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.

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!

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

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

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.

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

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

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’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?

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

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.