Since my last tier list did so well I though I'd make a part 2. Just to preface this is my own personal opinions from data providers I have used, I am an undergraduate economics student at Cambridge looking to break into Quant Research next year no need to grill me in the comments below.

London Strateigc Edge: Tickdata for all US stocks and options+ economic data for all countries FOR FREE just a massive archive of data. Everyone gets an api key with 50gb of data usage +100 websocket connections. Unfortunately no level 3 data which makes sense as exchanges charge per user who views the data.

Databento: If you need Level 3 data this is your place to go, all US exchanges covered + EUREX unfortunately in the 200usd plan live web sockets for l3 data not included. free $125usd credit for signup too

Alpaca: $100 for access to all US Exchanges for stocks and options data, includes websocket connections for all stocks and options definitely the best price option out of all the paid providers for websocket connections.

Massive: Biggest archive of historical data for US exchanges 20+ years, offers alternative data like credit card reports. Extremely easy to download the data. Free plan is meh.

FMP : Access to different exchanges like LSE, EUREX and other niche providers but low quantity of historical tick data.

Rithmic: API service offered through brokers like AMP futures, best price for level 3 futures data but slightly more complex to setup straight out of the box.

Yahoo Finance: Free historical data for a wide range of assets but London strategic edge providers more detailed data.

Tiingo: $30 USD for all US exchanges data, unfortunately 30gb bandwidth limit.

EODHD + Alpha vantage + Finnhub: intuitive api to use but just use alpaca + FMP for the same data but a lot cheaper

Hello again everyone, I want to share another strategy that I've been working on that I thought I would share with you folks to hear your thoughts.

The whole strategy is based on using the RSI indicator but instead of using it on the close, using it on the EMA 20 (RSI-EMA). I find that it is much smoother and gives stronger signals across timeframes vs. traditional RSI. A few backtests that I've done also find that the RSI-EMA is a fairly strong modification that produces more consistent wins and better results.

I of course wanted to turn this into a trading systems and had played around with various formulas, and entries and exits but believe that I finally found a system that may work well.

The set up is as follows. I use a universe of the top 20 companies in the S&P 500 by market cap (data goes back to 1989 and can be found here; my own backtest is 2010 to present). The tickers used for a current year are based on the top 20 companies from the previous year to avoid any lookahead/survivorship bias.

Within a given year, when an equity's RSI-EMA in the top 20 crosses over 30, I buy that stock. If there are multiple crossovers, I buy the one with the lowest RSI (but over 30 as it's crossing over). When there isn't an equity crossing over, capital is shifted into SPY. Exits occur after 10 trading days with all entries/exits happening at the next open. This system also uses 100% of the capital in each trade.

That's it!

With respect to exits, I tested using an ATR based exit (1.0 in either direction) and an RSI-EMA based exit (when it crosses above 70 or crosses back below 30). I also included a slippage penalty of 5 bps (0.05%).

Here are the results based on the various exits (initial capital $100,000; backtest starts in 2010):

Yearly results:

What if we experiment with the different number of holdings days? We see that generally 12 - 16 days is the ideal hold time with returns being lower on either side of that range. To minimize slippage and the number of trades, I would likely pivot to 15 trading days instead of 10.

And that's that! I would love to hear any feedback including criticisms, general thoughts and suggestions to improve.

EDIT: WALK-FORWARD TESTING ADDITION

To address some of the points about potential overfitting and the drop-off in CAGR between certain days, I decided to do an expanding walk forward test.

I find the number of days held from 2010 - 2015 that maximizes profit (ranging from 10 - 20) and then apply that to 2016. I continue expanding the in-sample period (for 2017, the in-sample optimization period is 2010 - 2016, for 2018 2010 - 2017, etc.) until June 30th 2026.

Yearly results, with B&H and numerical means/sd/sharpe are below with 2 bps of slippage (changed it when experimenting and don't want to re-do the table factoring in 5 - do what you will with that).

Walk forward testing actually improves upon my initial results and - numerically - produces a Sharpe that is actually higher than that of SPY. Though odds are if I converted all this to CAGR we may see an underperforming sharpe.

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?

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!

reddit algorithm finally figured out what i'm actually interested in and recommended this subreddit so i thought i share this story

it was around 1 or 2 months where i was basically stuck at 1.1 profit factor (after fees and slippage) and i had no idea how to make my strategy more profitable.
it sucked because i manually traded the strategy before and i knew and saw it could work. so i decided to change my stop loss strategy because when i traded it manually i would handle to exit myself and had no particular strategy.
so i wrote a code to allow backtests to ignore strategy's default stop loss and select a custom stop loss strategy instead.
and i tried all different stop loss strategies basically, and none of them were actually better.
the original stop loss of the strategy was also in the list of custom sl strategies , so i thought let's check if it actually matches the results of my previous backtest.
i got profit factor of 1.35...

usually when i get a positive number like this it's almost certainly a bug, so i compared the trades.
turns out it's not actually a bug, it's just a different implementation.
my normal strategy would use a static stop loss and never changed it because i don't believe in trailing stops or break evens.
but the code would update the stop loss price, every time it got a new signal in the same direction of the open position. it was totally implementable.

i still can't believe such a stupid bug and exit strategy affected my profit factor this much.

How do you make sure that people in your team are actually respecting the non disclosure and non competition agreements they signed as part of their contracts?

Hi all, I am building an app right now that utilizes stock data in a public facing manner that could likely be called commercial use. I was looking at stock data api providers, and I was like “okay, thirty bucks a month and every stock at a fifteen minute delay. Great. I’ll use that.” Then it turns out for commercial use it’s like $300. I cannot afford that for my app. I am hoping that there are cheaper options and I have been looking to see if there are, but the closest thing I’ve found so far is marketstack, but they limit my requests per month too harshly and I would not be able to provide all the data I need to my customers. Any api you folks can name would be greatly appreciated. I am looking for one that allows commercial use, has at the very least end of day prices and price/percentage changes every day, along with ideally no cap on requests or a very generous one. Thanks for any and all help.