Using Machine Learning to Predict Whether Weekly High Will Beat Median Weekly High

Hello everyone! I'd like to share the results of a model I've been working on where we attempt to predict whether a stock will beat its median high, relative to the Monday open. I won't reveal the variables I used (other than it's a handful - less than 10) but I will discuss the parameters I use and the results across numerous ETFs and some equities. To restate, the target variable was whether this week's high would surpass the median high relative to the Monday open. To avoid any data leakage, the median value used was derived from the training data. The model uses a rolling a 10 year/1 month train-test period (starting 2005) - anecdotally I find the 1 month rolling period to be superior to using something like a 10 yea/1 year period. For accuracy criteria I only look at signals where the probability is greater than 60% or less than 40%. There's not much more to it than that - but I'm happy to answer any questions should there be any. For the results, let's start with SPY. You'll see the results in the table below. Strategy 1 is where we buy at Monday open and sell the moment the median high (or hurdle) is hit. Strategy 2, we still buy at Monday open but hold until the end of the week and sell at Friday close. Baseline here refers to buying every week at Monday open and selling when the hurdle is hit. |Metric (SPY)|**Strategy 1**|**Strategy 2**|**Baseline**| |:-|:-|:-|:-| |**Total Traded Mondays**|171|171|539| |**Model Win Rate (Hits/Hurdle)**|70.18%|57.89%|49.72%| |**Average Return/Trade**|0.26%|0.55%|0.14%| |**Avg Max Profit Potential/Week**|2.15%|2.15%|1.35%| |**Cumulative Profit**|54.02%|142.50%|109.52%| |**Average Hurdle Imposed**|0.93%|0.93%|0.96%| As you can see our model performs quite impressively on SPY. Unsurprisingly, since we are using the median, baseline accuracy is about 50% and we surpass that with an accuracy of 70% of hitting that hurdle. In the buy/hold strategy our win rate is 59.89% with an avg return of 0.55% (28.6% annualized) and a significantly higher maximum potential profit (2.15% vs. 1.35%). It's also fairly selective as well only giving a signal 32% of all active weeks. When looking to see whether probabilities less than 40% accurately signal that SPY won't hit its median hurdle, we see an impressive 71.34% accuracy there for a **total blended accuracy of 70.73%.** Perhaps unsurprisingly this genealizes across other ETFs such as QQQ (blended accuracy: 65.27%), DIA (blended accuracy: 66.87%) and even IWM (blended accuracy: 67.09%; results for all three below) |Metric (QQQ)|**Strategy 1**|**Strategy 2**|**Baseline**| |:-|:-|:-|:-| |**Total Traded Mondays**|149|149|539| |**Model Win Rate (Hits/Hurdle)**|69.13%|57.05%|53.99%| |**Average Return/Trade**|0.28%|0.68%|0.21%| |**Avg Max Profit Potential/Week**|2.75%|2.75%|1.80%| |**Cumulative Profit**|48.05%|153.68%|187.13%| |**Average Hurdle Imposed**|1.25%|1.25%|1.25%| |Metric (DIA)|**Strategy 1**|**Strategy 2**|**Baseline**| |:-|:-|:-|:-| |**Total Traded Mondays**|142|142|539| |**Model Win Rate (Hits/Hurdle)**|72.54%|61.97%|52.13%| |**Average Return/Trade**|0.28%|0.62%|0.16%| |**Avg Max Profit Potential/Week**|2.18%|2.18%|1.32%| |**Cumulative Profit**|45.50%|126.68%|122.77%| |**Average Hurdle Imposed**|0.95%|0.95%|0.96%| |Metric (IWM)|**Strategy 1**|**Strategy 2**|**Baseline**| |:-|:-|:-|:-| |**Total Traded Mondays**|145|145|539| |**Model Win Rate (Hits/Hurdle)**|68.28%|53.10%|51.76%| |**Average Return/Trade**|0.28%|0.41%|0.09%| |**Avg Max Profit Potential/Week**|2.72%|2.72%|1.87%| |**Cumulative Profit**|46.94%|64.63%|48.44%| |**Average Hurdle Imposed**|1.33%|1.33%|1.35%| On non-ETF equities or commodity ETFs it also tends to show relatively good accuracy on the positive side (probability > 60% - not so much on the short side). For example you'll see GOOG and USO below. |Metric (GOOG)|**Strategy 1**|**Strategy 2**|**Baseline**| |:-|:-|:-|:-| |**Total Traded Mondays**|90|90|539| |**Model Win Rate (Hits/Hurdle)**|67.78%|55.56%|55.47%| |**Average Return/Trade**|0.39%|0.66%|0.37%| |**Avg Max Profit Potential/Week**|3.56%|3.56%|2.64%| |**Cumulative Profit**|36.68%|67.75%|541.97%| |**Average Hurdle Imposed**|1.77%|1.77%|1.77%| |Metric (USO)|Strategy 1|Strategy 2|Baseline| |:-|:-|:-|:-| |**Total Traded Mondays**|91|91|480| |**Model Win Rate (Hits/Hurdle)**|69.23%|63.74%|52.50%| |**Average Return/Trade**|0.83%|0.98%|0.08%| |**Avg Max Profit Potential/Week**|4.77%|4.77%|3.07%| |**Cumulative Profit**|107.28%|102.80%|22.46%| |**Average Hurdle Imposed**|2.10%|2.10%|2.15%| Well those are my results! i'd love to hear what people think; I'm quite happy with the model, but perhaps it's not as impressive as I think it is. With respect to a trading strategy, I'm still trying to figure that out. I tried a multi-ticker approach using a SP100 universe, but that didn't seem to work particularly well. I'd love to hear thoughts regarding that as well, should there be any.