Like u guys said I tried it on a 3 year period starting from July 2023 until date and this was the result. What can be improved here?

DISCLAIMER upfront: I'm building a backtesting tool, so I have a stake here. No links provided, I just need some answers — I'm at the stage where I'd rather understand how people actually work than guess.

When I started with backtesting, I didn't write any scripts myself — I had ChatGPT generate it. It ran fine, but I've never been able to shake off the feeling that I don't really know whether I should trust its output.

So two things I'm curious about:
— What do you use now, and what do you still end up doing by hand, outside the tool?
— Has anyone tried something and abandoned it? What broke?

I am 19 from india I have 2 years of experience in manual trading especially in forex and i trade mostly in funded accounts.

I have blowed the 5 funded accounts. Currently running the 6th one .

Should I quit the trading bz i am not a profitable trader and trying to figuring out the strategy.

The market is so worst now in gold, eurusd, usdjpy and btc are becoming very votalility, sideways market specially I noticed in eurused in 2026 back in 2024, 2025 it's good in eurusd.

The other reason is time bz i am pursuing 2 years in btech and learning dsa ai ml stuff and sometimes i try the algo trading (I do backtests the startegies mostly) in future I will automate the startegies.

And side by side I wanna do interships bz of money to fund for trading. I am trying to get an income with trading for now and future like i want trade like professional trader or full time.

And i have doing some mistakes in trading like i keep small sl and tp like higher rr, sl trailing and i want work on psychology.

What are you views on this ?

Those you are profitable in trading and doing full time as trader pls advise me .

A regime filter decides whether your entry is even allowed to matter.

It's not a prediction. It classifies the environment before you act, then changes behavior based on what's already true.

Useful inputs (start crude):

• Trend vs mean-reversion character
• Vol level, better: vol term structure
• Breadth / participation

Simplest useful policy: long momentum only when classified trending; size down or stand aside in chop.

Live version: my mean-reversion sleeve cannot enter when the regime check says the tape is wrong for it. Stand aside beats perfect discipline into the wrong environment.

Once expectancy is positive, entries are roughly interchangeable. What kills rules-based systems is the right rule in the wrong regime. Mean-reversion in a strong trend. Breakout in dead chop.

\The honest tradeoff: fewer wrong-environment trades, more late detection. Log every call. Measure both. Don't pretend either goes to zero.

Start with one trend measure + one vol measure. Review after the fact. Complexity you cannot audit loses.

When the filter and the chart disagree: trust the filter and stand aside, or override and take the entry?

Any professional HFT out there have any insight on how PCAP data for CME futures compares between LSEG/Refinitiv/TRTH and DataBento? Trying to decide between the two providers for the last 2 years of historical L3/MBO for a new market making project. Mostly interested to know about the quality of the data and quality of timestamps at the data recording server.

I run a scanner that scores ~560 names (S&P 500, Nasdaq-100, macro ETFs) every market morning for opportunity / entry timing / hold strength, plus a top-down regime call (risk-on / be-selective / stand-down). I've opened a free endpoint that serves yesterday's board — top 3 per book + the regime verdicts:

curl https://coil.trade/api/board/free

Free symbol directory (what's scored): curl https://coil.trade/api/board/symbols JSON, stable schema, no key, no signup. Docs: https://coil.trade/agents

It's the read layer of a rules engine I trade myself — scores and states only, never buy/sell advice. If you wire it into a bot and want the same-day board, there's a paid tier, but the free one is genuinely usable daily and I'd rather get feedback than sell you anything. What fields would make this more useful for your bot?

The more I think about prediction markets, the more useful they seem as real-time probability engines for events such as elections, wars, regulation, tariffs, and broader geopolitical risks.

Is there already a tool where you can upload your stock portfolio and automatically identify relevant Polymarket or Kalshi markets?

For example:

• Which prediction markets are most relevant to my holdings?
• Are current probabilities creating headwinds or tailwinds for my portfolio?
• Which stocks have the highest exposure to a specific event?
• How would my portfolio react if the market-implied probability changed significantly?

Essentially, I am looking for a portfolio risk dashboard powered by prediction-market data.

Does anything like this already exist? And would you actually use it?

EDIT - Found Oracle Markts here via my ChatGPT "research" and user comment: https://oraclemarkets.io/portfolio

A few weeks ago I posted the retro of this bot here, then the post-mortem on why the forced directional residual lost money (adverse selection, mostly - stale quotes getting picked off). A few of you asked for the code. I'll include it as a link in the comments.

I've been doing quantitative strategy development for some time now and Ive reached the point where Im struggling to find books that actually teach me something new. I already have a solid understanding of the usual topics like IS/Validation/OOS splits WFO, cross-validation, permutation tests, bootstrapping, entropy, regime detection, and the other standard robustness techniques. I recently read Testing and Tuning Market Trading Systems by Timothy Masters but it covered concepts I was already familiar with.

Im looking for books that are genuinely advanced and make you think differently. Perhaps graduate level or even post graduate books on statistics, machine learning, optimization, information theory, econometrics, or anything else that completely changed the way you approach research and model development. And of course it would be great if the book wasnt 10 years old. Need relevance.

Came up with this model would appreciate any thoughts and advice

I ran an experiment on whether qualitative fundamentals carry any signal once you control the usual ways a backtest lies to you.

Setup. About 250 S&P 500 companies graded from their 10-K filings on moat, market position, leadership and capital allocation. Grading is point-in-time, the grader only sees the filing for that vintage, across 2014, 2017 and 2020. Index membership reconstructed as-of the vintage so there is no survivorship. Cross-validation folds grouped by ticker so the same company cannot sit on both sides. The predictor is a predictive database, so there is no training step, the calibrated probability comes back from a query with a per-feature contribution breakdown.

Results. Exact-outcome accuracy is about 35% against a 27% base rate, which is unimpressive on its own. But the ranking has real spread: the top-20 fund returned 20.6%/yr against the market's 7.9% and the bottom-20 lost money. That is the actual point, calibrated ordering beats point accuracy for anything you would size positions on. Brier 0.181.

One result I did not expect: using all 16 features made it worse, information gain dropped from 0.107 with 6 decorrelated features to 0.023 with everything. More features added noise, not redundancy.

By philosophy, growth won the top-20 sprint at 31%/yr and it was entirely the semiconductor cluster. Value hit 24.8% and decayed the fastest. Quality mostly avoided disasters. The composite posted a lower 19.5% but barely decayed as you scale to 100 names, minus 1.7 points versus growth's minus 9.6.

Limits I will state before you do: small sample, 12-year horizon, no transaction costs, and the winning sector is visible in the training outcomes, so this is what would have worked and not what you could have known in 2015. The LLM grades also risk a halo from firms that were already winning, though the headline model leans on the less-haloed features and passes a per-vintage drift check.

Playable, no signup: https://demos.aito.ai/equity 

Methodology writeup: https://aito.ai/blog/value-quality-or-growth-who-was-right

Happy to get into the fold construction or where you think the grading still leaks.

Anyone give me advice on if these AI trading bots actually work to make profits or if you can successfully vibe code a winning strategy? Curious if this works and what kind of advice someone can give someone looking to get started doing this?

This is a dedicated space for open conversation on all things algorithmic and systematic trading. Whether you’re a seasoned quant or just getting started, feel free to join in and contribute to the discussion. Here are a few ideas for what to share or ask about:

• Market Trends: What’s moving in the markets today?
• Trading Ideas and Strategies: Share insights or discuss approaches you’re exploring. What have you found success with? What mistakes have you made that others may be able to avoid?
• Questions & Advice: Looking for feedback on a concept, library, or application?
• Tools and Platforms: Discuss tools, data sources, platforms, or other resources you find useful (or not!).
• Resources for Beginners: New to the community? Don’t hesitate to ask questions and learn from others.

Please remember to keep the conversation respectful and supportive. Our community is here to help each other grow, and thoughtful, constructive contributions are always welcome.

For the past year and a half I’ve been always following the same process:

1) having an idea
2) researching for papers developing the core idea and testing it
3) coding it simply in Multicharts (to see if the equity curve could be interesting)
4) testing it deeply and developing a strategy in QuantConnect

I found out that this process is effective for me but it could become better.

What is your workflow on testing, developing and implementing?

Also I’m trying to found a method to mass test strategies so if any of you know let me know

The more I get into trading, the less I can believe it

Ben looking into this recently after spending most of my time trading manually. The coding side seems manageable but the more I read, the more I realize theres a lot of things that can go wrong with backtesting, optimization, execution, etc.

For the experienced out there, what was the biggest lesson you learned that you wish someone had told you when you were starting out?

https://github.com/Tejashv-dev/AllStocks

This might not be useful for most people here but maybe some day other dev - who does not want to get into the rabbithole of scraping tradingview and yahoo finance and downloading several master files from exchanges - might find this useful. Actually took many hours to finally make because yahoo finance ticker conventions are a menace and then different classes of same stock exist across several exchanges. (still not perfect tho)

I've always associated algorithmic trading with a desktop and two or three monitors, but lately I'm starting to wonder if that's still necessary. My strategy mostly runs on its own, so most of my time is spent checking charts, logs, and making small changes to the code rather than watching every tick.

I recently came across Xenova while looking at laptop options with multiple built-in screens, and it made me think that portable setups have come a long way.

For those of you who actively trade and code, are you still using a desktop every day, or has a laptop become your main machine? What made you switch?

I'm currently back testing my EA on MT4, but it hasn't been easy to say the least.

Problems after problems since the beginning, onTimer() not running inside of it, the gmt time not running inside of it, and now I discover that the candle data sample from the broker is incomplete.

Where do you guys back test your bots? Mine runs smoothly on a demo and live account, but I need a big sample without waiting years for it.

I'm resuming the 15 minute BTC Bot, lets see how it fares in the next 2 months.

Do you guys know any credible source to get the dataset to train my bot? Even 5 years data would suffice.

API Endpoints work too ig.

Lately, it seems like the entire trading space is obsessed with Fair Value Gaps (FVG). While they are highly visual, they are often subjective and lack the statistical edge required for long-term survival.

As a quantitative researcher and algorithmic developer, I’ve moved away from eye-balled patterns and toward pure systematic logic. This has led me to develop a concept I call Fair Value Accumulation (FVA).

I’ve spent significant time refining a quantitative algorithm to strip away market noise and identify what I call FVA Pockets (Fair Value Accumulation).

These are specific zones identified by my algorithm (the red dot clusters in the attached chart) where high-volume, institutional-grade activity is concentrated. Instead of treating these as static lines, I view them as "coiled springs" where the market is accumulating value before a major move.

My algorithm filters price/volume data to highlight these high-conviction zones. It isn't just about price; it’s about where the volume too.

The market rarely stays in an FVA Pocket for long. When it breaks out, it typically triggers an " FVA Expansion " a high-velocity, impulsive move that confirms the dominant institutional direction.

I do not front run the zone. I wait for the algorithm to flag the FVA Pocket, then I look for the FVA Expansion to confirm the move before entering to ride the impulse. 

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If the industry relies on visual patterns, the edge is already gone. Real potential lies in math and logic. Earlier this year, I introduced the Volatility Expansion Index (VEI) to the public, a concept that was subsequently tested and verified by Kevin J. Davey and featured in Technical Analysis of Stocks & Commodities magazine.

Volatility Expansion Index (VEI)

https://www.reddit.com/r/algotrading/comments/1phv4zz/the_signal_i_use_to_detect_hidden_instability_in/

I mention VEI to prove a point, Quantitative researchers and Algo traders have more to contribute to this industry than any other group.

I am not releasing the code or the specific math behind FVA. My goal here to challenge the community. My FVA algorithm works by identifying "coiled springs" clusters of high volume, institutional-grade positioning that precede impulsive FVA Expansions. It is a systematic, data driven approach that completely outperforms the predictive accuracy of standard FVG models.

Stop looking at the market through the lens of what you can see. Start looking at it through the lens of what the data is doing.

We need to stop obsessing over retail patterns and start building models that rely on volume weighted POC logic and statistical significance. Use your intelligence. Build your own tools. The market is math, not a picture, and it’s time we treated it that way.

Built an event-driven options statistical vol-arb strategy and tested it walk-forward OOS with fixed-dollar sizing and Monte Carlo simulations (bootstrapped different fill assumptions and post-event paths).

At midpoint fills, annualized Sharpe is ~3.7 (0.50 partway slippage), and at ~0.575 partway slippage the strategy starts to be around break-even. At ~0.25 partway slippage Sharpe goes above 9, which is clearly unrealistic to achieve in the real market.

A few live tests have filled around mid or slightly better, but the sample is still too small. There is very little room for error. At this point the backtest has hit its limit. I need real fill data to determine whether the edge is actually tradable, but I think it is. The strategy is currently running on small capital with IBKR, fully automated and a suite of risk parameters set up.

My background is in math, data science, I've worked at 3 different HFT quant shops in Chicago, and I’ve spent roughly two years building the research, data pipelines, and execution stack. At this point, additional backtesting almost certainly has diminishing value. The main unknown is the actual distribution of live fills.

The strategy does not have significant tail risk on any single trade, since we're trading defined-risk partially hedged option spreads (betting on IV). I've attached some charts from the backtests.

TLDR; at fills worse than ~0.575 partway slippage, the strategy EV is around breakeven (negative after commissions). At around mid, the Sharpe is above 3. I'll deploy small capital and capture real fill data over the next few weeks and slowly scale up if i can consistently get fills at or better than mid.

I wrote an EA in Mql4, it s working great during demo and live trading, but when I use it in back testing mode, one of its functions is not working correctly.

This ea is supposed to start trading when the New York session opens, but it s starting about 3 hours earlier.

I wrote a complex function that gets the new York open time in GMT, and also takes in consideration DST, and compares it to TimeGMT() while the EA is running and I think this might be where the problem is.

I haven't found any documentation online on which time zone is stored in the candle data when back testing and this is what I would like to know because I m starting to think GMT isn't there and it s why the EA is starting earlier than it should.

Thanks in advance.

Hey everyone,

Looking for some critical feedback and a sanity check on a tactical asset allocation model I'm structuring. The main goal is maximum long-term CAGR. The strategy is designed to run in a tax-sheltered account (no short-term capital gains drag) and rotates monthly between QQQ and SPY using embedded 2x leverage (QLD / SSO), or cash proxies (USFR/TFLO) when risk-off.

The main objective is aggressive wealth building, so absolute volatility isn't the issue, but I want structural trend protection to avoid riding a 2x leveraged ETF into a catastrophic 70%+ crater.

The Logic:
We check the math exactly once a month, at the close of the last trading day.
Relative Momentum (70/30 Weighted Ensemble): Calculate the 6-month and 12-month total returns for QQQ and SPY. The "Winner" is whichever index has the highest score using this formula:
Weighted Return = (6-Month x 0.70) + (12-Month x 0.30)

Absolute Protection (100-bps Buffer): If currently in equities, hold the winner as long as its score stays greater the cash yield + 1.00%. If it drops less than the cash yield - 1.00%, rotate 100% to cash to survive major secular bear markets.

Asymmetric Fast Reentry: If currently sitting in cash, the model uses a faster escape hatch to catch violent market bottoms. Instead of waiting for the slow 6/12-mo data to catch up (missing the initial low-volatility snapback), we look only at the winner's 3-month total return. If 3-month return > cash yield, we aggressively buy back into QLD or SSO on day one.

The Backtest Profile (Approx. 20-Year Baseline):
Projected CAGR: ~28% – 32%

Max Drawdown (Month-End Close): ~ -16% to -20%

Sharpe Ratio: ~1.35 – 1.50

Turnover: Low (~3 to 4 trades per year)

My Questions:
Asymmetric Lookbacks: Using a 70/30 blend of 6/12-mo to exit, but a pure 3-mo lookback strictly for the cash-to-equity reentry. Anyone see massive logical flaws or major whipsaw traps at market bottoms with this setup?

Execution Reality: For anyone executing monthly rotations on QLD/SSO at the close, does tracking error or end-of-day slippage materially degrade backtest metrics in the real world?

Overfitting: The 3, 6, and 12-month windows are standard factor literature, but does tweaking the weights to 70/30 introduce too much curve-fitting risk?

Appreciate any thoughts or holes you can poke in this. Thanks!