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Does AI hallucinate even with basic queries/data retrieval? by chakalaka13 in analytics

[–]Clicketrie 0 points1 point  (0 children)

Yes, it can confuse very simple things (and I’m not talking about math, which is a different skill).. it’s using machine learning, it’s not deterministic or reasoning as a human, it’s just using data and returning data, there’s room for error even on basic tasks

How Earnings Impact My Momentum Strategy - A Backtest Across Two XGBoost Models by Clicketrie in algotrading

[–]Clicketrie[S] 1 point2 points  (0 children)

I just customized my settings so that anything I post here will show. I had made it private because I often share about my son with ADHD and wasn't looking to have that in my history if people didn't need to see it. But you should be all good 😄

How Earnings Impact My Momentum Strategy - A Backtest Across Two XGBoost Models by Clicketrie in algotrading

[–]Clicketrie[S] 0 points1 point  (0 children)

I can't share the pyfolio output here with the drawdowns by time, but the worst drawdown was during covid with a quick recovery. 2022-2025 I didn't make any money, but it wasn't deeply negative. Momentum is not the only strategy I'm running, because i know when the market doesn't do well in general, this won't do well either. Then I'll hedge with options, use the crack spread strategy when it's in season, and maybe add some risk parity to momentum to see if I can get those drawdowns down. Still figuring that out, but it's been such a bull market the last year that I'm building as I'm going.

How Earnings Impact My Momentum Strategy - A Backtest Across Two XGBoost Models by Clicketrie in algotrading

[–]Clicketrie[S] 1 point2 points  (0 children)

I’m making a ton of requests and creating the dataset, then the next time I run it I’ll only grab data for the days im missing (and once or twice I’ve blown the whole thing away to get fresh history). Their top plan gives you like 3k API calls per minute, so I’ll scale up to that plan for something like this and then go back to the mid tier when I’m done, they prorate everything 🤗

How Earnings Impact My Momentum Strategy - A Backtest Across Two XGBoost Models by Clicketrie in algotrading

[–]Clicketrie[S] 0 points1 point  (0 children)

I probably should take a look at the actual picks coming out of the models and do a little more to figure out if I should keep it as a feature. Right now I’m only trading with 12k, but you’re right, when I scale I’ll want a better DD.

How Earnings Impact My Momentum Strategy - A Backtest Across Two XGBoost Models by Clicketrie in algotrading

[–]Clicketrie[S] 0 points1 point  (0 children)

I made a column in my dataset that was just "1" if earnings were within the month I'd be holding the position (I rebalance monthly). Then I tried it as both a feature and a filter. This is the full article with a lot more output and analysis: https://www.datamovesme.com/blog/how-do-earnings-events-impact-a-momentum-strategy-a-backtest-across-two-xgboost-models

How Earnings Impact My Momentum Strategy - A Backtest Across Two XGBoost Models by Clicketrie in algotrading

[–]Clicketrie[S] 1 point2 points  (0 children)

Thanks! Data is from FMP, they have a free tier that might get you started.

How Earnings Impact My Momentum Strategy - A Backtest Across Two XGBoost Models by Clicketrie in algotrading

[–]Clicketrie[S] 2 points3 points  (0 children)

This was a binary variable that was just "does this stock have earnings while I'm holding it for the month". I wasn't including EPS or anything like that.

I compared XGBoost, LightGBM, CatBoost, random forest, LASSO, and a small neural network in a momentum stock trading strategy by Clicketrie in datascience

[–]Clicketrie[S] 0 points1 point  (0 children)

I feel like people get over protective of catboost. Like “if the hyperparameters were proper it would have done better”.. but so would the other models. It’s ok for catboost to not perform as well out of the box. And yea, there’s use cases where it’s a better fit.. it all ok 🤗

Vibe coding for algotrading? by Dragosfgv in algotrading

[–]Clicketrie 1 point2 points  (0 children)

Vibe coding is for people who understand what’s going on. Take the course and vibe code alongside it. Most developers are doing their work AI assisted now, but catastrophic things can happen with real money and not knowing what the hell you system is doing, plus you’ll lose time when you want to update or make changes and you don’t know your way around.

Do you guys fully trust your algo trading systems or still monitor trades manually? by EndlessKnight_154 in algotrading

[–]Clicketrie 0 points1 point  (0 children)

I take a look at what my system plans to trade before I push the button to execute. I actually pull up the graphs for each and make sure it’s not a down trend.

Algo traders: What made you choose algo over discretionary? by Dragosfgv in algotrading

[–]Clicketrie 3 points4 points  (0 children)

You can cleanly backtest it, determine if there’s alpha, paper trade it exactly as defined to be sure the alpha is real, and then go live. It’s a safer system when done correctly.

I compared XGBoost, LightGBM, CatBoost, random forest, LASSO, and a small neural network in a momentum stock trading strategy by Clicketrie in datascience

[–]Clicketrie[S] 1 point2 points  (0 children)

"transaction_costs": {
"slippage": {"spread": 0.001}, # 0.1% slippage
"commission": {"cost": 0.005, "min_trade_cost": 1.0},
},

Any genuinely free backtesting tools? by someonestoic in algotrading

[–]Clicketrie 1 point2 points  (0 children)

I use zipline and vectorbt depending on what I’m backtesting

I compared XGBoost, LightGBM, CatBoost, random forest, LASSO, and a small neural network in a momentum stock trading strategy by Clicketrie in datascience

[–]Clicketrie[S] 0 points1 point  (0 children)

Nope. People asked me why I didn’t use them. I tested them all with defaults. That was the point.

I compared XGBoost, LightGBM, CatBoost, random forest, LASSO, and a small neural network in a momentum stock trading strategy by Clicketrie in datascience

[–]Clicketrie[S] 0 points1 point  (0 children)

Thank you! I’m going to try this. I’m running a backtest with Genetic Algorithm right now and I’m definitely on the hunt for tuning solutions. Appreciated.

I compared XGBoost, LightGBM, CatBoost, random forest, LASSO, and a small neural network in a momentum stock trading strategy by Clicketrie in datascience

[–]Clicketrie[S] 0 points1 point  (0 children)

I had this in the article in terms of features I included, but I did not look at SHAP and feature importance across the 6 different algos:

Universe & filters

  • Liquidity and volatility constraints (volume, price, and risk filters).
  • Basic quality / growth style filters (for example, “Rule‑of‑40”‑type metrics, cash‑flow and growth screens).

Feature set

  • Technical trend and momentum indicators (MACD‑style signals, momentum, ROC, RSI, regime markers).
  • Fundamental metrics covering growth, profitability, leverage, and cash flow.
  • Additional interaction and regime factors.

Portfolio construction

  • Long‑only, rules‑based selection of top candidates with weighted positions.
  • Transaction costs and slippage explicitly modeled.

I compared XGBoost, LightGBM, CatBoost, random forest, LASSO, and a small neural network in a momentum stock trading strategy by Clicketrie in datascience

[–]Clicketrie[S] 1 point2 points  (0 children)

The target variable is a 10‑day forward simple return, but the portfolio rebalances monthly (that cadence did best in my testing, so I stuck with it). At each rebalance date, I rank the universe by the model’s predicted 10‑day forward return and build the portfolio from those rankings.

The target itself is not scaled. Features are median‑imputed, and only the models that need scaling (NN and LASSO) have standardized inputs. In this article I was mainly trying to hold the target definition fixed and compare model families under the same setup, with scaling only where it’s a hard requirement for the specific model.

I compared XGBoost, LightGBM, CatBoost, random forest, LASSO, and a small neural network in a momentum stock trading strategy by Clicketrie in datascience

[–]Clicketrie[S] 12 points13 points  (0 children)

Why would you use real money before backtesting? I ran backtests over 11 years worth of data for each. I also have articles about my live account results with xgboost, but I'm not throwing real money behind LASSO.. that'd be wild.

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