees on Solana and is that even possible to manage if you aren’t a rug factory?

Before I trust my engine on anything fancier, I ran it on the most well-understood rule there is — the 200-day MA trend filter — specifically to check the plumbing against results this group already knows cold. Posting the numbers and my assumptions; I’m after critique of the methodology, not the signal.

Rule: long while price > 200-day MA, else flat. Positions act on next day’s close. One fixed rule, zero parameters fit to data.
Data: Polygon daily bars, ~2 years (free-tier cap — yes, I know that’s the elephant; see below).
Costs: equities ~0.03%/side, crypto 0.40%/side (Kraken taker). Charged only on days it switches.
Results (strategy vs buy & hold, net of fees):
SPY → +24.9% vs +50.4% · max DD −5.2% vs −9.1% · 3 trades
QQQ → +39.9% vs +78.0% · max DD −8.5% vs −12.2% · 3 trades
NVDA → +53.7% vs +118.8% · max DD −17.4% vs −20.2% · 3 trades
BTC → −22.9% vs −33.8% · max DD −33.7% vs −51.2% · 18 trades
Textbook, as expected: underperforms B&H on return in a bull tape, cuts max drawdown on every asset, and BTC whipsaws the line 18x so fees eat it alive. Nothing here should surprise anyone — that’s the point. If the engine were wrong, this is where it’d show.
Where I know the method is thin (rank these / add what I’m missing):
• ~2yr window is a single mostly-up regime — useless for judging trend, fine only as a plumbing check. Longer history is next.
• No param sensitivity yet (150/200/250d, dual-MA, channel breakout).
• Daily-close fills, flat per-side cost, no intraday slippage model.
• Liquid hand-picked names = selection bias baked in.
What I’m actually asking:
1. For a long/flat system, how do you prefer to report risk-adjusted return when cash days deflate vol and inflate Sharpe? Sortino, Calmar, exposure-adjusted?
2. Flat taker fee per switch for crypto — reasonable, or do you model maker/limit fills?
3. Minimum history you’d want before a daily trend result earns any weight?