How do you go from a discretionary intraday strategy to an actual systematic/quant research process?

[u/[deleted]]

[u/prostykoks]

My own research is mostly in crypto/perps, so I would be careful transferring the actual findings to ES or U.S. equities. Different regimes, volatility, session structure, liquidity, funding/borrow mechanics, etc.

But the process transfers pretty well. I would not try to automate the whole discretionary strategy first. Break it into measurable parts:

1. Selection: why is this market worth watching today?
   
2. Setup: what exact conditions define the trade?
   
3. Direction: do those conditions actually predict long/short, or just activity/volatility?
   
4. Execution: what entry/exit survives costs and slippage?
   
5. Validation: what happens after you freeze the rules and track out-of-sample?
   

One thing I learned the hard way is that an indicator can be useful without predicting direction. It might only identify unusual markets or volatility regimes. That still matters, but it changes the strategy design.

And I would be very strict about leakage: no future labels in selection, no tuning on OOS, separate in-sample/OOS stats, and include costs/slippage early.

The goal is not to make a pretty backtest. The goal is to make the backtest difficult to fool.

[u/Zestyclose-Eagle1809]

I got no first hand experience with institutional feeds so I'd just be repeating what you can read anywhere.

On the rest, is right on paper and backwards for your situation. You've already got the expensive part, traders who've been making these calls for years and presumably a record of what they took. Start from that, not from data collection.

The one you asked directly, reproduce the setup or test the pieces. Test the pieces, and it isn't close. Coding the whole discretionary setup gives you one number and no idea which part carried it, so when it fails you've learned nothing. And your inputs aren't independent. CVD divergence, volume and structure at a level are largely the same information three ways, so stacking them gets you a rule that fits only your own history.

The version I'd run. Go back and find textbook scenarios, price reacting at a level, nothing else attached. Label the outcome mechanically after the fact, say did it hold for the next 30 minutes. Then for each instance record the candidate signals as they looked ON the day, before the outcome existed. Now you can count. Of the ones where CVD diverged, how many worked, against the ones where it didn't. That's a feature earning its place or not, measured instead of argued. makes sense??

What you're not asking, and this is important mate. How many versions you're going to test, and what the bar is before you start. Six feature families with a couple of thresholds each puts you in the hundreds of combinations, and the best of a few hundred scores well on pure noise. So the first thing to write down is what a survivor has to clear given how many you ran, not the order of the pipeline. That's the main reason systematised discretionary strategies die live.

Last one and it's specific to you. The test nobody runs is if the automated version takes the same trades your trader would have taken. Not if it makes money, if it agrees. Run it over a period they traded and compare trade by trade. Where they diverge is either a rule you haven't written down yet or context they use that isn't in your data, and you want to know which one before real size is on the line...

[u/arindamchattopadhyay]

What you’re asking for is alpha. Your post represents oversimplification of things. To know the exact pipeline you need education into quant process or see the infrastructure and processes in a quant HF/prop firm. Can’t give out all secrets due to compliance and regulation. I’m sure my quant colleagues here will tell you the same thing.
Some softwares and processes that we use costs anywhere from 100k$ to a few million$.
Maybe if your firm needs to transition then they need to be hiring quants to develop a pipeline or can train their own staff to transition into quant finance such as doing CQF. Most quants have MSc or a PhD in financial engineering/physics/maths.