PS C:\script\python> py .\talib_rf_predict05d.py NVDA 10 C:\\intel\\tmp Starting multi-class sliding window prediction model... Loading multi-class dataset... Loading dataset... [*********************100%***********************] 1 of 1 completed Dataset shape: (2477, 10) Creating sliding window features with window size 10... Sliding window X shape: (2468, 80) Target shape: (2468,) Training data shape: (2458, 80), (2458,) Target classes: [-2, -1, 0, 1, 2] Training Random Forest Classification model for multi-class problem... Model trained successfully! Model Evaluation: Accuracy: 0.5102 Generating predictions for all data points... Predictions for the last 10 rows (future predictions): date predicted_target1 prob_class_-2 prob_class_-1 prob_class_0 prob_class_1 prob_class_2 2458 2025-03-11 0 0.027024 0.077202 0.405948 0.359891 0.129935 2459 2025-03-12 1 0.071112 0.106218 0.297869 0.341895 0.182906 2460 2025-03-13 1 0.078943 0.132997 0.238572 0.345285 0.204203 2461 2025-03-14 1 0.079349 0.249104 0.222090 0.302085 0.147372 2462 2025-03-17 -1 0.076433 0.324325 0.257043 0.213218 0.128981 2463 2025-03-18 -1 0.068432 0.319259 0.240611 0.264642 0.107056 2464 2025-03-19 -1 0.067207 0.482924 0.207085 0.191639 0.051145 2465 2025-03-20 -1 0.156388 0.469793 0.121483 0.183465 0.068870 2466 2025-03-21 -1 0.297349 0.362623 0.165872 0.160985 0.013170 2467 2025-03-24 -2 0.439722 0.349954 0.117044 0.056326 0.036954 Historical predictions (showing last 20 rows): date predicted_target1 actual_target1 prob_class_-2 prob_class_-1 prob_class_0 prob_class_1 prob_class_2 is_future 2438 2025-02-10 -1 -1 0.020083 0.517353 0.321315 0.107406 0.033843 False 2439 2025-02-11 0 0 0.009311 0.239575 0.611336 0.094006 0.045773 False 2440 2025-02-12 -1 -1 0.048878 0.639140 0.196945 0.083748 0.031289 False 2441 2025-02-13 -1 -1 0.049512 0.735499 0.107091 0.086861 0.021037 False 2442 2025-02-14 -2 -2 0.700107 0.177625 0.067284 0.046959 0.008025 False 2443 2025-02-18 -2 -2 0.827222 0.098758 0.051928 0.016765 0.005327 False 2444 2025-02-19 -2 -2 0.908148 0.037407 0.045926 0.007778 0.000741 False 2445 2025-02-20 -2 -2 0.938889 0.050000 0.000000 0.011111 0.000000 False 2446 2025-02-21 -2 -2 0.944444 0.011111 0.022222 0.000000 0.022222 False 2447 2025-02-24 -2 -2 0.922222 0.033333 0.032791 0.000000 0.011653 False 2448 2025-02-25 -2 -2 0.900855 0.069801 0.022792 0.005128 0.001425 False 2449 2025-02-26 -2 -2 0.835901 0.056209 0.065640 0.023203 0.019048 False 2450 2025-02-27 -1 -1 0.082885 0.695050 0.166262 0.027243 0.028560 False 2451 2025-02-28 -1 -1 0.018846 0.758767 0.173954 0.033041 0.015392 False 2452 2025-03-03 0 0 0.052071 0.098434 0.709743 0.108765 0.030987 False 2453 2025-03-04 0 0 0.026606 0.049322 0.759066 0.146354 0.018652 False 2454 2025-03-05 0 0 0.029191 0.120560 0.670985 0.108920 0.070344 False 2455 2025-03-06 1 1 0.016852 0.073677 0.169281 0.708499 0.031690 False 2456 2025-03-07 0 0 0.021011 0.071554 0.750604 0.090443 0.066388 False 2457 2025-03-10 1 1 0.031771 0.068053 0.155295 0.684852 0.060029 False Performance metrics on historical data: Accuracy: 0.9296 Generating confusion matrix... Done!