508 lines
21 KiB
Plaintext
508 lines
21 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"import pickle\n",
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"import plotly.graph_objs as go\n",
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"import latextable\n",
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"from texttable import Texttable\n",
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"from strategy.strategy import (\n",
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" BuyAndHoldStrategy,\n",
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" MACDStrategy,\n",
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" RSIStrategy,\n",
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" ModelQuantilePredictionsStrategy,\n",
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" ModelGmadlPredictionsStrategy,\n",
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" ConcatenatedStrategies\n",
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")\n",
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"from strategy.util import (\n",
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" get_data_windows,\n",
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" get_sweep_window_predictions,\n",
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" get_predictions_dataframe\n",
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")\n",
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"from strategy.evaluation import (\n",
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" parameter_sweep,\n",
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" evaluate_strategy\n",
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")\n",
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"from strategy.plotting import (\n",
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" plot_sweep_results\n",
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")\n",
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"\n",
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"PADDING=5000\n",
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"VALID_PART=0.2\n",
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"INTERVAL='min'\n",
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"METRIC='mod_ir'\n",
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"TOP_N=10"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"Failed to detect the name of this notebook, you can set it manually with the WANDB_NOTEBOOK_NAME environment variable to enable code saving.\n",
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"\u001b[34m\u001b[1mwandb\u001b[0m: Downloading large artifact btc-usdt-1m:latest, 3717.80MB. 12 files... \n",
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"\u001b[34m\u001b[1mwandb\u001b[0m: 12 of 12 files downloaded. \n",
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"Done. 0:0:4.7\n"
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]
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}
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],
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"source": [
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"data_windows = get_data_windows(\n",
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" 'wne-masters-thesis-testing',\n",
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" 'btc-usdt-1m:latest',\n",
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" min_window=0, \n",
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" max_window=5\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"def sweeps_on_all_windows(data_windows, strategy_class, params, **kwargs):\n",
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" result = []\n",
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" for in_sample, _ in data_windows:\n",
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" data_part = int((1 - VALID_PART) * len(in_sample))\n",
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" result.append(parameter_sweep(in_sample[data_part-PADDING:], strategy_class, params, padding=PADDING, interval=INTERVAL, **kwargs))\n",
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" return result"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"buyandhold_best_strategies = [BuyAndHoldStrategy() for _ in data_windows] "
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"\u001b[34m\u001b[1mwandb\u001b[0m: 2 of 2 files downloaded. \n",
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"\u001b[34m\u001b[1mwandb\u001b[0m: 2 of 2 files downloaded. \n",
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"\u001b[34m\u001b[1mwandb\u001b[0m: 2 of 2 files downloaded. \n",
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"\u001b[34m\u001b[1mwandb\u001b[0m: 2 of 2 files downloaded. \n",
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"\u001b[34m\u001b[1mwandb\u001b[0m: 2 of 2 files downloaded. \n",
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"\u001b[34m\u001b[1mwandb\u001b[0m: 2 of 2 files downloaded. \n",
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"\u001b[34m\u001b[1mwandb\u001b[0m: 2 of 2 files downloaded. \n",
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"\u001b[34m\u001b[1mwandb\u001b[0m: 2 of 2 files downloaded. \n",
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"\u001b[34m\u001b[1mwandb\u001b[0m: 2 of 2 files downloaded. \n",
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"\u001b[34m\u001b[1mwandb\u001b[0m: 2 of 2 files downloaded. \n"
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]
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}
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],
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"source": [
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"# Model with gmadl loss\n",
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"SWEEP_ID = 'filipstefaniuk/wne-masters-thesis-testing/s8goxcbz'\n",
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"# SWEEP_ID = 'filipstefaniuk/wne-masters-thesis-testing/v3epl3qk'\n",
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"# train_gmadl_pred_windows = get_sweep_window_predictions(SWEEP_ID, 'train')\n",
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"valid_gmadl_pred_windows = get_sweep_window_predictions(SWEEP_ID, 'valid')\n",
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"test_gmadl_pred_windows = get_sweep_window_predictions(SWEEP_ID, 'test')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
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"outputs": [],
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"source": [
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"# y = test_gmadl_pred_windows[0][2][:, 0, 0]\n",
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"# fig = go.Figure([\n",
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"# go.Scatter(y=y[::100]),\n",
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"# ])\n",
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"# fig.show()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"100%|██████████| 1176/1176 [04:40<00:00, 4.20it/s]\n",
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"100%|██████████| 1176/1176 [04:40<00:00, 4.20it/s]\n",
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"100%|██████████| 1176/1176 [04:36<00:00, 4.26it/s]\n",
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"100%|██████████| 1176/1176 [04:35<00:00, 4.28it/s]\n",
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"100%|██████████| 1176/1176 [04:36<00:00, 4.26it/s]\n",
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"100%|██████████| 1176/1176 [04:30<00:00, 4.35it/s]\n"
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]
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}
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],
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"source": [
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"MODEL_GMADL_LOSS_FILTER = lambda p: (\n",
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" ((p['enter_long'] is not None and (p['enter_short'] is not None or p['exit_long'] is not None))\n",
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" or (p['enter_short'] is not None and (p['exit_short'] is not None or p['enter_long'] is not None)))\n",
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" and (p['enter_short'] is None or p['exit_long'] is None or (p['exit_long'] > p['enter_short']))\n",
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" and (p['enter_long'] is None or p['exit_short'] is None or (p['exit_short'] < p['enter_long'])))\n",
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"\n",
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"gmadl_model_sweep_results = []\n",
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"for (in_sample, _), valid_preds, test_preds in zip(data_windows, valid_gmadl_pred_windows, test_gmadl_pred_windows):\n",
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" data_part = int((1 - VALID_PART) * len(in_sample))\n",
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" params={\n",
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" 'predictions': [get_predictions_dataframe(valid_preds, test_preds)],\n",
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" 'enter_long': [None, 0.001, 0.002, 0.003, 0.004, 0.005, 0.006, 0.007],\n",
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" 'exit_long': [None, -0.001, -0.002, -0.003, -0.004, -0.005, -0.006, -0.007],\n",
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" 'enter_short': [None, -0.001, -0.002, -0.003, -0.004, -0.005, -0.006, -0.007],\n",
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" 'exit_short': [None, 0.001, 0.002, 0.003, 0.004, 0.005, 0.006, 0.007],\n",
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" }\n",
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" \n",
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" gmadl_model_sweep_results.append(parameter_sweep(\n",
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" in_sample[data_part-PADDING:],\n",
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" ModelGmadlPredictionsStrategy,\n",
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" params,\n",
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" params_filter=MODEL_GMADL_LOSS_FILTER,\n",
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" padding=PADDING,\n",
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" interval=INTERVAL,\n",
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" sort_by=METRIC))\n",
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" \n",
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"\n",
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"gmadl_model_best_strategies = [[strat for _, strat in results[:TOP_N]] for results in gmadl_model_sweep_results]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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"ename": "NameError",
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"evalue": "name 'buyandhold_best_strategies' is not defined",
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"output_type": "error",
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"traceback": [
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
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"Cell \u001b[0;32mIn[3], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# Persist best strategies, so that they don't have to be recomputed every time\u001b[39;00m\n\u001b[1;32m 2\u001b[0m best_strategies \u001b[38;5;241m=\u001b[39m {\n\u001b[0;32m----> 3\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbuy_and_hold\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[43mbuyandhold_best_strategies\u001b[49m,\n\u001b[1;32m 4\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mgmadl_model\u001b[39m\u001b[38;5;124m'\u001b[39m: gmadl_model_best_strategies\n\u001b[1;32m 5\u001b[0m }\n\u001b[1;32m 7\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mopen\u001b[39m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcache/1min-best-strategies-v1.pkl\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mwb\u001b[39m\u001b[38;5;124m'\u001b[39m) \u001b[38;5;28;01mas\u001b[39;00m outp:\n\u001b[1;32m 8\u001b[0m pickle\u001b[38;5;241m.\u001b[39mdump(best_strategies, outp, pickle\u001b[38;5;241m.\u001b[39mHIGHEST_PROTOCOL)\n",
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"\u001b[0;31mNameError\u001b[0m: name 'buyandhold_best_strategies' is not defined"
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]
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}
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],
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"source": [
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"# Persist best strategies, so that they don't have to be recomputed every time\n",
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"best_strategies = {\n",
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" 'buy_and_hold': buyandhold_best_strategies,\n",
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" 'gmadl_model': gmadl_model_best_strategies\n",
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"}\n",
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"\n",
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"with open('cache/1min-best-strategies-v1.pkl', 'wb') as outp:\n",
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" pickle.dump(best_strategies, outp, pickle.HIGHEST_PROTOCOL)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"with open('cache/1min-best-strategies-v1.pkl', 'rb') as inpt:\n",
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" best_strategies = pickle.load(inpt)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"# plot_sweep_results(pd.DataFrame([result for result, _ in gmadl_model_sweep_results[0]]), parameters=['enter_long', 'exit_long', 'enter_short', 'exit_short'], round=5, objective='mod_ir')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [],
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"source": [
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"def results_plot(idx, result_buyandhold, result_gmadl_model, width=850, height=500, notitle=False, v_lines=None):\n",
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"\n",
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" fig = go.Figure([\n",
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" go.Scatter(y=result_buyandhold['portfolio_value'], x=result_buyandhold['time'], name=\"Buy and Hold\"),\n",
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" go.Scatter(y=result_gmadl_model['portfolio_value'], x=result_gmadl_model['time'], name='GMADL Informer Strategy')\n",
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" ])\n",
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" \n",
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" if v_lines:\n",
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" for v_line in v_lines:\n",
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" fig.add_shape(\n",
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" go.layout.Shape(type=\"line\",\n",
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" yref=\"paper\",\n",
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" xref=\"x\",\n",
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" x0=v_line,\n",
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" x1=v_line,\n",
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" y0=0,\n",
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" y1=1,\n",
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" line=dict(dash='dash', color='rgb(140,140,140)')))\n",
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" fig.update_layout(\n",
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" title={\n",
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" 'text': f\"W{idx}-{INTERVAL}\",\n",
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" 'y':0.97,\n",
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" 'x':0.5,\n",
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" 'xanchor': 'center',\n",
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" 'yanchor': 'top'} if not notitle else None,\n",
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" yaxis_title=\"Portfolio Value\",\n",
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" xaxis_title=\"Date\",\n",
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" font=dict(\n",
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" # family=\"Courier New, monospace\",\n",
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" size=14,\n",
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" ),\n",
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" autosize=False,\n",
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" width=width,\n",
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" height=height,\n",
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" margin=dict(l=20, r=20, t=20 if notitle else 110, b=20),\n",
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" plot_bgcolor='white',\n",
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" legend=dict(\n",
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" orientation=\"h\",\n",
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" yanchor=\"bottom\",\n",
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" y=1.02,\n",
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" xanchor=\"left\",\n",
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" x=0.02\n",
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" )\n",
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" )\n",
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" fig.update_xaxes(\n",
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" mirror=True,\n",
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" ticks='outside',\n",
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" showline=True,\n",
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" linecolor='black',\n",
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" gridcolor='lightgrey'\n",
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" )\n",
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" fig.update_yaxes(\n",
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" mirror=True,\n",
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" ticks='outside',\n",
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" showline=True,\n",
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" linecolor='black',\n",
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" gridcolor='lightgrey'\n",
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" )\n",
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" fig.write_image(f\"images/eval-w{idx}-{INTERVAL}.png\")\n",
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" fig.show()\n",
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" \n",
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"def results_table(result_buyandhold, result_gmadl_model):\n",
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" table_eval_windows = Texttable()\n",
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" table_eval_windows.set_deco(Texttable.HEADER)\n",
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" table_eval_windows.set_cols_align([\"l\", \"c\",\"c\", \"c\", \"c\", \"c\", \"c\", \"c\", \"c\", \"c\"])\n",
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" table_eval_windows.set_precision(3)\n",
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"\n",
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" table_eval_windows.header([\n",
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" \"\\\\textbf{Strategy}\",\n",
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" \"\\\\textbf{VAL}\",\n",
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" \"\\\\textbf{ARC}\",\n",
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" \"\\\\textbf{ASD}\",\n",
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" \"\\\\textbf{IR*}\",\n",
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" \"\\\\textbf{MD}\",\n",
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" \"\\\\textbf{IR**}\",\n",
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" \"\\\\textbf{N}\",\n",
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" \"\\\\textbf{LONG}\",\n",
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" \"\\\\textbf{SHORT}\",\n",
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" ])\n",
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"\n",
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" strategy_name_result = [\n",
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" ('Buy and Hold', result_buyandhold),\n",
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" ('GMADL Informer', result_gmadl_model)\n",
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" ]\n",
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" for strategy_name, result in strategy_name_result:\n",
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" table_eval_windows.add_row([\n",
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" strategy_name,\n",
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" result['value'],\n",
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" f\"{result['arc']*100:.2f}\\%\",\n",
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" f\"{result['asd']*100:.2f}\\%\",\n",
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" result['ir'],\n",
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" f\"{result['md']*100:.2f}\\%\",\n",
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" result['mod_ir'],\n",
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" result['n_trades'],\n",
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" f\"{result['long_pos']*100:.2f}\\%\",\n",
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" f\"{result['short_pos']*100:.2f}\\%\",\n",
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" ])\n",
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" print(latextable.draw_latex(table_eval_windows))\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\\begin{table}\n",
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"\t\\begin{center}\n",
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"\t\t\\begin{tabular}{lccccccccc}\n",
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"\t\t\t\\textbf{Strategy} & \\textbf{VAL} & \\textbf{ARC} & \\textbf{ASD} & \\textbf{IR*} & \\textbf{MD} & \\textbf{IR**} & \\textbf{N} & \\textbf{LONG} & \\textbf{SHORT} \\\\\n",
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"\t\t\t\\hline\n",
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"\t\t\tBuy and Hold & 0.929 & -13.87\\% & 69.66\\% & -0.199 & 52.09\\% & -0.053 & 2 & 100.00\\% & 0.00\\% \\\\\n",
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"\t\t\tGMADL Informer & 1.306 & 71.83\\% & 69.69\\% & 1.031 & 41.57\\% & 1.781 & 50 & 7.29\\% & 92.71\\% \\\\\n",
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"\t\t\\end{tabular}\n",
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"\t\\end{center}\n",
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"\\end{table}\n",
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"\\begin{table}\n",
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"\t\\begin{center}\n",
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"\t\t\\begin{tabular}{lccccccccc}\n",
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"\t\t\t\\textbf{Strategy} & \\textbf{VAL} & \\textbf{ARC} & \\textbf{ASD} & \\textbf{IR*} & \\textbf{MD} & \\textbf{IR**} & \\textbf{N} & \\textbf{LONG} & \\textbf{SHORT} \\\\\n",
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"\t\t\t\\hline\n",
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"\t\t\tBuy and Hold & 0.549 & -70.35\\% & 73.36\\% & -0.959 & 63.40\\% & -1.064 & 2 & 100.00\\% & 0.00\\% \\\\\n",
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"\t\t\tGMADL Informer & 1.837 & 243.15\\% & 73.38\\% & 3.314 & 25.16\\% & 32.024 & 186 & 18.19\\% & 81.81\\% \\\\\n",
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"\t\t\\end{tabular}\n",
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"\t\\end{center}\n",
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"\\end{table}\n",
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"\\begin{table}\n",
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"\t\\begin{center}\n",
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"\t\t\\begin{tabular}{lccccccccc}\n",
|
|
"\t\t\t\\textbf{Strategy} & \\textbf{VAL} & \\textbf{ARC} & \\textbf{ASD} & \\textbf{IR*} & \\textbf{MD} & \\textbf{IR**} & \\textbf{N} & \\textbf{LONG} & \\textbf{SHORT} \\\\\n",
|
|
"\t\t\t\\hline\n",
|
|
"\t\t\tBuy and Hold & 1.016 & 3.33\\% & 52.45\\% & 0.064 & 38.42\\% & 0.006 & 2 & 100.00\\% & 0.00\\% \\\\\n",
|
|
"\t\t\tGMADL Informer & 0.739 & -45.82\\% & 52.21\\% & -0.878 & 42.46\\% & -0.947 & 35 & 4.70\\% & 93.05\\% \\\\\n",
|
|
"\t\t\\end{tabular}\n",
|
|
"\t\\end{center}\n",
|
|
"\\end{table}\n",
|
|
"\\begin{table}\n",
|
|
"\t\\begin{center}\n",
|
|
"\t\t\\begin{tabular}{lccccccccc}\n",
|
|
"\t\t\t\\textbf{Strategy} & \\textbf{VAL} & \\textbf{ARC} & \\textbf{ASD} & \\textbf{IR*} & \\textbf{MD} & \\textbf{IR**} & \\textbf{N} & \\textbf{LONG} & \\textbf{SHORT} \\\\\n",
|
|
"\t\t\t\\hline\n",
|
|
"\t\t\tBuy and Hold & 1.230 & 52.29\\% & 44.30\\% & 1.180 & 22.35\\% & 2.761 & 2 & 100.00\\% & 0.00\\% \\\\\n",
|
|
"\t\t\tGMADL Informer & 1.086 & 18.12\\% & 40.58\\% & 0.446 & 26.30\\% & 0.308 & 11 & 60.03\\% & 23.82\\% \\\\\n",
|
|
"\t\t\\end{tabular}\n",
|
|
"\t\\end{center}\n",
|
|
"\\end{table}\n",
|
|
"\\begin{table}\n",
|
|
"\t\\begin{center}\n",
|
|
"\t\t\\begin{tabular}{lccccccccc}\n",
|
|
"\t\t\t\\textbf{Strategy} & \\textbf{VAL} & \\textbf{ARC} & \\textbf{ASD} & \\textbf{IR*} & \\textbf{MD} & \\textbf{IR**} & \\textbf{N} & \\textbf{LONG} & \\textbf{SHORT} \\\\\n",
|
|
"\t\t\t\\hline\n",
|
|
"\t\t\tBuy and Hold & 1.439 & 109.31\\% & 43.75\\% & 2.498 & 21.12\\% & 12.930 & 2 & 100.00\\% & 0.00\\% \\\\\n",
|
|
"\t\t\tGMADL Informer & 1.010 & 1.98\\% & 43.47\\% & 0.046 & 31.96\\% & 0.003 & 67 & 80.24\\% & 15.37\\% \\\\\n",
|
|
"\t\t\\end{tabular}\n",
|
|
"\t\\end{center}\n",
|
|
"\\end{table}\n",
|
|
"\\begin{table}\n",
|
|
"\t\\begin{center}\n",
|
|
"\t\t\\begin{tabular}{lccccccccc}\n",
|
|
"\t\t\t\\textbf{Strategy} & \\textbf{VAL} & \\textbf{ARC} & \\textbf{ASD} & \\textbf{IR*} & \\textbf{MD} & \\textbf{IR**} & \\textbf{N} & \\textbf{LONG} & \\textbf{SHORT} \\\\\n",
|
|
"\t\t\t\\hline\n",
|
|
"\t\t\tBuy and Hold & 1.561 & 146.58\\% & 53.72\\% & 2.729 & 27.11\\% & 14.756 & 2 & 100.00\\% & 0.00\\% \\\\\n",
|
|
"\t\t\tGMADL Informer & 1.178 & 39.32\\% & 43.06\\% & 0.913 & 18.63\\% & 1.927 & 92 & 54.86\\% & 0.00\\% \\\\\n",
|
|
"\t\t\\end{tabular}\n",
|
|
"\t\\end{center}\n",
|
|
"\\end{table}\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"for i, (in_sample, out_of_sample) in enumerate(data_windows):\n",
|
|
" padded_window = pd.concat([in_sample.iloc[-PADDING:], out_of_sample])\n",
|
|
" result_buyandhold = evaluate_strategy(padded_window, best_strategies['buy_and_hold'][i], padding=PADDING, interval=INTERVAL)\n",
|
|
" result_gmadl_model = evaluate_strategy(padded_window, [s[0] for s in best_strategies['gmadl_model']][i], padding=PADDING, interval=INTERVAL)\n",
|
|
"\n",
|
|
" results_table(result_buyandhold, result_gmadl_model)\n",
|
|
" # results_plot(i+1, result_buyandhold, result_gmadl_model)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 2,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# test_data = pd.concat([data_windows[0][0][-PADDING:]] + [data_window[1] for data_window in data_windows])\n",
|
|
"# buy_and_hold_concat = evaluate_strategy(test_data, BuyAndHoldStrategy(), padding=PADDING, interval=INTERVAL)\n",
|
|
"# gmadl_model_concat = evaluate_strategy(test_data, ConcatenatedStrategies(len(data_windows[0][1]), [s[0] for s in best_strategies['gmadl_model']], padding=PADDING), padding=PADDING, interval=INTERVAL)\n",
|
|
"\n",
|
|
"# v_lines=[data_window[1]['close_time'].iloc[-1] for data_window in data_windows][:-1]\n",
|
|
"# results_table(buy_and_hold_concat, gmadl_model_concat)\n",
|
|
"# results_plot(0, buy_and_hold_concat, gmadl_model_concat, width=1300, height=500, notitle=True)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 3,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"import plotly.figure_factory as ff\n",
|
|
"\n",
|
|
"def results_for_strats(\n",
|
|
" data_windows, \n",
|
|
" best_strategies,\n",
|
|
" top_n=10):\n",
|
|
" test_data = pd.concat([data_windows[0][0][-PADDING:]] + [data_window[1] for data_window in data_windows])\n",
|
|
"\n",
|
|
" buy_and_hold_concat = evaluate_strategy(test_data, BuyAndHoldStrategy(), padding=PADDING, interval='min')\n",
|
|
" gmadl_1min_model_concat = [evaluate_strategy(test_data, ConcatenatedStrategies(len(data_windows[0][1]), [s[x] for s in best_strategies['gmadl_model']], padding=PADDING), padding=PADDING, interval='min') for x in range(top_n)]\n",
|
|
"\n",
|
|
" z = list(reversed([\n",
|
|
" list(reversed([round(buy_and_hold_concat['mod_ir'], 3)]*top_n)),\n",
|
|
" list(reversed([round(x['mod_ir'], 3) for x in gmadl_1min_model_concat])),\n",
|
|
" ]))\n",
|
|
" x = list(reversed(range(1, top_n+1)))\n",
|
|
" y = list(reversed([\n",
|
|
" \"Buy and Hold\",\n",
|
|
" \"Gmadl Informer (1 min)\"\n",
|
|
" ]))\n",
|
|
" # 'Portland'\n",
|
|
" fig = ff.create_annotated_heatmap(z, x=x, y=y, colorscale='thermal', zmid=buy_and_hold_concat['mod_ir'])\n",
|
|
" fig.update_layout(\n",
|
|
" margin=dict(l=20, r=20, b=20, t=20),\n",
|
|
" width=1100,\n",
|
|
" height=650,\n",
|
|
" font=dict(\n",
|
|
" # family=\"Courier New, monospace\",\n",
|
|
" size=16, # Set the font size here\n",
|
|
" # color=\"RebeccaPurple\"\n",
|
|
" )\n",
|
|
" )\n",
|
|
" fig.show()\n",
|
|
"\n",
|
|
"# results_for_strats(data_windows, best_strategies, top_n=10) "
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "wnemsc",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.9.19"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 2
|
|
}
|