[tiredfatdude]

If you glance over the whitepaper, it's obvious this is a completely ridiculous model. Trying to predict intraday price movements with previous day volume numbers and open interest as features? Please. Also, selling the "model" predictions for $365 a year is the real scam -- if he had any confidence in the model, he'd get a private backer and be trading size out of his own account rather than making $50 bets.

[madchops1]

It's an indicator to assist in trading decisions not a crystal ball. And your analysis of my analysis is incorrect. I do have private backers using this. But I don't see why I can't share as well and have multiple streams of revenue. I post all the results so you can see for yourself. I'm not rich and have only been day trading for a year or two. This is for the little guy not the big guy. So I trade and show examples as someone who may not have lots of liquidity available to show how you too could learn to do this.

[rainhacker]

You should change the following in your whitepaper though:

"..It contains information that is confidential and privileged. If you have received this document in error, please notify the sender and delete this file."

[madchops1]

Ya I should remove that. Ty

[sireat]

I mean it is possible that the model could work for someone with access to more capital and better fee structure than OP making $50 bets.

Still, this seems like a model that will likely work about 95% then fail on outliers ie "picking pennies in front of a bulldozer" model.

[madchops1]

The nice thing about QM is that 1 tick is a 12.50 dollar move. So its easy to cover fees. Today those were $50 profits. 2 shares moved 2 ticks. I use stops and limits of course. So my risk was about $100. Its not the same as options.

[HockeyPlayer]

A strategy that goes long or short QM doesn't have an asymetrical return profile. I.e., it isn't "picking up pennies". An example of that is selling options.

[madchops1]

Thanks HockeyPlayer spot on.

[tiredfatdude]

I think you misunderstand, what you're doing is even worse than picking up pennies, your strategy just boils down to punting futures with no hedge or any form risk management to your open position. You're just gambling on coinflips. If you backtest your "model" you'll find that external macro event induced crude oil moves will completely wipe you out because you have no hedge against them.

[madchops1]

Your are correct about no hedge agains outlying macro events. But I'm not suggesting using this without any other variables in your trading decision. It may help make a trading decision.

[madchops1]

Also you should always place a stop and limit order. So you don't get completely wiped out. Its very common.

[madchops1]

I do appreciate the criticism though I honestly thought I'd get more. Results, results, results

[soVeryTired]

Have a look at Carver's book 'Systematic Trading' and maybe 'Expected Returns' by Ilmanen. You also need to get a proper backtest going. A black box and a diary won't get you very far.

[madchops1]

I am doing my training/evaluation with a data split of 70/30. Doesn't that qualify as a proper backtest?

[soVeryTired]

I don't really know what you mean by evaluation. But you need to be able to (faithfully) generate all the positions your system would take through time, and also to generate all the returns you would have made through time.

Aside from pure P&L, you should be looking at how much risk your system is taking, and under what conditions it's doing badly. All backtests are overfit: their use is mostly in identifying problems with your strategy, rather than predicting how much money you'll make.

One question you'd get asked if you were proposing this in a real trading environment is this: what is it about the QM emini contract that makes this work? Does it work for other energy contracts? For other commodities? For bonds, or equities? If not, why not?

[madchops1]

Basically I have a dataset and I train my model with 70% and then evaluate its guesses against the remaining 30%. Hence a baseline is created and I can see if my model performs better.

It took some doing to get this model to perform well. I did this by adding features that help recognize patterns in the time series data.

The features I created are not specific to QM as they are technical (eg. numbers, not news), and time-series related. So the models should work with any historical dataset with the same fields.

My goal is to add another future at some point.

[soVeryTired]

I don't understand your baseline.

I feel like you're talking past me a little. The first thing you need to do is generate all the positions your system would have taken over as many years as possible, and figure out at what times you make and lose money. Otherwise you don't have a backtest.

[madchops1]

I will. Thank you for the advice.

[minimaxir]

A statistical model based on bad/dependent features is essentially just random guessing, which in this case the model just makes favorable guesses.

[madchops1]

I experimented with the features until I got good results in my evaluations. If I am getting favorable guesses, how does that point to bad features. Favorable guesses is what I was going for in order to assist in trading decisions.

I use a 70/30 split of training/eval

[madchops1]

Just look for feedback. I use many historical price values other than volume. Plus some specific things I have added for pattern recognition in time-series data. That's the special part.

[hellofunk]

@madchops1 This comment reveals just how seriously we should take your work. This article should be flagged.

[madchops1]

touche. Sorry for my negative reaction.

[hellofunk]

Note to other readers: the comment was changed.

[madchops1]

I reacted that way because dude said he read the white-paper and somehow took this as a historical analysis of volume. Which it is not.

I use historical open, high, change, last, settle, prev. day open interest, plus several other fields I use to help recognize patterns and properly weight time-series data.

[madchops1]

It was changed. I said "your just jelly". Sorry. I changed it.