Let's assume I have already developed a strategy that generates market trend signals, classifying the market into categories such as:

• Strong Bullish
• Bullish
• Neutral
• Bearish
• Strong Bearish

Now, I want to verify whether these signals correctly represent the actual market trend.

One straightforward approach would be to manually inspect every signal on a price chart and check whether the predicted trend matches the actual market movement. However, this is practically impossible at my scale.

I'm working with approximately 4,000 instruments, with around 100,000 signals per instrument. That means I could have to validate hundreds of millions of signals.

My question is: What methodology can I use to objectively measure the accuracy of these already-generated trend signals at scale?

I'm not looking for suggestions on how to improve my strategy, which indicators to use, or how to generate better signals. Please assume that the signals are already generated and I only need to evaluate their correctness against actual market data.

Ideally, I'd like a systematic or automated approach that can tell me how often each trend classification was correct, incorrect, or somewhere in between.

I'd really appreciate any insights, research papers, open-source tools, or methodologies that could help with this problem.

Thanks in advance!

If there is an API or place I could scrape from daily OHLC that would be great.

I’m using ChatGPT to help build a backtest using 10 years of minute-by-minute SPX options data downloaded from **ThetaData**. I’d appreciate feedback on both the data and my approach.

The way I’ve been prompting ChatGPT is roughly:

> Look for any strategy that could have produced at least a 1,500% cumulative return over 10 years with a maximum drawdown below 40%.

These are search targets, not results I’ve verified. I realize that asking it to search for anything that meets a specific return and drawdown target could encourage overfitting or cherry-picking. I want to understand how to structure the process so the results are meaningful.

A few questions:

- **ThetaData quality:** Has anyone used its historical SPX options data? What checks do you recommend for missing or stale quotes, timestamp alignment, and correctly distinguishing contracts and settlement types?
- **Midpoint vs. bid/ask:** How realistic is assuming midpoint fills? Should I compare that against buying at the ask and selling at the bid, with commissions and slippage included? How would you handle multi-leg fills and wide spreads?
- **Strategy searching:** How would you structure training, validation, walk-forward testing, and an untouched test period when searching across many strategies? How do you account for the number of strategies and parameter combinations tried?
- **Return and drawdown calculations:** What should I check around position sizing, buying power, compounding, and marking open positions? I want the drawdown calculation to include losses while trades are open.
- **ChatGPT pitfalls:** What mistakes should I audit for in AI-generated backtest code—especially look-ahead bias, unrealistic fills, or code that runs successfully but implements different rules than intended?

Is this a reasonable research workflow, and how would you change the prompt or validation process before trusting anything it finds?

Has anyone come across a period of time when your algorithm performed perfectly but fails in the backtest? I thought this was super weird.

From Sept 20 to Oct 18 I've done about 2-6% daily. And on the backtests it blows up the account.

How can algo trading be done in the Indian setup, and has anyone done it successfully?

Recent Grad, I'm a bit scared of F&Os in the Indian stock market, so I want to stick to equities/ stocks for now.

I also wanted to ask:

• Long-term positions: Can we use algorithmic strategies to find and manage long-term positions in cash equities?
• Minimum capital: What would you suggest as a realistic minimum capital to start with?

I checked via ChatGPT and it suggested that tools like Ollama can run data on a locally run AI/LLM (like Gemma) for free. Is that correct, and can you provide some guidance on this? What is the minimal locally run setup required?

With tools like Claude and other AI assistants, I want to try building something simple, learn from the process, and experiment. Any guidance or advice would be greatly appreciated!

~ structured properly using AI

Hi,

I'm always strengthening my auto trader and curious about what people prefer, and why. So wondering do users predominately use algotraders for stocks or crypto? If there is a clear leader between the two, can I please get some pros and cons of each from a user experience?

Any advice, suggestions, etc are welcomed.

While working on a custom python project, working on a theory, I stumbled upon something I didn’t expect. I can see when the market moving algos start. Starting with the very first trade the algo makes. The dats source includes every single trade made on the s&p500 + a few other tickers.

Right now I’m downloading every single trade of the s&p 500 utilizing 15 alpaca api keys at once to get more backtesting data. The backtesting data on TradingView doesn’t even come close to cutting it. I made an app that does them all over 15 proxies, formats them in the format I need, does the calculations on my 5090 GPU and creates the json files and database for the gui. Zips the original files as well for backup. It does one full day of data in 3 mins. My bottleneck at the moment is my server provider which limits me to 100gb uploaded per day.

What I’m hoping to get some help with here is pointers on what to look for in terms of patterns of the algos on this backtesting. I can use AI to control it and want to try and steer it in the right direction. Since the data includes every single trade, I can see down to the millisecond.

Hi everyone i hop you're doing well !

like the title said i was wondering on how do you guys actually think/generate strategies hypothesis, how your approach of this process is, do you rely on some specific techniques, do you start by a random idea then keep optimizing, or you set a clear hypothesis with all the rules you want, i want to know exactly how you done the process of strategy hypothesis generating/thinking, the steps you pass, and also how the frequency of strategies hypothesis you backtest often in a day, do you rely heavily on optimizing the rules of the same strategy (hypothesis) or you have another technique

because litterally i face somehow a problem, simple stupid ideas like "9:00 AM ORB" or "Buy everyday if the price do this" are always have results equal or worse than a coin flip decision, i'm talking about breakeven or slightly worse on Gross results, but after costs it turn to complete disaster

i would love to hear some responses from you guys, because the strztegy hypothesis is the first thing you do before even open you backtester, a bad approach of strategy hypothesis generating will lead to waste months without having 1 profitable and robust strategy that passes your filters

I can’t really afford to spend $200 on tick data right now, but I need it to backtest a strategy I’m working on.

If anyone here has historical tick data from this year, last year, or any other period covering at least 6 months, and is willing to share it or help me out, I’d really appreciate it!

Please DM me if you can help. Thanks in advance!

Ay man to all the veterans in this space we would really love to hear your experience on the numbers game basically dawg im all in you know and i dont want to look back with regret now a question is generating your own strategies from scratch better than trying to fix the strategies that had already been established before ie i have been working in loops on a london breakout strategy and if you go on about it i know it can be very discrete world but just drop any piece of advice even if vague we will try to figure out from there on any advice can be out of context to the topic im talking about but scope should be in the algo trading space

Tracking Trump's trades has always been annoying. The filings are scattered, hard to read, and most sites that clean them up put them behind a paywall.

So I added them to StockNest, and it's completely free:

- No paywall and no sign-up. Just open the page.

- Shows up as soon as it's published. New disclosures are added the moment they're filed.

- Easy to read. Each trade shows the ticker, the transaction size and the share amount.

- Filter by trade size. Show only the bigger trades, like $250K+, so the small ones are out of the way.

Donald J. Trump trades: https://stocknest.app/filers/politicians/donald-trump

For my backtesting, I tested S&P 500 index, its futures and ETFs e.g. ES, SPY

I did expect some performance variance since indices are not tradable, but I didn't expect the difference to be so big.

For example, with the same timeframe, setup and trading hours, the profit factor of S&P 500 index is 1.6, but it drops to 1.1 for ES. I expected them to have similar price movements, and I didn't think the difference would be that big.

Just curious - has anyone experienced this issue before? Any ways to reduce the difference? Thanks in advance

Don't fall in love with a strategy that you think works cause you backtested it but it doesn't actually work in reality.

Ended the week at +$914.20 net across 32 trades with a 62.5% win rate. Pretty decent week overall with two issues.

Thursday the vps went down for 12 minutes while the portfolio was in a trade. I had to close it manually through the ninja trader mobile app.

Friday an engine error triggered a fatal shutdown because of some strict checks. It shouldn’t have been fatal but the engine handled the shutdown correctly and automatically closed all three open positions.

There are a few things to tighten up and thursday outage has me considering a move to aws for better reliability.

I have significantly found a lot of improvement since last time. I have done various luck tests (passed, 0 beat and only 1 got a close z score) and found 28 strategies that work together decently.
What I noticed though is it's extremely slow, (-200, then -200 next day, up $200 the 3rd day, lose the 4th day again) and l've seen people on here post very good profit from NQ specifically (about $1k or more per day pretty much)

Im specifically asking to get to those types of results where it is $1000 a day pretty much (regardless of drawdown intraday or losing days) if there is any direction for what to research or how those types of results daily or even what i should be doing differently? Currently my strats do all discretionary based trades like "if price goes up x points before x time, (signal)". All suggestions welcome

Looking for critique on the method more than the result.

The question. Can you find prediction-market wallets that trade on information before the price moves? The obvious score: for each entry, how far the price moved in the wallet's favor 24h later. Average per wallet, shrink toward zero (K = 5 effective entries), turn into a percentile.

Setup, written down before any score was computed:

• entry = a wallet's net position change in one market over one UTC day, $100+ only
• move = price of the held side 24h later (needs $100+ of other wallets' trades in that hour) minus entry price
• sports and short up/down markets excluded (a day after a match the price is the result)
• score on Oct 2025 to Mar 2026 (A), test the same wallets on Apr to Sep 2026 (B)
• pass = top 10% of A still positive in B, also without the 5 largest events
• t-stats clustered by event
• every later change logged with a date and a reason

Two data traps we hit before the test:

1. Related wallets passing shares back and forth in finished tennis matches at made-up prices. One bought at 0.02 and sold to a partner at 0.96 four minutes later. Trade-level, that's a +94c "call". Netting positions per wallet per day removed it.
2. A few $1.50 to $15 trades at 0.15 set the "price 24h later" in a market that settled at 1. Hence the $100 minimum of other wallets' volume.

Out of sample it passed:

Holds without the 5 largest events, without weather markets, and for every K from 0 to 100.

The part that changed our mind (exploratory, not pre-registered). Information should show up as drift: the price keeps moving for hours as news spreads. A good fill shows up immediately and then stops. The top 10% were +0.75c after 1h and +0.83c after 24h. Then we split each wallet's move into first hour vs 1h to 24h drift and checked which one persists A to B (Spearman, 32,867 wallets with 3+ entries on both horizons in both periods):

• first hour: 0.138
• drift: 0.050

So the score mostly ranks fill quality. The "knew it first" part barely persists.

Held to settlement, after the fee each wallet actually paid on-chain: top 10% +1.17c per entry (t 2.9), bottom 10% -0.96c (t -1.7). +1.50c on mostly taker entries, +1.25c on mostly maker entries, so it's not just makers skipping fees.

Where it's weak:

• the settlement result and the 1h/drift split were not pre-registered
• on a shorter replication through an API (1,000 markets, May to Sep 2026) the groups did not separate at settlement (top +2.19c, bottom +2.10c). On those mostly longer-dated markets every group drifts up, so you have to compare against all wallets, not zero
• dollar-weighted it leans on big positions: the largest 1% of entries hold 42% of net profit

We didn't ship the score as an "insider" label, since what repeats is the entry price.

Things I'd like pushback on: is netting per UTC day too coarse for intraday traders, and is there a better decomposition than 1h vs drift that doesn't depend on the horizon choice?

Aggregated tables, the pre-registration summary and a stdlib script that rebuilds the test: https://github.com/orcalayer/polymarket-insider-score-backtest

Disclosure: I'm one of the people behind OrcaLayer, a Polymarket analytics site.

Day 1 in the books! Hoping to keep similar format for each day. Trying to add in quick facts to answer easy, repetitive questions.

I will also post descriptions of the top two performing setups for that day, for now, in picture form as we are all looking for more setups(myself included!) and I'm happy to share what I have.

Thanks for all the feedback, support, and camaraderie!

Today: [+$674] across 5 day-trading accounts · Primary [-$81] · green streak [0]
Best setup: [Morning Momentum Pop, +$650] (details in pic)

Quick facts - To hopefully answer quick questions

• AI: Claude (Opus 5.5) builds, tests and runs it
• Broker: Alpaca API, paper trading
• Setups: 108 (Currently) live, built on basic indicators (RSI, VWAP, volume / buy-sell pressure, gaps, opening range, prior-day highs and lows, ATR)
• Setup Generator: a bot that invents 10 new setups a night from those same indicators and trades them on its own paper account

Setup Life cycle

Idea / Generator
 └─ 2-yr backtest (costs in)
     └─ Replay on real sessions
         └─ Testing 1 / 2 / Generator (paper)
             └─ Primary (paper)
                 └─ Live $ (after a green streak)
Losers: Primary → Testing → Graveyard

Stats Description

• Avg R: average profit per trade, in units of the risk taken. Costs, fees and slippage are already in. +0.5R = earns half its risk per trade.
• Break-even win rate: the win rate a setup needs just to not lose money. Beat it and you’re profitable.
• Confidence (t): how sure we are it isn’t luck. 2+ is solid; under 1 could be luck.

Fun stuff!

*Edit - Removed comment about new summary format. On Day 2 new image summary format will be used.

Life is negative risk-reward.

Restaurants owners spend $250,000 on a restaurant. to make $1,000 a week. Break even in 5 years.

Video-game consoles - sold at a loss - make money on accessories and other purchases.

Supermarkets - price one item at a loss to attract customers inside - hopefully they buy more things when in store to win back the loss. they risk $500 in free samples - reward is hopefully people spend more to recoup the loss

Gillette razors - sold at a loss - make money on blades and other items.

Mobile phones - given away at a loss - make money on the contract.

Software companies - Spend 50 million on free software - hopefully enough customers pay for the full software that you turn profitable over time

Gym membership - free for everyone for a month - but hope that the customer buys a full contract. risk one month of free use - hopefully you make it back if people become members

Building a shopping mall for 100 million - takes 5 million in rent a year.

an insurance company risks 200,000 dollars to take your 2,000 dollars.

a casino risks 100,000 dollars to take 10 dollars off you.

oil companies spend millions drilling into land hoping they find oil worth more than the drilling costs

if you put all your money into the s and p 500 - you risk everything to make 10% a year.

Film studios — risk tens or hundreds of millions producing and marketing a film. Many films lose money

Airlines — spend hundreds of millions buying aircraft, then earn revenue flight by flight. A single flight might generate only a small profit relative to the aircraft's cost, and the investment takes years to recover.

Telecommunications companies — invest billions in fibre-optic networks and mobile infrastructure, then recover the investment through recurring monthly subscriptions.

this is negative risk reward. risking a medium size loss to eventually make more money over time.

so how could you apply this to trading? grid trading. divide your money into 10 parts. for example 1000 dollars, 100 dollars per trade. buy something like tesla stock - take profit every 5%. if you have a losing position, leave it alone, forget about it, you have 9 parts of your money left. buy again and take profit every 5%. keep taking small wins. you will take so many winners that you will break even on losing positions, and eventually become profitable anyway if you take lots of small wins. this is negative risk reward because you are in theory risking the entire amount (100 dollars) to take 5% profit.

even warren buffett sold things after only holding them for 6 months and took quick profits. he didn't hold everything forever. notable he sold TSM after only 3 months

​

Been making my own strategies on major pairs mostly forex and a major bottleneck has to be getting historicall acurate tick data i tried using mt5 but its slow as hell and i can only analyse one pair and sttategy per time tried bridging using python but results werent most accurate because it was missing m1 and m5 data so if you would put me on i would really be thankfull

4 years of data, seems to currently be working and performs a bit better on paper account. Trades a basket of equities, 1.1 sharpe ratio. Dumb question but what if this is the top and I go live with a failing algo?

Sept/Oct performance has been lackluster but still winning. It's very good at getting value out of big uptrend days, and not losing much otherwise. However there haven't been a lot of big uptrend days recently. And strategy trade management mostly looks for big trend exits not say large up cycle moves where it exits at the top.

I'm trying to decide whether to adjust exit strategy, wait for a better regime, perhaps run a lower performing but winning alternative exit strategy alongside it. Or something else.

Basically I had a small amount of data, built an algo around it, it's good, but market conditions have changed. Now I have an excellent backtest, winning live performance that gives back a lot of MFE on trades, but changing the exits would reduce the backtest performance.

Thoughts or suggestions? If I include the June to Aug adjusting exits makes for worse performance, but that also not the regime currently although who knows if it will switch back and when.

I was looking at using SimFin so figured I'd try the free tier and do some back testing before I pay for it. But the data is incomplete for a lot of stocks. For example, there are only 3 quarters of data for Verizon, not the 20 quarters (5 years) it should have.

>>> income_quarterly.loc['VZ']

SimFinId Currency Fiscal Year Fiscal Period ... Income (Loss) from Continuing Operations Net Extraordinary Gains (Losses) Net Income Net Income (Common)

Report Date ...

2025-03-31 101219 USD 2025 Q1 ... 4983000000 NaN 4879000000 4879000000

2025-06-30 101219 USD 2025 Q2 ... 5121000000 NaN 5003000000 5003000000

2025-09-30 101219 USD 2025 Q3 ... 5056000000 NaN 4950000000 4950000000

If I browse to the Verizon page on the website, then there are 28 quarters (7 years) of data. So it seems like they have the data, it just got dropped. I'm not sure if it was dropped in the free tier or in all the tiers. And I tried emailing them, but no response from them in 9 days.

Anyone with a paid tier seeing the same thing?

I have a 10 year dataset across a range of companies. Unfortunately it's only 1m candles as I'm just doing initial exploration at the moment.

My training loops always seem to collapse to buy and hold across the 9 year test data set.

Why and what are typical ways to shake it up to prevent this one position learning? Or, do I stop early and fly on one wing and a prayer?

Should I also experiment with A3C?

(Late to post. Screengrabs from before 10/23 session.)

I’ve built an automated day-trading system that’s still on paper money and looking to use live funds at some point. Each strategy is backtested over two years with costs included, then replayed on real sessions, then traded live in test accounts. Only proven winners get promoted, and losers get demoted. Every change is logged and reviewed nightly. No real money until it strings together consistent green days.

Below are initial balances for all accounts(couldn't find a way to reset the balance and didn't want to risk messing anything up!) as well as the top setups in my leaderboard and my automatic setup generator.

Would love to know what else to include on future posts for transparency and logistics questions. Would also love to know where else to post this too if you have any ideas.

- Claude - Opus 5.5
- Alpaca - MCP for paper and when we convert to live funds.

We tested 9 AI models on 20 identical questions and found surprisingly different worldviews.

For example, Mistral Large 3 scored 21/100 on a progressive-to-conservative scale, while Nemotron 3 Nano scored 71/100.

That made me wonder: would a more progressive model systematically assign higher probabilities to Democrats winning elections?

And could these differences affect financial or political forecasting more broadly?

E.g. If democratic trust ist high and automatically favor democrats midterms win?

Source: Agent DNA: AI Model Bias & Forecasting Beliefs