Hello everyone,

I am planning to try algo trading. My goal is to start with paper trading for swing strategies, using a Claude agent to backtest ideas and understand what works and what doesn’t. If the results are good, I may invest real money later.

If you have experience with algo trading, I would like to ask:

1. How has your experience been?
2. What has worked for you, and what hasn’t?
3. Which strategies have you used?
4. What does your architecture look like?
5. Any suggestions?

What is the best metric for identifying the quality of regime detection algorithms?

I've been building a cross-sectional equity ranker and want honest critique on the backtest framework + results. Keeping model/feature details abstract (that's the IP I've invested in) but happy to discuss architecture and methodology.

Setup

• Universe: ~650 US equities (S&P 500 + mid-caps + some delisted names, point-in-time membership)
• Data: daily OHLCV from Tiingo, 2006-present, adjusted prices
• Label: 5-day forward excess return vs SPY, decile-ranked for training
• Model: tree-based cross-sectional ranker

Walk-forward validation

• 6 rolling folds, each 12y train / 1y validation / 1y test
• 10-day embargo between val and test
• Non-overlapping test windows spanning 2020-02 to 2026-02
• Proper point-in-time universe (no look-ahead on ticker membership)

Three portfolio variants run in parallel

Portfolio	Rebalance	Holding
TOPN-5	Every 5 days	Full 5 days
TRANCHE	Daily (5 overlapping tranches)	5 days each
MINHOLD	Daily entry	Min 5 days, signal-driven exit

Per-portfolio sizing

After finding no single sizing works best for all, my production config runs:

• TOPN / TRANCHE: rank-based confidence weighting (weights ∝ rank² within top-5)
• MINHOLD: equal-weighted (daily entry made rank-concentration too noisy)

6-fold test-set results (total return, 1-year test each)

Fold	Period	TOPN	TRANCHE	MINHOLD	SPY
1	2020-02→21-02	+72%	+141%	+146%	+9.5%
2	21-02→22-02	+4%	+18%	+4%	+9.9%
3	22-02→23-02	+63%	+39%	+55%	−9.7%
4	23-02→24-02	−15%	+25%	+12%	+23.6%
5	24-02→25-02	+176%	+159%	+184%	+21.9%
6	25-02→26-02	+125%	+78%	+101%	+11.7%
Avg		+71%	+76%	+84%	+13%

Test Sharpe ranges 0.3 to 3.6 across folds. IC (Spearman) averages 0.02, per-fold range −0.002 to +0.046.

Costs modeled: 1bp fee + 3bp slippage + 5bp spread buffer per trade, 50bp annual borrow (long-only in this config).

What I think might actually be alpha

• Beats SPY in 5/6 folds across all three portfolios
• TRANCHE's daily-5-tranche structure has the best risk-adjusted numbers — often Sharpe 2-3 on test
• Consistent across varied regimes: COVID, 2022 drawdown, 2023 AI rally, 2025-26 range
• Signal is orthogonal to market beta (test fold 3 returned +55% MINHOLD while SPY was −10%)

What's concerning me (please pile on)

1. Fold 2 (2021-22) is universally weak. All three portfolios barely beat or lose to SPY. Growth-to-value rotation year. IC near zero — model has essentially no signal in that regime. I haven't found a fix.
2. TOPN fold 4 was negative despite highest IC (0.046). Broader ranking was correct but the specific top-5 picks got unlucky. Concentrated-bet variance.
3. IC of 0.02 is below the usual "tradeable" threshold of 0.04. Returns come from stacking small edges across many trades. Feels thin.
4. Fold 5 and 6 look almost too good (TOPN +176%, MINHOLD +184%). I've been careful with walk-forward, embargo, point-in-time universe, label-derived features are lag-aware, etc. But Sharpe 2-3 on daily-rebalanced long-only in test feels too clean. Most likely explanation I can't rule out: subtle feature leakage.
5. Adjusted-price drift across data refreshes. Tiingo re-applies dividend adjustments retroactively when new dividends are paid, so historical adjClose values shift. Discovered the hard way — the same code + same tickers ran with different adjClose snapshots gives different backtest numbers. Found ~20% of tickers had 10-100 bps adjClose drift on historical rows between two fetches a week apart. Results aren't bit-reproducible across refreshes.
6. TOPN struggled in the 2023 AI rally — the concentrated top-5 missed the Mag-7 concentration. A broader (TRANCHE) basket captured some of it.

Open questions

1. Low-IC high-return puzzle: is ~+70-84% annual return on low IC (0.02) plausible as alpha, or is there a typical look-ahead trap I should be hunting for?
2. Rank-based confidence sizing: my ranker produces scores that sigmoid to a narrow band around the mean (not calibrated probabilities). Switching from the standard (p_up − 0.5) confidence weighting to rank-within-top-N added 4-6pp on concentrated portfolios. Is this a common fix for lambda-rank-style models, or is there a more principled approach (isotonic calibration etc.)?
3. Dividend-adjustment drift: how do people handle this for reproducibility? Snapshot the dataset at a point in time? Use raw close and manually compound dividends? Accept drift and retrain?
4. Fold-2-style regime change: is there a standard defensive overlay (macro gate, vol target, credit-spread filter) that you've seen actually work, or do most models just accept one bad regime year?
5. Three correlated portfolio variants — is it defensible to run all three and report the best, or am I just p-hacking the presentation?

Been thinking about how much time usually gets lost between coming up with a trading idea and actually testing and automating it, especially for US market strategies.

I’ve been experimenting with a simplified workflow that tries to compress the process (idea → backtest → validation → automation) into ~10 minutes.

Curious how others here handle this step in their process.

So you spend all this time dialing in your strategy, but there are still unknown unknowns.

What do you do to limit asymmetric risk?

Do you have a kill criteria?

Do you reduce allocation to the strategy at a certain point?

Hi all,

Recently started to learn about trading to start my own algo-trading project - started by learning some theoreticals of pricing and asset classes, types of backtesting etc.

I think my next move is starting to search for signals to indicate strategies - but this is where I feel a bit out of my depth, how does one even go about researching signals? Is it mostly feature engineering over different moving averages and having a good predictive model that uses them correctly? building the search space of the model based on whatever features come to mind?

Would love to hear the thoughts of wiser men/women

For quick reference, scalping is in like 15-min charts; typical day traders do around 30-60 min charts; swing traders do daily charts; positional traders focus on weekly charts; and trend traders do monthly charts.

I am curious which style of trading you find yourself drawn to.

Your experience probably makes you focus on one or two particular styles over others.

What did you notice about the selection of these timeframes while designing your system? Perhaps subconsciously?

Hi all. Doing back testing on my algo on mt5 however it’s taking over 3 hours to Backtest 1/2 days worth of data.

Obviously it’s a mt5 EA but is there anywhere that can run this much much faster for 1m / 5m timeframe?

Unfortunately im in need to use real tick data for my EA. Hope there’s something out there! Cheers all

I've been going back and forth on automated trading for probably close to a year now. Every time I think I'm ready to commit I end up falling down another rabbit hole of research and talking myself out of it again. I know there's risk involved with anything trading related but the potential time savings alone seems worth exploring.

For those of you who were in a similar spot of analysis paralysis what was the thing that finally made you say ok I'm doing this. Was it seeing someone else's results, a specific platform feature, or did you just reach a breaking point with manual trading? Also how long after starting did it take before you actually felt comfortable trusting the system?

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I’m on my third complete rewrite of a deterministic value investing engine. The hardest lesson from the first two tries was that standard deviation is practically useless for filtering raw API data.

I used to run a rolling Z-score to drop exchange glitches and fat fingers. But I found out the HARD way that a single garbage tick at $5.00 on a $150 stock distorts the mean so violently that subsequent wicks hide perfectly inside expanded standard deviation. So my execution engine failed.

Instead, I moved my Layer 1 ingestion to Median Absolute Deviation (MAD) (flowchart attached). The median provides an immovable floor that completely ignores the chaos of a massive glitch.

(Note: I had to hardcode a minimum variance "Penny Stock Shield" to prevent division-by-zero errors on frozen assets. Still looking for a more elegant math fix for that).

Before I permanently bake this into my TimescaleDB pipeline, I need to know my blind spots. Are there specific volatility regimes or edge cases where a MAD filter completely breaks down?

Roast my logic before I risk capital on it. Let me know if you want to see the code.

I've been trading for around 9 years now, and lately every platform I use seems to claim it has "AI features," and I mean a lot of them look like normal screeners with a chatbot attached. So I'm a bit skeptical now if there is any actual benefit to them, like are there any tools where AI components actually provide useful analysis instead of just repeating information already visible on the screen?

Hi,

I have recently been trying to build a backtester/strategies to trade cryptocurrency.

How do we go about portfolio management compared to equities since not everything has the same numeraire?

Do we take all pairs and calculate prices for every traded currency in terms of one currency (say USD) and then proceed like how we would in equities? e.g. calculate optimal portfolio, risk, etc.

But then how do we rank based on previous day’s dollar volume? do we only use the most liquid path from an asset to USD? or do we aggregate across all possible paths to USD?

When trying to forecast future returns of our assets, do we only use the shortest series of hops? or do we also use information from the intermediate steps? e.g modeling ETHUSD (which is traded on exchange), do we use ETHUSD or (ETHBTC x BTCUSD)?

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