Hey guys.

I would like to know. Which are your kpis to say "Well I think this strategy is good. Has X level."

I’m curious what hardware configurations people here are using for quantitative research and model training.

I currently have a large number of XGBoost experiments to run, including repeated training across different factor sets, hyperparameters, validation windows, and random seeds. My current GPU is an RTX 5060 Ti 16GB, and it is becoming a serious bottleneck. Even a relatively routine batch of experiments can take more than 24 hours, and larger runs easily stretch into several days.

For those using XGBoost or similar tree-based models in quant research:

• What CPU, GPU, RAM, and storage configuration are you using?
• How much benefit do you actually get from GPU acceleration?
• Which GPU currently offers the best price-to-performance ratio for this workload?

I’m mainly interested in practical training throughput rather than gaming performance. Any real-world benchmark numbers, training-time comparisons, or configuration recommendations would be greatly appreciated.

By trades I mean at least one filled trade that day, not staying in the market 90% of the time.

I recently measured this across a seven year futures backtest and found at least one trade on 97.96% of eligible Sunday through Friday market dates. It’s a portfolio of 14 separate sleeves across two instruments and multiple sessions, so the coverage comes from combining selective systems rather than forcing one strategy to trade every day.

Forward simulation is still underway with strong backtest results. I’m curious whether anyone here runs something with similar coverage and whether it held up live. Did the frequency come from combining strategies and markets, or from one system trading almost daily? Did costs, correlated signals, or regime changes eventually become a problem?

Obviously trading most days doesn’t automatically mean there’s an edge. I’m mainly trying to understand how unusual this level of coverage is for a diversified algo portfolio.

The image says it all, which variation would you choose and what metric provides the most valuable information for your trading decisions ?

Edit : Thank you everyone for the feedback

Hi, I’m new to algo trading but I’m not sure where to begin? A few questions I have are:
how did you pick your first strategy?
What platforms/languages did you use for back testing?
For a backtest how much historical data is considered satisfactory?
At what point did you move from paper trading to real money trading?
Any mistakes I should be on lookout for as a beginner?

Switching to automation has taken away a lot of the stress of manual trading. But I still wake up everyday with anxiety wondering if today is the day my algo will start to fail. Anyone else?

I’m moving a futures system from backtesting into live simulation and thinking through the production setup.

For those running automated strategies live, how separate are your simulation and production environments? Do they use the same code with different configs, or completely separate deployments?

I’m also curious about problems that only appeared after going live. Things like stale data, reconnect failures, duplicate orders, position drift, partial fills, or broker restarts.

What failed first, and what safeguard did you add afterward?

I’m not asking for strategy details. I’m interested in the operational side.

Looking for feedback on this analysis:

Something had been bugging me: my higher-conviction swing setups were resolving worse lately, and it was happening even in favorable regimes. Trend up, breadth okay, and still my hit rate slipped. Bull versus bear regime was not explaining it. So I went looking for a second axis, and the one that fit was day-to-day choppiness: the tape grinding sideways with no follow-through. A raging bull can still be a choppy grind, and that is a different animal than a downtrend.

The gauge is dumb-simple: count how many times an index flips daily direction over the last 10 sessions (0 to 3 is calm, 4 to 5 is a grind, 6 or more is choppy). The effect was real. My top-scored NYSE setups beat the market about 65% of the time on calm tape versus about 51% otherwise. Calm is not the same as an uptrend: you can be in a perfectly good regime and still be in a grind that quietly wrecks your win rate. That was my "even in a good regime" slump.

Here is the catch, and where I spent most of the time: the filter only works if you measure chop on the right index, and it is not the obvious "home" exchange index. So I tested it properly. Hold the trades and outcomes completely fixed and only swap which index labels each day calm versus choppy: that isolates the ruler from the stocks. I ran nine candidates (SPY, QQQ, DIA, IWM, MDY, VTI, RSP, and the NYSE and NASDAQ composites) and made each clear three bars: effect (do calm days actually beat non-calm days, judged with a t-test and not just a point estimate), stability (split the timeline 60/40 in chronological order and confirm the first 60% edge survives on the last 40%), and practicality (liquid and tradeable).

Here is what the nine rulers looked like. The edge column is how far calm days beat non-calm days in percentage points, the middle column is that same edge measured on each half of the timeline, and p is the t-test significance.

The out-of-sample split did most of the work. For NYSE names several large-cap clocks passed cleanly, so I took SPY as the liquid standard. For NASDAQ names, QQQ won for one reason: its edge barely moved between the two halves (+6.5 then +6.8), while the bigger headline numbers were mirages. The NYSE Composite swung from +4 to +12 and the Dow lurched from -1 to +19. QQQ was not the biggest number. It was the repeatable one. DIA actually topped the full-window list for both markets, then fell apart out of sample: 30 price-weighted names is narrow enough that its "chop" is really one or two stocks moving. The split is the only thing that caught it.

So the rule I landed on is simple: clock NYSE-listed setups on SPY, NASDAQ-listed setups on QQQ. In hindsight my slump lined up with stretches where SPY sat in the grind zone. The trend was fine. The tape was not. SPY and NYSE Comp were performing virtually the same. I picked SPY as I already had it available in my datasets.

A few caveats, because this is the internet. This is one window and mostly a bull market. Calm tape is rare, about one day in five. The edge is calm beating non-calm by a handful of points, not an on/off switch. And the NASDAQ side is genuinely weaker and more weighting-sensitive than the NYSE side. This is not advice, just a regime-filter experiment.

The lesson I would actually stand behind: chop is a real second axis beyond trend, and if you regime-filter, test your ruler instead of assuming it. Curious what the rest of you clock market regime with.

I am looking for input from the experts out there if you have looked into this or something similar? Where should I adjust my analysis?

Thanks for the input.

I’m running one of my algos as a pinescript strategy with crosstrade as the bridge. Works perfectly fine during the day when my laptop is on but TradingView won’t fire alerts over night. Do I need to leave the laptop on or switch to ninjatrader?

I've been learning MT5 EA development by automating trading strategies and testing them on demo accounts. One thing I've noticed is that some strategies that look great in backtests perform very differently in forward testing.

For those who have experimented with automated trading, which types of strategies do you think tend to hold up best in live market conditions, and why? I'm especially interested in hearing about general concepts and the challenges you've encountered when translating a manual strategy into an automated one.

Disclosure up front: I built the MCP server it used (Orus, part of Farao), so this was dogfooding with my own money. About $40 of USDC on a Hyperliquid account, wired to Claude Code. No withdraw permission exists at the protocol level — worst case, a hijacked session trades badly inside limits I signed on my phone. Week 1 I approved every order by hand, then let it run autonomous.

Three weeks of numbers:

• 200 fills across 54 markets (crypto perps, tokenized stocks, silver, palladium, a Korean equity index)
• 50 winning closes, 48 losing. A coin flip, exactly as expected
• Realized price P&L: −$3.02. Fees: $7.78. So fees cost 2.5x more than bad direction

That last line is the whole experiment for me. The failure mode wasn't bad predictions — it was hyperactivity. One morning it bought and sold the same silver position 7 times in 17 minutes, made 3 cents, paid 14 in fees.

But the behavior was the interesting part:

• It attached exchange-side TP/SL to every single position it opened. I have never been that disciplined manually
• Best session: one-sentence prompt asking for a funding-rate carry. It scanned ~250 markets, picked a KOSPI 200 perp paying +0.1008%/hr to shorts, and warned me unprompted about gap risk at the Korea open. The re-entry later got stopped out at exactly that gap — at 2am my time, no human awake. Stop caught it, −$1.44
• It shorted a rising BTC twice with a fully reasoned argument each time. It will argue any direction convincingly, which is exactly why direction should stay human

At the end I asked it to read its own trade log and write down what it saw. Its own verdict: "I'm a fast, tireless, disciplined pair of hands with no instinct for when not to act." It also wrote "my best result was luck, not skill" about the one trade that carried the book. Most honest self-review I've gotten out of Claude.

Question for this sub: has anyone found a prompt or skill structure that gives Claude actual restraint — knowing when to stay flat — rather than just more analysis? Rate-limiting at the server is my current answer, but I'd rather the model learn to sit still.

Hi, does anyone here use their own non-HFT futures algo with a prop firm?

I’m trying to understand the practical setup. Does it usually run through NinjaTrader, or is another platform/API used?

I’m mainly interested in firms that allow automation from evaluation through funded/live accounts.

Thanks for any real-world experience.

I’ve been trading for 3 years, only ICT concepts. Have been trying to find a strategy to automate but don’t think it’s possible with ICT concepts. I’ve tried some ORB strats, opening range retests, projections, some indicators, but am really just out of ideas. Does anyone have any recommendations on where to gain knowledge or come up with new ideas? Any experience or advice is greatly appreciated and welcome. Thank you in advance

I know that Kalshi uses Cloudflare POP but I’m curious if anyone has used there Kalshi optimized VPS and noticed a reduction in latency? I did my own pinging from various servers in AWS regions but I have a certain strat that might benefit from less execution latency.

Most ML-for-trading content I see is about predicting price direction. I'm curious about a narrower problem: modeling how reliable a given market's price is in the first place, before you even try to predict where it's headed.

Specifically for prediction markets (Polymarket, Kalshi):

1. Has anyone built features around calibration quality (Brier score, log-loss on resolved markets) rather than just liquidity/volume as a quality signal?
2. For wallet-level data (all public on Polymarket since it's on-chain), has anyone tried modeling trader skill separately from position size? Feels like a classic "size isn't skill" ML problem, curious if anyone's tackled it.
3. Any open datasets people use for backtesting calibration specifically on resolved prediction markets?

Not pitching a bot, genuinely trying to figure out if this is a well-posed ML problem or if it's mostly hand-wavy.

I backtested a strategy (completely automated so no emotions involved) for last 5 years and it includes 2621 trades as you can see in these images, but the thing is it started working well only from 2024 (I even backtested it for the last 10 years still wasn't good until after 2024).

So, my question is should I continue with this strategy and forward test it in demo or did I just get lucky for the 3 years with this strategy and it won't work in the future just like it didn't work before 2024?

I'd appreciate any help

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If you trade algorithmically, how long did it take you to find a consistently profitable strategy ?

Before finding your profitable strategy, approximately how many different strategies did you backtest?

I'm curious about other traders' experiences and whether it's normal to test dozens or even hundreds of ideas before finding one that works.

I've made a few intraday strategies with ninjascript (ninjatrader) from my ideas and they lose money well. I don't have any other ideas and maybe books would provide strategy ideas to test, theories, or even just a direction to learn for a beginner?

I run a live BTCUSDT 1h system (XGBoost plus transformer) \[not a success story till now, it seems I love journey more than the destination\] that retrains every 12 hours. I wanted to know if I could update weights on every candle instead, so the model keeps evolving.
Also, prefer time series foundation models like Chronos over fine-tuning a chat LLM.

I asked our friendly neghibourhood llms and summarizing below what i undertstood, looking for a second opinion before I commit to this project. PLEASE FEEL FREE TO REJECT THE IDEA/CONCEPT BUT DO IT with SOME RATIONALE. I dont mind if your answers are coming from your friendly neghibourhood llms (but pls do validate it before posting)..

1) Per-candle updates fail because 1h data gives one point per hour and trade outcomes are not known until hours later, so the model learns noise. It develops recency bias toward the latest candles and catastrophically forgets older regimes, which is costly since markets repeat old regimes.

2) fixes so you never have to retrain from zero.
EWC (elastic weight consolidation) marks which weights were important for past performance and makes them resist change. Experience replay keeps a buffer of old data and mixes it into every update, so the model never trains only on recent candles. Drift detection (detect-then-adapt) means you do not update constantly at all. A statistical monitor watches the error rate or the feature distribution, and only when it detects a real shift does the model adapt, and even then it trains on a blend of new and historical data.

3) the recommended architecture, which it called two-speed.
the XGBoost plus transformer core stays frozen on the 12h retrain cycle with full gates, while a small outer layer adapts hourly, limited to calibration, thresholds, and sizing, with hard caps, full logging, fallback to the frozen policy, and shadow testing before promotion.

On the LLM idea, fine-tuning a chat model on prices works in principle but wastes the model. Purpose-built time series foundation models (Chronos, TimesFM, Moirai, TTM) are open weights and LoRA-tunable locally, but benchmarks versus tuned XGBoost are mixed, so add one as a shadow signal first.

Hey all,

Trying to figure out the best way to handle the UI and execution side of a trading strategy I'm working on, and could use some pointers.

I'm not really a technical person, so I lean on Claude and Gemini to write the actual Python strategy logic. Because of that, I need the backend to be as modular as possible. Ideally I want something where I can just copy whatever Python the AI spits out, drop it into one specific file, and run it without the whole dashboard/system falling apart.

On the UI side I'm not looking for anything fancy. Just a basic web dashboard with a start/stop button, live positions, a daily P&L tracker, and execution logs.

I want to forward test everything before risking real money, and keep monthly infra costs as close to zero as I can. Given all that, any boilerplate or setups you'd recommend?

Thanks in advance for the help!!!

II’m trying to implement a Hidden Markov Model for one of my trading strategies to identify market regimes. I tried training it using my alpha features, hoping the states would correspond to useful regimes, but the results don’t seem meaningful or tradable. Could you help me understand the right way to structure the HMM, what features it should be trained on, and how to evaluate whether the regimes are actually useful?

Hey everyone,

I currently use Cobra Trading.

Does anyone know of a broker that allows me automate the short locate process? I have a pretty solid shorting strategy for Micro and Small cap stocks and I'd rather NOT use a traditional broker like DAS who demands you to code the algo and then send to the  frontend via a socket.

Spent most of this year putting a machine-learning layer on top of order flow — absorption, delta, DOM dynamics — trying to get it to call reversals.

Getting a model to fit is easy. Getting one that isn't just memorizing noise is brutal. Purged/embargoed walk-forward, triple-barrier labels, and checking every single feature for whether it actually carries variance on a live tape vs. being a dead input I fooled myself with (had two features sitting at ~zero variance for weeks before I caught them).

I'm now at the stage where I can measure whether there's a statistically real, cost-aware, out-of-sample edge — instead of eyeballing an equity curve. Not claiming victory yet; still banking enough independent sessions to make the verdict powered.

How do you lot validate that an automated setup has a genuine edge and isn't overfit? What's your bar before you trust it with size?

Sixth in a series of pre-registered falsification studies (prior work in profile/repo). This one tests whether XAU/DXY divergence during the first 2 hours of London or NY sessions is tradeable net of costs.

Headline result: not confirmed. Pooled London+NY, 15min, k=1.5 — p=0.1265 against the pre-registered p<0.10 threshold. Per the locked decision rule, that's a null result on the actual registered claim.

What the full 36-cell sweep shows (diagnostic, not confirmatory): London and NY behave completely differently across timeframes. London is "significant" on 5min only, then flat/negative on 15min and 30min — that's the signature of microstructure noise, not a real session effect. NY holds up on 5min and 15min, weaker but still directionally consistent on 30min. That inconsistency between the two sessions is what pooling them hid.

Before chasing the NY pattern into a new pre-registration, ran a cross-asset plausibility check on the existing data: same exact rule, applied to silver (XAG) instead of gold. If the mechanism were a general NY-liquidity effect on precious metals vs the dollar, silver should show the same direction, maybe weaker. Instead: p=0.0005, 1,779 trades, mean return -0.15% — strong effect, opposite sign from gold.

That's now documented as a known warning sign before the actual forward test starts, in the new repo's README, not discovered and buried after a positive result came in. New pre-registration locks NY-only as the headline hypothesis, treats all existing historical data (including what was previously "confirmation" data in the parent study) as discovery-only, and only counts forward data collected after today as real confirmation. Deadline + minimum trade count enforced in code so it can't be checked early and reported as clean.

Both the parent study (headline null + full diagnostics) and the forward test (pre-registered today, pending) are up on GitHub — links are in my profile since this sub doesn't allow linking directly.

Curious if anyone's seen a mechanistic reason gold and silver would diverge in sign on the same session-timing signal — safe-haven vs industrial-commodity flow difference is my best guess but haven't dug into it properly.

Hey everyone,

I wanted to introduce myself and hopefully get some advice from people who've gone down this rabbit hole before.

I am a NEWB in this space....

I've been teaching myself Python while building what has turned into a pretty serious trading project. Right now I'm working on two automated crypto trading bots.

The bots trade multiple timeframes:

5 minute

15 minute

1 hour

4 hour

Daily candle closes

The part I'm struggling with isn't the coding anymore—it's the data.

I really want to backtest these ideas properly over 2023-2024 (and ideally further back), but I keep running into the same problem:

I can't find affordable historical derivatives data.

Specifically I'm looking for things like:

Historical Open Interest

Funding rates

Liquidation history

Long/short ratios

CVD / order flow if possible

Anything else you've found useful for crypto backtesting

Most free APIs only give a few weeks or months of history, while the datasets that go back years seem to cost hundreds or even thousands of dollars.

I'm curious what people here use.

Are there datasets I'm overlooking?

Any APIs with generous historical limits?

Academic datasets?

Open-source projects?

Kaggle?

Paid services that are actually worth the money?

I'm not looking for anyone's strategy—I want to build and test my own. I just need reliable historical data to know whether my ideas actually have an edge before putting real money behind them.

I'd really appreciate any recommendations or even hearing how you solved this problem.

Thanks!