Pic related is equity curve (Ignore labels on the side they are wrong I was updating tonnes of graphs and couldn't be bothered to keep changing the labels) X axis is amount of candles strategy has been running for (or time in other words). Y axis is equity curve.
I have overfitted the data to find optimal parameters but before overfitting, and just testing the theory of the strategy, it is still profitable. Pic related is after overfitting but equity curve before overfitting looks similar. See below pics for overfitted
Here is a graph of one of the parameters being overfitted. X axis is the parameter. Y axis is profitability (again, ignore labels titles on the chart they are wrong)
Here is another parameter being overfitted. Y is parameter. X is profitability.
So as you can see on the two graphs above, there are clearly sweet spots for the strategy. However, is the fact that clear sweet spots arise an indication that the strategy is working due to luck and not the theory being correct?
So if i did have an edge, would there not be multiple peaks and less of a clear place where the strategy works? any statistics anons can help me out? thanks
holy fuck. That's it i'm done. Fuck this place I'm going back to elite trader.
Blake Hill
it's when you not nut
Ethan Morgan
I'm doing my masters in Finance and I know a lot about financial markets. Are you talking about some meme technical analysis?
Kayden Cook
كيك
Juan Smith
*hedge
Connor Brown
If you're using OHLC data, make sure you're not leaking future information. Remember that the close doesn't take place until... Well, the close of the trading day (or observational period in your data). You need to shift all your data down one row.
Jonathan Perry
Also I'm under the suspicion you're using an ensemble model (tree based?), looks like you have number of learners and some regularization term as hyperparams.
Keep in mind tree based ensembles CANNOT EXTRAPOLATE meaning they'll fail if presented with data they've never seen before. But this can be overcome by clever feature engineering.
To see if indeed you found alpha, you need to backtest it, mimicking stop losses on each trading period (day, hour, etc). Good luck user
Dylan Long
yeh of course im not doing that.
no its nothing fancy. Just brute force try every single param and build a graph. I already have the strategy. Just finding optimal parameters. The question i'm asking is, if my strategy is better than random would the curves look like mine do, or something different? I cba to do a pure random walk and build all the graphs and shit again multiple times.
Jacob Hernandez
>To see if indeed you found alpha, you need to backtest it, mimicking stop losses on each trading period (day, hour, etc). Good luck user Ok I will build this graph now and let me know what you think? just a sec.... backtesting is done in javascript so its slow as fuck lol. cba to change it all to rust...
Elijah Williams
X axis is timeframe. (1 min candles - 30 min candles). Y axis is equity.
Even though the far right looks bad, It's still 1.012 equity. So 1.2% returns over a few months. No losses on any timeframe. Data was has been overfitted to the 5 min though but even without overfitting i suspect results will be similar but will look something like the graph in my next post
Let me clarify my previous remarks, to backtest you need to simulate entry/exits. You can do this in a table. In Python, you'd pass your observations into your model.predict() and the result would be your entry, exit, hold decision (assuming that's how your model works; in, out). Now, if you're model says to buy, then you'd have to calculate entry price - exit price (the observation/row where model.predict = exit). During the time periods between enter and exit, if price fell
Carter Wilson
>How do I know if i Have discovered an edge? That's an easy one. If you're posting about it on biz, you haven't.
Parker Hall
Once you have this p/L column correctly made from this, you just sum it up.
Assuming long-only: P/L = tn_modelsaysSell - t1_modelsaysBuy. Where t1... tn (time that position was held).
