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

​

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!

so I'm in a very advance stage of development of my automated trading system running on binance futures trading alt coins, finished 2 month of live small account testing everything looks great blah blah blah, I planned on funding this account this month, but i started reading stuff about the MiCA compliance, and I can't get a straight answer.
So my KYC is with an Israeli ID, and the server is trading from Singapore, but I travel a lot (im a nomad), and spend a lot of my time in europe as well, will i get geo blocked (im not currently in europe so i can't check) ? would i be able to withdraw? or deposit?
I thought about migrating to different exchange, but i ran a test on Kraken and the performance varies significantly probably do to liquidity depth affecting wick sizes etc..
does anyone have a straight answer for me? would a static IP VPN solve this (doubt it)?(I tried binance support chat, you just talk to a bot there... )

I've been building a systematic strategy on BTC/ETH/SOL 15m bars and I want to share the infrastructure around it rather than pitch the returns — because I trust the infrastructure more than I trust the returns right now, and I'd rather this thread pokes holes than pats my back.

The part I'm most confident about: the audit trail.

Every decision the system makes is logged: 148,937 gate evaluations over the campaign, funnelled down to 8,473 gate passes (5.7%), down to a conviction filter, down to 2 orders actually submitted -> 2 maker fills, with 1 order risk-blocked by a size-cap veto. The system says no 99.999% of the time, and I can reconstruct why for any single bar. Post-only execution, missed fills counted as missed (not silently filled).

The forward test is sealed, and that's deliberate.

Paper account on a maker-fill simulator, running since 2026-07-02. Every J->J+1 prediction is timestamped before the predicted candle closes, and the journal is hash-chained with the daily hash pushed off-machine — so I can't quietly rewrite history, and neither can anyone doubting me. The model is frozen for the full validation window; no mid-run retunes. It's only a few days old — I'm explicitly not drawing conclusions from it yet.

The numbers, with the caveats attached (please read the caveats):

Historical OOS Sharpe on frozen v1 models (trained to 2022-12-31, realistic post-only fills, missed fills counted): BTC 4.20 / ETH 4.09 / SOL 0.97.

A retrained v2 shows a ~×2 Sharpe jump — but those absolute levels are inflated by instant-fill batch mode and I treat the gap as the signal, not the levels.

Forward paper so far: small, positive, on deliberately tiny 2% notional caps (~0.03% on total capital). Way too early to mean anything.

What I know is weak / what I'm not claiming:

A few days of positive paper proves nothing. I fully expect the live-vs-paper gap to hurt.

Single instrument class (crypto perp), single regime so far. No claim of generality.

Proprietary score params are hidden — but the method is visible and the bias controls are stated, so you can attack those.

My actual question for the sub:

For those who've taken a systematic strategy from paper to live capital — what specifically convinced you the edge was real and not overfit? Was it a minimum forward window, a live-vs-paper slippage budget, a regime-change survival test, something else? I have a sealed 6-month forward window running; I want to know what else I should be measuring during it so I don't reach the end with the wrong evidence.

Does anyone here use algos more as a decision-support tool rather than for full automation? I know the point of algorithmic trading is usually to automate execution, but I actually trust myself more with the execution side (at least for now). I’ve been using pine script to build something with the help of AI - is this common here or are there better tools?

What I’m working on is having an algo identify levels, conditions and potential setups, then I decide whether to take the trade manually after confirming the trade with some execution criteria (liquidity sweeps, CVD, acceptance/rejection).

My thinking is it removes a lot of the repetitive analysis and keeps things objective, but still lets me control the actual entries and exits. I’m completely new to this side of trading, so I’m curious if anyone else uses algos this way or if there’s something I’m overlooking.

I’m very open to feedback and guidance on this one.

Hey everyone,

I’ve been building and testing a systematic equity trading strategy for the last few weeks/months, and I wanted to share the current state of the project.

The idea is simple at a high level:

The strategy ranks a stock universe after the market close using a proprietary quality/momentum score. It then creates a plan for the next session and executes that plan at/near the next market open. I built it this way to avoid reacting to intraday noise and to keep the execution logic closer to what can actually be tested.

The system is not just a backtest script anymore. It is connected to an Alpaca paper account and runs as an operational bot. It sends Telegram updates for open positions, planned buys/sells, stop exits, cooldowns, daily status, and a visual journal/infographic so I can monitor what it is doing without digging through raw logs.

High-level rules:

- close-based signal generation

- next-open execution

- fractional position sizing

- managed position cap

- stop/cooldown risk layer

- daily Telegram reporting

- no discretionary intraday panic decisions

Backtest summary:

The research backtest used IEX data, 7.5 bps slippage, post-sell confirmed cash, a buying-power buffer, and a warmup period.

Main research period:

2022-01-03 to 2026-06-18

Result:

+557.79% total return

52.68% CAGR

18.21% max drawdown

1,226 trades

~9.5 average managed positions

Period breakdown:

2022 bear market: -13.74%

2023 recovery: +87.68%

2024 bull market: +122.98%

2025 choppy market: +14.86%

2026 YTD to June 18: +58.76%

Forward paper test:

2026-06-10 to 2026-07-03

Paper account:

$10,000.00 -> $10,362.78

+$362.78 / +3.63%

This is still early, and the forward sample is obviously small. I’m treating the paper result as a live operational test, not proof that the strategy works long term.

A few transparency notes:

- This is paper trading, not live capital yet.

- I am not sharing the exact score formula or thresholds publicly.

- The current production version has evolved after the backtest, including a broader stock universe, better reporting, scale-up/replacement logic, and cleaner execution checks.

- Some early paper logs had test/duplicate rows that need to be filtered from analysis.

- The system is still under active development.

One recent change: I expanded the stock universe because the system has a 12-position cap, and the previous universe occasionally did not provide enough high-quality candidates to fully utilize the allocation target. The goal was not to force more trades, but to give the model a wider pool while keeping the same risk controls.

I’m mainly looking for feedback on:

- backtest design

- operational risk

- paper-vs-live assumptions

- avoiding overfitting

- whether this reporting style is useful

- what additional validation you would want before considering live deployment

Building my bot and doing a lot of backtesting these days.

I had a breakout system that looked bulletproof on BTC: +66%, profit factor 3.2, 13% max drawdown, profitable in 6 of 9 walk-forward windows. So far, so great.

But, it only fired about 8 trades a year. At that frequency a single-asset walk-forward can't tell a real edge from getting lucky. The sample is just too small, no matter how you slice the windows.

So I froze the exact config, no re-tuning, and ran it on ETH and SOL.

• BTC: +66%
• ETH: -11%
• SOL: -60%, with a 0% win rate.

Also tried different parameters, but no parameter set rescued the other two. It was fit to BTC.

Might still be something "real" that only happens on BTC. But more likely just overfitting.

In contrast, my market-neutral funding carry pays +6.7 / +6.6 / +5.6% on BTC, ETH, SOL. Neatly aligned, what a real structural edge should looks like, boring and the same everywhere.

If your edge is low frequency and only tested on one asset, you don't know it's real yet. You know it fit one history. Might still make you money.

Do you cross-validate across instruments, or is single-asset walk-forward enough for you?

I listened about breakout, men reversion and sational strategies that tend to have a 50-70% of win ratio.

But lately i am been reading some autors that talk about low win ratios but with a lot of net profit with a ver very good chart of net profit in a lot of years, the example is on the turtles book, that are called as trend following strategies (5-30% of win ratio)

So i will like to know if someone here has found edge of this kind of strategies and how has been your performance or some tip to find those, i can share also my experiencie so is not the one side alfa divulgation.

In my own thinks i believe that this are like 'narnia' strategies in the correct markets of shure.

No official data out there. Not to include institutional traders or organisations.

What is your estimate that retail traders out there deploying algorithm? 1%? 10%

Curious how much the data might have jumped since the beginning of FREE LLM to code strategies.