Add notebooks with evaluations, visualizations are commented out due to large size
This commit is contained in:
Filip Stefaniuk
parent
7989813534
commit
aa4bc26c53
817
notebooks/btcusdt_15m_evaluation.ipynb
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817
notebooks/btcusdt_15m_evaluation.ipynb
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{
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"cells": [
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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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"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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"import plotly.graph_objs as go\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='15min'\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": 3,
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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-15m:latest, 248.65MB. 12 files... \n",
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"\u001b[34m\u001b[1mwandb\u001b[0m: 12 of 12 files downloaded. \n",
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"Done. 0:0:0.5\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-15m: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": 4,
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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": 5,
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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": 6,
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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%|██████████| 3840/3840 [00:06<00:00, 614.41it/s]\n",
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"100%|██████████| 3840/3840 [00:08<00:00, 479.05it/s]\n",
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"100%|██████████| 3840/3840 [00:06<00:00, 554.94it/s]\n",
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]
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}
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],
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"source": [
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"MACD_PARAMS = {\n",
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" 'fast_window_size': [2, 3, 5, 8, 13, 21, 34, 55, 89, 144, 233, 377, 610, 987, 1597, 2584],\n",
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" 'slow_window_size': [2, 3, 5, 8, 13, 21, 34, 55, 89, 144, 233, 377, 610, 987, 1597, 2584],\n",
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" 'signal_window_size': [2, 3, 5, 8, 13, 21, 34, 55, 89, 144, 233, 377, 610, 987, 1597, 2584],\n",
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" 'short_sell': [True, False]\n",
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"}\n",
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"MACD_PARAMS_FILTER = lambda p: (\n",
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" p['slow_window_size'] > p['fast_window_size']\n",
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")\n",
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"macd_sweep_results = sweeps_on_all_windows(\n",
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" data_windows,\n",
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" MACDStrategy,\n",
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" MACD_PARAMS,\n",
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" params_filter=MACD_PARAMS_FILTER,\n",
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" sort_by=METRIC)\n",
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"macd_best_strategies = [[strat for _, strat in results[:TOP_N]] for results in macd_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": 41,
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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 macd_sweep_results[0]]), parameters=MACD_PARAMS.keys())"
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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": 7,
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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%|██████████| 11088/11088 [00:19<00:00, 580.54it/s]\n",
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"100%|██████████| 11088/11088 [00:17<00:00, 627.38it/s]\n",
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]
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}
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],
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"source": [
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"RSI_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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"RSI_PARAMS = {\n",
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" 'window_size': [2, 3, 5, 8, 13, 21, 34, 55, 89, 144, 233, 377, 610, 987, 1597, 2584],\n",
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" 'enter_long': [None, 70, 75, 80, 85, 90, 95],\n",
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" 'exit_long': [None, 5, 10, 15, 20, 25, 30],\n",
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" 'enter_short': [None, 5, 10, 15, 20, 25, 30],\n",
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" 'exit_short': [None, 70, 75, 80, 85, 90, 95],\n",
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"}\n",
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"rsi_sweep_results = sweeps_on_all_windows(data_windows, RSIStrategy, RSI_PARAMS, params_filter=RSI_FILTER, sort_by=METRIC)\n",
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"rsi_best_strategies = [[strat for _, strat in results[:TOP_N]] for results in rsi_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": 43,
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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 rsi_sweep_results[0]]), parameters=RSI_PARAMS.keys())"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": []
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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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{
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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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]
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}
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],
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"source": [
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"# Model with quantile loss\n",
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"SWEEP_ID = 'filipstefaniuk/wne-masters-thesis-testing/btu0mf6u'\n",
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"train_pred_windows = get_sweep_window_predictions(SWEEP_ID, 'train')\n",
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"valid_pred_windows = get_sweep_window_predictions(SWEEP_ID, 'valid')\n",
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"test_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": 10,
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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%|██████████| 1125/1125 [00:21<00:00, 51.66it/s]\n",
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]
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}
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],
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"source": [
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"MODEL_QUANTILE_LOSS_FILTER = lambda p: (\n",
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" ((p['quantile_enter_long'] is not None and (p['quantile_enter_short'] is not None or p['quantile_exit_long'] is not None))\n",
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" or (p['quantile_enter_short'] is not None and (p['quantile_exit_short'] is not None or p['quantile_enter_long'] is not None)))\n",
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" and (p['quantile_enter_short'] is None or p['quantile_exit_long'] is None or (p['quantile_exit_long'] < p['quantile_enter_short']))\n",
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" and (p['quantile_enter_long'] is None or p['quantile_exit_short'] is None or (p['quantile_exit_short'] < p['quantile_enter_long'])))\n",
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"\n",
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"quantile_model_sweep_results = []\n",
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"for (in_sample, _), train_preds, valid_preds, test_preds in zip(data_windows, train_pred_windows, valid_pred_windows, test_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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" 'quantiles': [[0.01, 0.02, 0.03, 0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95, 0.97, 0.98, 0.99]],\n",
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" 'quantile_enter_long': [None, 0.9, 0.95, 0.97, 0.98, 0.99],\n",
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" 'quantile_exit_long': [None, 0.9, 0.95, 0.97, 0.98, 0.99],\n",
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" 'quantile_enter_short': [None, 0.9, 0.95, 0.97, 0.98, 0.99],\n",
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" 'quantile_exit_short': [None, 0.9, 0.95, 0.97, 0.98, 0.99],\n",
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" 'exchange_fee': [0.001, 0.002, 0.003],\n",
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" 'future': [1]\n",
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" }\n",
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" \n",
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" quantile_model_sweep_results.append(parameter_sweep(\n",
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" in_sample[data_part-PADDING:],\n",
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" ModelQuantilePredictionsStrategy,\n",
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" params,\n",
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" params_filter=MODEL_QUANTILE_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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"quantile_model_best_strategies = [[strat for _, strat in results[:TOP_N]] for results in quantile_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": 46,
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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 quantile_model_sweep_results[0]]), parameters=['quantile_enter_long', 'quantile_exit_long', 'quantile_enter_short', 'quantile_exit_short', 'future'])"
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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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"\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 quantile loss\n",
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"# SWEEP_ID = 'filipstefaniuk/wne-masters-thesis-testing/a6q8zv10'\n",
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"SWEEP_ID = 'filipstefaniuk/wne-masters-thesis-testing/7n3w718v'\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": 13,
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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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|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"MODEL_GMADL_LOSS_FILTER = lambda p: (\n",
|
||||
" ((p['enter_long'] is not None and (p['enter_short'] is not None or p['exit_long'] is not None))\n",
|
||||
" or (p['enter_short'] is not None and (p['exit_short'] is not None or p['enter_long'] is not None)))\n",
|
||||
" and (p['enter_short'] is None or p['exit_long'] is None or (p['exit_long'] > p['enter_short']))\n",
|
||||
" and (p['enter_long'] is None or p['exit_short'] is None or (p['exit_short'] < p['enter_long'])))\n",
|
||||
"\n",
|
||||
"gmadl_model_sweep_results = []\n",
|
||||
"for (in_sample, _), train_preds, valid_preds, test_preds in zip(data_windows, train_gmadl_pred_windows, valid_gmadl_pred_windows, test_gmadl_pred_windows):\n",
|
||||
" data_part = int((1 - VALID_PART) * len(in_sample))\n",
|
||||
" params={\n",
|
||||
" 'predictions': [get_predictions_dataframe(valid_preds, test_preds)],\n",
|
||||
" 'enter_long': [None, 0.001, 0.002, 0.003, 0.004, 0.005, 0.006, 0.007],\n",
|
||||
" 'exit_long': [None, -0.001, -0.002, -0.003, -0.004, -0.005, -0.006, -0.007],\n",
|
||||
" 'enter_short': [None, -0.001, -0.002, -0.003, -0.004, -0.005, -0.006, -0.007],\n",
|
||||
" 'exit_short': [None, 0.001, 0.002, 0.003, 0.004, 0.005, 0.006, 0.007],\n",
|
||||
" }\n",
|
||||
" \n",
|
||||
" gmadl_model_sweep_results.append(parameter_sweep(\n",
|
||||
" in_sample[data_part-PADDING:],\n",
|
||||
" ModelGmadlPredictionsStrategy,\n",
|
||||
" params,\n",
|
||||
" params_filter=MODEL_GMADL_LOSS_FILTER,\n",
|
||||
" padding=PADDING,\n",
|
||||
" interval=INTERVAL,\n",
|
||||
" sort_by=METRIC))\n",
|
||||
" \n",
|
||||
"\n",
|
||||
"gmadl_model_best_strategies = [[strat for _, strat in results[:TOP_N]] for results in gmadl_model_sweep_results]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 14,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Persist best strategies, so that they don't have to be recomputed every time\n",
|
||||
"best_strategies = {\n",
|
||||
" 'buy_and_hold': buyandhold_best_strategies,\n",
|
||||
" 'macd_strategies': macd_best_strategies,\n",
|
||||
" 'rsi_strategies': rsi_best_strategies,\n",
|
||||
" 'quantile_model': quantile_model_best_strategies,\n",
|
||||
" 'gmadl_model': gmadl_model_best_strategies\n",
|
||||
"}\n",
|
||||
"\n",
|
||||
"with open('cache/15min-best-strategies.pkl', 'wb') as outp:\n",
|
||||
" pickle.dump(best_strategies, outp, pickle.HIGHEST_PROTOCOL)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Visualizations & Tables"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 15,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"with open('cache/15min-best-strategies.pkl', 'rb') as inpt:\n",
|
||||
" best_strategies = pickle.load(inpt)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Tables with parameters"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 16,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"\\begin{table}\n",
|
||||
"\t\\begin{center}\n",
|
||||
"\t\t\\begin{tabular}{lcccc}\n",
|
||||
"\t\t\t\\textbf{Window} & \\textbf{Fast Window Size} & \\textbf{Slow Window Size} & \\textbf{Signal Window Size} & \\textbf{Short sell} \\\\\n",
|
||||
"\t\t\t\\hline\n",
|
||||
"\t\t\tWindow 1 & 8 & 377 & 1597 & True \\\\\n",
|
||||
"\t\t\tWindow 2 & 144 & 987 & 2584 & True \\\\\n",
|
||||
"\t\t\tWindow 3 & 377 & 2584 & 2584 & True \\\\\n",
|
||||
"\t\t\tWindow 4 & 377 & 2584 & 610 & True \\\\\n",
|
||||
"\t\t\tWindow 5 & 233 & 610 & 233 & False \\\\\n",
|
||||
"\t\t\tWindow 6 & 144 & 377 & 2584 & False \\\\\n",
|
||||
"\t\t\\end{tabular}\n",
|
||||
"\t\\end{center}\n",
|
||||
"\\end{table}\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Best hparams for MACD strategy\n",
|
||||
"table_macd_params = Texttable()\n",
|
||||
"table_macd_params.set_deco(Texttable.HEADER)\n",
|
||||
"table_macd_params.set_cols_align([\"l\", \"c\", \"c\", \"c\", \"c\"])\n",
|
||||
"table_macd_params.header([\n",
|
||||
" \"\\\\textbf{Window}\",\n",
|
||||
" \"\\\\textbf{Fast Window Size}\",\n",
|
||||
" \"\\\\textbf{Slow Window Size}\",\n",
|
||||
" \"\\\\textbf{Signal Window Size}\",\n",
|
||||
" \"\\\\textbf{Short sell}\"\n",
|
||||
"])\n",
|
||||
"\n",
|
||||
"for i, macd_strategy in enumerate(best_strategies['macd_strategies']):\n",
|
||||
" macd_strategy_info = macd_strategy[0].info()\n",
|
||||
" table_macd_params.add_row([\n",
|
||||
" f\"Window {i+1}\",\n",
|
||||
" macd_strategy_info['fast_window_size'],\n",
|
||||
" macd_strategy_info['slow_window_size'],\n",
|
||||
" macd_strategy_info['signal_window_size'],\n",
|
||||
" macd_strategy_info['short_sell']\n",
|
||||
" ])\n",
|
||||
"print(latextable.draw_latex(table_macd_params))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 17,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"\\begin{table}\n",
|
||||
"\t\\begin{center}\n",
|
||||
"\t\t\\begin{tabular}{lccccc}\n",
|
||||
"\t\t\t\\textbf{Window} & \\textbf{\\textit{window}} & \\textbf{\\textit{enter long}} & \\textbf{\\textit{exit long}} & \\textbf{\\textit{enter short}} & \\textbf{\\textit{exit short}} \\\\\n",
|
||||
"\t\t\t\\hline\n",
|
||||
"\t\t\tW1-15min & 5 & 80 & - & 15 & - \\\\\n",
|
||||
"\t\t\tW2-15min & 34 & 75 & - & 30 & - \\\\\n",
|
||||
"\t\t\tW3-15min & 5 & 95 & - & 10 & - \\\\\n",
|
||||
"\t\t\tW4-15min & 34 & 75 & - & 30 & - \\\\\n",
|
||||
"\t\t\tW5-15min & 13 & 70 & - & 5 & - \\\\\n",
|
||||
"\t\t\tW6-15min & 21 & 85 & 15 & - & - \\\\\n",
|
||||
"\t\t\\end{tabular}\n",
|
||||
"\t\\end{center}\n",
|
||||
"\\end{table}\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"table_rsi_params = Texttable()\n",
|
||||
"table_rsi_params.set_deco(Texttable.HEADER)\n",
|
||||
"table_rsi_params.set_cols_align([\"l\", \"c\",\"c\", \"c\", \"c\", \"c\"])\n",
|
||||
"table_rsi_params.header([\n",
|
||||
" \"\\\\textbf{Window}\",\n",
|
||||
" \"\\\\textbf{\\\\textit{window}}\",\n",
|
||||
" \"\\\\textbf{\\\\textit{enter long}}\",\n",
|
||||
" \"\\\\textbf{\\\\textit{exit long}}\",\n",
|
||||
" \"\\\\textbf{\\\\textit{enter short}}\",\n",
|
||||
" \"\\\\textbf{\\\\textit{exit short}}\"\n",
|
||||
"])\n",
|
||||
"\n",
|
||||
"for i, rsi_strategy in enumerate(best_strategies['rsi_strategies']):\n",
|
||||
" rsi_strategy_info = rsi_strategy[0].info()\n",
|
||||
" table_rsi_params.add_row([\n",
|
||||
" f\"W{i+1}-15min\",\n",
|
||||
" rsi_strategy_info['window_size'] or '-',\n",
|
||||
" rsi_strategy_info['enter_long'] or '-',\n",
|
||||
" rsi_strategy_info['exit_long'] or '-',\n",
|
||||
" rsi_strategy_info['enter_short'] or '-',\n",
|
||||
" rsi_strategy_info['exit_short'] or '-'\n",
|
||||
" ])\n",
|
||||
"print(latextable.draw_latex(table_rsi_params))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 18,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"\\begin{table}\n",
|
||||
"\t\\begin{center}\n",
|
||||
"\t\t\\begin{tabular}{lccccc}\n",
|
||||
"\t\t\t\\textbf{Window} & \\textbf{\\textit{enter long}} & \\textbf{\\textit{exit Long}} & \\textbf{\\textit{enter Short}} & \\textbf{\\textit{exit Short}} & \\textbf{\\textit{threshold}} \\\\\n",
|
||||
"\t\t\t\\hline\n",
|
||||
"\t\t\tW1-15min & 0.980 & - & 0.990 & - & 0.003 \\\\\n",
|
||||
"\t\t\tW2-15min & 0.990 & 0.950 & - & 0.900 & 0.003 \\\\\n",
|
||||
"\t\t\tW3-15min & - & - & 0.990 & 0.990 & 0.003 \\\\\n",
|
||||
"\t\t\tW4-15min & 0.990 & 0.970 & - & 0.980 & 0.003 \\\\\n",
|
||||
"\t\t\tW5-15min & 0.950 & 0.990 & - & - & 0.001 \\\\\n",
|
||||
"\t\t\tW6-15min & 0.990 & 0.950 & - & 0.950 & 0.002 \\\\\n",
|
||||
"\t\t\\end{tabular}\n",
|
||||
"\t\\end{center}\n",
|
||||
"\\end{table}\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"table_quantile_params = Texttable()\n",
|
||||
"table_quantile_params.set_deco(Texttable.HEADER)\n",
|
||||
"table_quantile_params.set_cols_align([\"l\", \"c\",\"c\", \"c\", \"c\", \"c\"])\n",
|
||||
"table_quantile_params.header([\n",
|
||||
" \"\\\\textbf{Window}\",\n",
|
||||
" \"\\\\textbf{\\\\textit{enter long}}\",\n",
|
||||
" \"\\\\textbf{\\\\textit{exit Long}}\",\n",
|
||||
" \"\\\\textbf{\\\\textit{enter Short}}\",\n",
|
||||
" \"\\\\textbf{\\\\textit{exit Short}}\",\n",
|
||||
" \"\\\\textbf{\\\\textit{threshold}}\"\n",
|
||||
"])\n",
|
||||
"\n",
|
||||
"for i, quantile_strategy in enumerate(best_strategies['quantile_model']):\n",
|
||||
" quantile_strategy_info = quantile_strategy[0].info()\n",
|
||||
" table_quantile_params.add_row([\n",
|
||||
" f\"W{i+1}-{INTERVAL}\",\n",
|
||||
" quantile_strategy_info['quantile_enter_long'] or '-',\n",
|
||||
" quantile_strategy_info['quantile_exit_long'] or '-',\n",
|
||||
" quantile_strategy_info['quantile_enter_short'] or '-',\n",
|
||||
" quantile_strategy_info['quantile_exit_short'] or '-',\n",
|
||||
" quantile_strategy_info['exchange_fee']\n",
|
||||
" ])\n",
|
||||
"print(latextable.draw_latex(table_quantile_params))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 19,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"\\begin{table}\n",
|
||||
"\t\\begin{center}\n",
|
||||
"\t\t\\begin{tabular}{lcccc}\n",
|
||||
"\t\t\t\\textbf{Window} & \\textbf{\\textit{enter long}} & \\textbf{\\textit{exit Long}} & \\textbf{\\textit{enter Short}} & \\textbf{\\textit{exit Short}} \\\\\n",
|
||||
"\t\t\t\\hline\n",
|
||||
"\t\t\tW1-15min & 0.005 & - & -0.002 & - \\\\\n",
|
||||
"\t\t\tW2-15min & 0.006 & - & -0.002 & - \\\\\n",
|
||||
"\t\t\tW3-15min & - & - & -0.001 & 0.005 \\\\\n",
|
||||
"\t\t\tW4-15min & 0.007 & - & -0.005 & - \\\\\n",
|
||||
"\t\t\tW5-15min & 0.001 & - & -0.004 & - \\\\\n",
|
||||
"\t\t\tW6-15min & 0.002 & -0.002 & - & - \\\\\n",
|
||||
"\t\t\\end{tabular}\n",
|
||||
"\t\\end{center}\n",
|
||||
"\\end{table}\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"table_gmadl_params = Texttable()\n",
|
||||
"table_gmadl_params.set_deco(Texttable.HEADER)\n",
|
||||
"table_gmadl_params.set_cols_align([\"l\", \"c\",\"c\", \"c\", \"c\"])\n",
|
||||
"table_gmadl_params.header([\n",
|
||||
" \"\\\\textbf{Window}\",\n",
|
||||
" \"\\\\textbf{\\\\textit{enter long}}\",\n",
|
||||
" \"\\\\textbf{\\\\textit{exit Long}}\",\n",
|
||||
" \"\\\\textbf{\\\\textit{enter Short}}\",\n",
|
||||
" \"\\\\textbf{\\\\textit{exit Short}}\",\n",
|
||||
"])\n",
|
||||
"\n",
|
||||
"for i, gmadl_strategy in enumerate(best_strategies['gmadl_model']):\n",
|
||||
" gmadl_strategy_info = gmadl_strategy[0].info()\n",
|
||||
" table_gmadl_params.add_row([\n",
|
||||
" f\"W{i+1}-{INTERVAL}\",\n",
|
||||
" gmadl_strategy_info['enter_long'] or '-',\n",
|
||||
" gmadl_strategy_info['exit_long'] or '-',\n",
|
||||
" gmadl_strategy_info['enter_short'] or '-',\n",
|
||||
" gmadl_strategy_info['exit_short'] or '-'\n",
|
||||
" ])\n",
|
||||
"print(latextable.draw_latex(table_gmadl_params))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Evaluation on the windows"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 29,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def results_plot(idx, result_buyandhold, result_macd, result_rsi, result_quantile_model, result_gmadl_model, width=850, height=500, notitle=False):\n",
|
||||
"\n",
|
||||
" fig = go.Figure([\n",
|
||||
" go.Scatter(y=result_buyandhold['portfolio_value'], x=result_buyandhold['time'], name=\"Buy and Hold\"),\n",
|
||||
" go.Scatter(y=result_macd['portfolio_value'], x=result_macd['time'], name=\"MACD Strategy\"),\n",
|
||||
" go.Scatter(y=result_rsi['portfolio_value'], x=result_rsi['time'], name=\"RSI Strategy\"),\n",
|
||||
" go.Scatter(y=result_quantile_model['portfolio_value'], x=result_quantile_model['time'], name='Quantile Informer Strategy'),\n",
|
||||
" go.Scatter(y=result_gmadl_model['portfolio_value'], x=result_gmadl_model['time'], name='GMADL Informer Strategy')\n",
|
||||
" ])\n",
|
||||
" fig.update_layout(\n",
|
||||
" title={\n",
|
||||
" 'text': f\"W{idx}-{INTERVAL}\",\n",
|
||||
" 'y':0.97,\n",
|
||||
" 'x':0.5,\n",
|
||||
" 'xanchor': 'center',\n",
|
||||
" 'yanchor': 'top'} if not notitle else None,\n",
|
||||
" yaxis_title=\"Portfolio Value\",\n",
|
||||
" xaxis_title=\"Date\",\n",
|
||||
" font=dict(\n",
|
||||
" # family=\"Courier New, monospace\",\n",
|
||||
" size=14,\n",
|
||||
" ),\n",
|
||||
" autosize=False,\n",
|
||||
" width=width,\n",
|
||||
" height=height,\n",
|
||||
" margin=dict(l=20, r=20, t=20 if notitle else 110, b=20),\n",
|
||||
" plot_bgcolor='white',\n",
|
||||
" legend=dict(\n",
|
||||
" orientation=\"h\",\n",
|
||||
" yanchor=\"bottom\",\n",
|
||||
" y=1.02,\n",
|
||||
" xanchor=\"left\",\n",
|
||||
" x=0.02\n",
|
||||
" )\n",
|
||||
" )\n",
|
||||
" fig.update_xaxes(\n",
|
||||
" mirror=True,\n",
|
||||
" ticks='outside',\n",
|
||||
" showline=True,\n",
|
||||
" linecolor='black',\n",
|
||||
" gridcolor='lightgrey'\n",
|
||||
" )\n",
|
||||
" fig.update_yaxes(\n",
|
||||
" mirror=True,\n",
|
||||
" ticks='outside',\n",
|
||||
" showline=True,\n",
|
||||
" linecolor='black',\n",
|
||||
" gridcolor='lightgrey'\n",
|
||||
" )\n",
|
||||
" fig.write_image(f\"images/eval-w{idx}-{INTERVAL}.png\")\n",
|
||||
" fig.show()\n",
|
||||
" \n",
|
||||
"def results_table(result_buyandhold, result_macd, result_rsi, result_quantile_model, result_gmadl_model):\n",
|
||||
" table_eval_windows = Texttable()\n",
|
||||
" table_eval_windows.set_deco(Texttable.HEADER)\n",
|
||||
" table_eval_windows.set_cols_align([\"l\", \"c\",\"c\", \"c\", \"c\", \"c\", \"c\", \"c\", \"c\", \"c\"])\n",
|
||||
" table_eval_windows.set_precision(3)\n",
|
||||
"\n",
|
||||
" table_eval_windows.header([\n",
|
||||
" \"\\\\textbf{Strategy}\",\n",
|
||||
" \"\\\\textbf{VAL}\",\n",
|
||||
" \"\\\\textbf{ARC}\",\n",
|
||||
" \"\\\\textbf{ASD}\",\n",
|
||||
" \"\\\\textbf{IR*}\",\n",
|
||||
" \"\\\\textbf{MD}\",\n",
|
||||
" \"\\\\textbf{IR**}\",\n",
|
||||
" \"\\\\textbf{N}\",\n",
|
||||
" \"\\\\textbf{LONG}\",\n",
|
||||
" \"\\\\textbf{SHORT}\",\n",
|
||||
" ])\n",
|
||||
"\n",
|
||||
" strategy_name_result = [\n",
|
||||
" ('Buy and Hold', result_buyandhold),\n",
|
||||
" ('MACD Strategy', result_macd),\n",
|
||||
" ('RSI Strategy', result_rsi),\n",
|
||||
" ('Quantile Informer', result_quantile_model),\n",
|
||||
" ('GMADL Informer', result_gmadl_model)\n",
|
||||
" ]\n",
|
||||
" for strategy_name, result in strategy_name_result:\n",
|
||||
" table_eval_windows.add_row([\n",
|
||||
" strategy_name,\n",
|
||||
" result['value'],\n",
|
||||
" result['arc'],\n",
|
||||
" result['asd'],\n",
|
||||
" result['ir'],\n",
|
||||
" result['md'],\n",
|
||||
" result['mod_ir'],\n",
|
||||
" result['n_trades'],\n",
|
||||
" f\"{result['long_pos']*100:.2f}\\%\",\n",
|
||||
" f\"{result['short_pos']*100:.2f}\\%\",\n",
|
||||
" ])\n",
|
||||
" print(latextable.draw_latex(table_eval_windows))\n",
|
||||
"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"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_macd = evaluate_strategy(padded_window, [s[0] for s in best_strategies['macd_strategies']][i], padding=PADDING, interval=INTERVAL)\n",
|
||||
"# result_rsi = evaluate_strategy(padded_window, [s[0] for s in best_strategies['rsi_strategies']][i], padding=PADDING, interval=INTERVAL)\n",
|
||||
"# result_quantile_model = evaluate_strategy(padded_window, [s[0] for s in best_strategies['quantile_model']][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_macd, result_rsi, result_quantile_model, result_gmadl_model)\n",
|
||||
"# results_plot(i+1, result_buyandhold, result_macd, result_rsi, result_quantile_model, result_gmadl_model)\n",
|
||||
" "
|
||||
]
|
||||
},
|
||||
{
|
||||
"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",
|
||||
"# macd_concat = evaluate_strategy(test_data, ConcatenatedStrategies(len(data_windows[0][1]), [s[0] for s in best_strategies['macd_strategies']], padding=PADDING), padding=PADDING, interval=INTERVAL)\n",
|
||||
"# rsi_concat = evaluate_strategy(test_data, ConcatenatedStrategies(len(data_windows[0][1]), [s[0] for s in best_strategies['rsi_strategies']], padding=PADDING), padding=PADDING, interval=INTERVAL)\n",
|
||||
"# quantile_model_concat = evaluate_strategy(test_data, ConcatenatedStrategies(len(data_windows[0][1]), [s[0] for s in best_strategies['quantile_model']], padding=PADDING), 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",
|
||||
"# results_table(buy_and_hold_concat, macd_concat, rsi_concat, quantile_model_concat, gmadl_model_concat)\n",
|
||||
"# results_plot(0, buy_and_hold_concat, macd_concat, rsi_concat, quantile_model_concat, gmadl_model_concat, width=1200, notitle=True)\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 23,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"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, buyandhold_best_strategies[i], padding=PADDING, interval='15min')\n",
|
||||
"# result_macd = evaluate_strategy(padded_window, macd_best_strategies[i], padding=PADDING, interval='15min')\n",
|
||||
"# result_rsi = evaluate_strategy(padded_window, rsi_best_strategies[i], padding=PADDING, interval='15min')\n",
|
||||
"# result_quantile_model = evaluate_strategy(padded_window, quantile_model_best_strategies[i], padding=PADDING, interval='15min')\n",
|
||||
"# result_gmadl_model = evaluate_strategy(padded_window, gmadl_model_best_strategies[i], padding=PADDING, interval='15min')\n",
|
||||
"\n",
|
||||
"# go.Figure([\n",
|
||||
"# go.Scatter(y=result_buyandhold['portfolio_value'], x=result_buyandhold['time'], name=result_buyandhold['strategy_name']),\n",
|
||||
"# go.Scatter(y=result_macd['portfolio_value'], x=result_macd['time'], name=result_macd['strategy_name']),\n",
|
||||
"# go.Scatter(y=result_rsi['portfolio_value'], x=result_rsi['time'], name=result_rsi['strategy_name']),\n",
|
||||
"# go.Scatter(y=result_quantile_model['portfolio_value'], x=result_quantile_model['time'], name='Quantile Model'),\n",
|
||||
"# go.Scatter(y=result_gmadl_model['portfolio_value'], x=result_gmadl_model['time'], name='GMADL model')\n",
|
||||
"# ]).show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"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
|
||||
}
|
||||
826
notebooks/btcusdt_30m_evaluation.ipynb
Normal file
826
notebooks/btcusdt_30m_evaluation.ipynb
Normal file
@ -0,0 +1,826 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 107,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import pandas as pd\n",
|
||||
"import pickle\n",
|
||||
"import plotly.graph_objs as go\n",
|
||||
"import latextable\n",
|
||||
"from texttable import Texttable\n",
|
||||
"from strategy.strategy import (\n",
|
||||
" BuyAndHoldStrategy,\n",
|
||||
" MACDStrategy,\n",
|
||||
" RSIStrategy,\n",
|
||||
" BaselineReturnsStrategy,\n",
|
||||
" ModelQuantilePredictionsStrategy,\n",
|
||||
" ModelGmadlPredictionsStrategy,\n",
|
||||
" ConcatenatedStrategies\n",
|
||||
")\n",
|
||||
"from strategy.util import (\n",
|
||||
" get_data_windows,\n",
|
||||
" get_sweep_window_predictions,\n",
|
||||
" get_predictions_dataframe\n",
|
||||
")\n",
|
||||
"from strategy.evaluation import (\n",
|
||||
" parameter_sweep,\n",
|
||||
" evaluate_strategy\n",
|
||||
")\n",
|
||||
"from strategy.plotting import (\n",
|
||||
" plot_sweep_results\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"PADDING=5000\n",
|
||||
"VALID_PART=0.4\n",
|
||||
"INTERVAL='30min'\n",
|
||||
"METRIC='mod_ir'\n",
|
||||
"TOP_N=10"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 101,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"\u001b[34m\u001b[1mwandb\u001b[0m: Downloading large artifact btc-usdt-30m:latest, 124.19MB. 12 files... \n",
|
||||
"\u001b[34m\u001b[1mwandb\u001b[0m: 12 of 12 files downloaded. \n",
|
||||
"Done. 0:0:0.6\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"data_windows = get_data_windows(\n",
|
||||
" 'wne-masters-thesis-testing',\n",
|
||||
" 'btc-usdt-30m:latest',\n",
|
||||
" min_window=0, \n",
|
||||
" max_window=5\n",
|
||||
")\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 40,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def sweeps_on_all_windows(data_windows, strategy_class, params, **kwargs):\n",
|
||||
" result = []\n",
|
||||
" for in_sample, _ in data_windows:\n",
|
||||
" data_part = int((1 - VALID_PART) * len(in_sample))\n",
|
||||
" result.append(parameter_sweep(in_sample[data_part-PADDING:], strategy_class, params, padding=PADDING, interval=INTERVAL, **kwargs))\n",
|
||||
" return result"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 41,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"buyandhold_best_strategies = [BuyAndHoldStrategy() for _ in data_windows] "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 44,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"100%|██████████| 3840/3840 [00:04<00:00, 769.13it/s]\n",
|
||||
"100%|██████████| 3840/3840 [00:04<00:00, 779.61it/s]\n",
|
||||
"100%|██████████| 3840/3840 [00:05<00:00, 754.69it/s]\n",
|
||||
"100%|██████████| 3840/3840 [00:06<00:00, 579.24it/s]\n",
|
||||
"100%|██████████| 3840/3840 [00:04<00:00, 777.65it/s]\n",
|
||||
"100%|██████████| 3840/3840 [00:04<00:00, 782.54it/s]\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"MACD_PARAMS = {\n",
|
||||
" 'fast_window_size': [2, 3, 5, 8, 13, 21, 34, 55, 89, 144, 233, 377, 610, 987, 1597, 2584],\n",
|
||||
" 'slow_window_size': [2, 3, 5, 8, 13, 21, 34, 55, 89, 144, 233, 377, 610, 987, 1597, 2584],\n",
|
||||
" 'signal_window_size': [2, 3, 5, 8, 13, 21, 34, 55, 89, 144, 233, 377, 610, 987, 1597, 2584],\n",
|
||||
" 'short_sell': [True, False]\n",
|
||||
" # 'short_sell': [False]\n",
|
||||
"}\n",
|
||||
"MACD_PARAMS_FILTER = lambda p: (\n",
|
||||
" p['slow_window_size'] > p['fast_window_size']\n",
|
||||
")\n",
|
||||
"macd_sweep_results = sweeps_on_all_windows(\n",
|
||||
" data_windows,\n",
|
||||
" MACDStrategy,\n",
|
||||
" MACD_PARAMS,\n",
|
||||
" params_filter=MACD_PARAMS_FILTER)\n",
|
||||
"macd_best_strategies = [[strat for _, strat in results[:TOP_N]] for results in macd_sweep_results]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 14,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# plot_sweep_results(pd.DataFrame([result for result, _ in macd_sweep_results[0]]), parameters=MACD_PARAMS.keys())"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 108,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"100%|██████████| 11088/11088 [00:18<00:00, 611.54it/s]\n",
|
||||
"100%|██████████| 11088/11088 [00:17<00:00, 644.30it/s]\n",
|
||||
"100%|██████████| 11088/11088 [00:16<00:00, 653.15it/s]\n",
|
||||
"100%|██████████| 11088/11088 [00:17<00:00, 640.80it/s]\n",
|
||||
"100%|██████████| 11088/11088 [00:14<00:00, 762.97it/s]\n",
|
||||
"100%|██████████| 11088/11088 [00:15<00:00, 737.06it/s]\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"RSI_FILTER = lambda p: (\n",
|
||||
" ((p['enter_long'] is not None and (p['enter_short'] is not None or p['exit_long'] is not None))\n",
|
||||
" or (p['enter_short'] is not None and (p['exit_short'] is not None or p['enter_long'] is not None)))\n",
|
||||
" and (p['enter_short'] is None or p['exit_long'] is None or (p['exit_long'] > p['enter_short']))\n",
|
||||
" and (p['enter_long'] is None or p['exit_short'] is None or (p['exit_short'] < p['enter_long'])))\n",
|
||||
"\n",
|
||||
"RSI_PARAMS = {\n",
|
||||
" 'window_size': [2, 3, 5, 8, 13, 21, 34, 55, 89, 144, 233, 377, 610, 987, 1597, 2584],\n",
|
||||
" 'enter_long': [None, 70, 75, 80, 85, 90, 95],\n",
|
||||
" 'exit_long': [None, 5, 10, 15, 20, 25, 30],\n",
|
||||
" 'enter_short': [None, 5, 10, 15, 20, 25, 30],\n",
|
||||
" 'exit_short': [None, 70, 75, 80, 85, 90, 95],\n",
|
||||
" # 'enter_short': [None],\n",
|
||||
" # 'exit_short': [None],\n",
|
||||
"}\n",
|
||||
"rsi_sweep_results = sweeps_on_all_windows(data_windows, RSIStrategy, RSI_PARAMS, params_filter=RSI_FILTER)\n",
|
||||
"rsi_best_strategies = [[strat for _, strat in results[:TOP_N]] for results in rsi_sweep_results]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 89,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# BASELINE_FILTER = lambda p: (\n",
|
||||
"# ((p['enter_long'] is not None and (p['enter_short'] is not None or p['exit_long'] is not None))\n",
|
||||
"# or (p['enter_short'] is not None and (p['exit_short'] is not None or p['enter_long'] is not None)))\n",
|
||||
"# and (p['enter_short'] is None or p['exit_long'] is None or (p['exit_long'] > p['enter_short']))\n",
|
||||
"# and (p['enter_long'] is None or p['exit_short'] is None or (p['exit_short'] < p['enter_long'])))\n",
|
||||
"\n",
|
||||
"# BASELINE_PARAMS = {\n",
|
||||
"# 'enter_long': [None, 0.001, 0.002, 0.003, 0.005, 0.006, 0.007],\n",
|
||||
"# 'exit_long': [None, -0.001, -0.002, -0.003, -0.005, -0.006, -0.007],\n",
|
||||
"# 'enter_short': [None, -0.001, -0.002, -0.003, -0.005, -0.006, -0.007],\n",
|
||||
"# 'exit_short': [None, 0.001, 0.002, 0.003, 0.005, 0.006, 0.007],\n",
|
||||
"# }\n",
|
||||
"# baseline_sweep_results = sweeps_on_all_windows(data_windows, BaselineReturnsStrategy, BASELINE_PARAMS, params_filter=BASELINE_FILTER)\n",
|
||||
"# baseline_best_strategies = [results[0][1] for results in baseline_sweep_results]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 90,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# plot_sweep_results(pd.DataFrame([result for result, _ in baseline_sweep_results[0]]), parameters=BASELINE_PARAMS.keys())"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 46,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"\u001b[34m\u001b[1mwandb\u001b[0m: 2 of 2 files downloaded. \n",
|
||||
"\u001b[34m\u001b[1mwandb\u001b[0m: 2 of 2 files downloaded. \n",
|
||||
"\u001b[34m\u001b[1mwandb\u001b[0m: 2 of 2 files downloaded. \n",
|
||||
"\u001b[34m\u001b[1mwandb\u001b[0m: 2 of 2 files downloaded. \n",
|
||||
"\u001b[34m\u001b[1mwandb\u001b[0m: 2 of 2 files downloaded. \n",
|
||||
"\u001b[34m\u001b[1mwandb\u001b[0m: 2 of 2 files downloaded. \n",
|
||||
"\u001b[34m\u001b[1mwandb\u001b[0m: 2 of 2 files downloaded. \n",
|
||||
"\u001b[34m\u001b[1mwandb\u001b[0m: 2 of 2 files downloaded. \n",
|
||||
"\u001b[34m\u001b[1mwandb\u001b[0m: 2 of 2 files downloaded. \n",
|
||||
"\u001b[34m\u001b[1mwandb\u001b[0m: 2 of 2 files downloaded. \n",
|
||||
"\u001b[34m\u001b[1mwandb\u001b[0m: 2 of 2 files downloaded. \n",
|
||||
"\u001b[34m\u001b[1mwandb\u001b[0m: 2 of 2 files downloaded. \n",
|
||||
"\u001b[34m\u001b[1mwandb\u001b[0m: 2 of 2 files downloaded. \n",
|
||||
"\u001b[34m\u001b[1mwandb\u001b[0m: 2 of 2 files downloaded. \n",
|
||||
"\u001b[34m\u001b[1mwandb\u001b[0m: 2 of 2 files downloaded. \n",
|
||||
"\u001b[34m\u001b[1mwandb\u001b[0m: 2 of 2 files downloaded. \n",
|
||||
"\u001b[34m\u001b[1mwandb\u001b[0m: 2 of 2 files downloaded. \n",
|
||||
"\u001b[34m\u001b[1mwandb\u001b[0m: 2 of 2 files downloaded. \n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Model with quantile loss\n",
|
||||
"SWEEP_ID = 'filipstefaniuk/wne-masters-thesis-testing/vimckxe2'\n",
|
||||
"train_pred_windows = get_sweep_window_predictions(SWEEP_ID, 'train')\n",
|
||||
"valid_pred_windows = get_sweep_window_predictions(SWEEP_ID, 'valid')\n",
|
||||
"test_pred_windows = get_sweep_window_predictions(SWEEP_ID, 'test')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 48,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"100%|██████████| 1125/1125 [00:28<00:00, 40.16it/s]\n",
|
||||
"100%|██████████| 1125/1125 [00:27<00:00, 41.37it/s]\n",
|
||||
"100%|██████████| 1125/1125 [00:27<00:00, 41.47it/s]\n",
|
||||
"100%|██████████| 1125/1125 [00:27<00:00, 40.47it/s]\n",
|
||||
"100%|██████████| 1125/1125 [00:25<00:00, 44.84it/s]\n",
|
||||
"100%|██████████| 1125/1125 [00:25<00:00, 44.11it/s]\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"MODEL_QUANTILE_LOSS_FILTER = lambda p: (\n",
|
||||
" ((p['quantile_enter_long'] is not None and (p['quantile_enter_short'] is not None or p['quantile_exit_long'] is not None))\n",
|
||||
" or (p['quantile_enter_short'] is not None and (p['quantile_exit_short'] is not None or p['quantile_enter_long'] is not None)))\n",
|
||||
" and (p['quantile_enter_short'] is None or p['quantile_exit_long'] is None or (p['quantile_exit_long'] < p['quantile_enter_short']))\n",
|
||||
" and (p['quantile_enter_long'] is None or p['quantile_exit_short'] is None or (p['quantile_exit_short'] < p['quantile_enter_long'])))\n",
|
||||
"\n",
|
||||
"quantile_model_sweep_results = []\n",
|
||||
"for (in_sample, _), train_preds, valid_preds, test_preds in zip(data_windows, train_pred_windows, valid_pred_windows, test_pred_windows):\n",
|
||||
" data_part = int((1 - VALID_PART) * len(in_sample))\n",
|
||||
" params={\n",
|
||||
" 'predictions': [get_predictions_dataframe(train_preds, valid_preds, test_preds)],\n",
|
||||
" 'quantiles': [[0.01, 0.02, 0.03, 0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95, 0.97, 0.98, 0.99]],\n",
|
||||
" 'quantile_enter_long': [None, 0.9, 0.95, 0.97, 0.98, 0.99],\n",
|
||||
" 'quantile_exit_long': [None, 0.9, 0.95, 0.97, 0.98, 0.99],\n",
|
||||
" 'quantile_enter_short': [None, 0.9, 0.95, 0.97, 0.98, 0.99],\n",
|
||||
" 'quantile_exit_short': [None, 0.9, 0.95, 0.97, 0.98, 0.99],\n",
|
||||
" 'exchange_fee': [0.001, 0.002, 0.003],\n",
|
||||
" # 'exchange_fee': [0.001, 0.0015, 0.002, 0.0025, 0.003, 0.005],\n",
|
||||
" # 'quantile_enter_short': [None],\n",
|
||||
" # 'quantile_exit_short': [None],\n",
|
||||
" 'future': [1]\n",
|
||||
" }\n",
|
||||
" \n",
|
||||
" quantile_model_sweep_results.append(parameter_sweep(\n",
|
||||
" in_sample[data_part-PADDING:],\n",
|
||||
" ModelQuantilePredictionsStrategy,\n",
|
||||
" params,\n",
|
||||
" params_filter=MODEL_QUANTILE_LOSS_FILTER,\n",
|
||||
" padding=PADDING,\n",
|
||||
" interval=INTERVAL,\n",
|
||||
" sort_by=METRIC))\n",
|
||||
"\n",
|
||||
"quantile_model_best_strategies = [[strat for _, strat in results[:TOP_N]] for results in quantile_model_sweep_results]\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 12,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# plot_sweep_results(pd.DataFrame([result for result, _ in quantile_model_sweep_results[0]]), parameters=['exchange_fee', 'quantile_enter_long', 'quantile_exit_long', 'quantile_enter_short', 'quantile_exit_short', 'future'], round=5)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 50,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"\u001b[34m\u001b[1mwandb\u001b[0m: 2 of 2 files downloaded. \n",
|
||||
"\u001b[34m\u001b[1mwandb\u001b[0m: 2 of 2 files downloaded. \n",
|
||||
"\u001b[34m\u001b[1mwandb\u001b[0m: 2 of 2 files downloaded. \n",
|
||||
"\u001b[34m\u001b[1mwandb\u001b[0m: 2 of 2 files downloaded. \n",
|
||||
"\u001b[34m\u001b[1mwandb\u001b[0m: 2 of 2 files downloaded. \n",
|
||||
"\u001b[34m\u001b[1mwandb\u001b[0m: 2 of 2 files downloaded. \n",
|
||||
"\u001b[34m\u001b[1mwandb\u001b[0m: 2 of 2 files downloaded. \n",
|
||||
"\u001b[34m\u001b[1mwandb\u001b[0m: 2 of 2 files downloaded. \n",
|
||||
"\u001b[34m\u001b[1mwandb\u001b[0m: 2 of 2 files downloaded. \n",
|
||||
"\u001b[34m\u001b[1mwandb\u001b[0m: 2 of 2 files downloaded. \n",
|
||||
"\u001b[34m\u001b[1mwandb\u001b[0m: 2 of 2 files downloaded. \n",
|
||||
"\u001b[34m\u001b[1mwandb\u001b[0m: 2 of 2 files downloaded. \n",
|
||||
"\u001b[34m\u001b[1mwandb\u001b[0m: 2 of 2 files downloaded. \n",
|
||||
"\u001b[34m\u001b[1mwandb\u001b[0m: 2 of 2 files downloaded. \n",
|
||||
"\u001b[34m\u001b[1mwandb\u001b[0m: 2 of 2 files downloaded. \n",
|
||||
"\u001b[34m\u001b[1mwandb\u001b[0m: 2 of 2 files downloaded. \n",
|
||||
"\u001b[34m\u001b[1mwandb\u001b[0m: 2 of 2 files downloaded. \n",
|
||||
"\u001b[34m\u001b[1mwandb\u001b[0m: 2 of 2 files downloaded. \n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Model with gmadl loss\n",
|
||||
"# SWEEP_ID = 'filipstefaniuk/wne-masters-thesis-testing/tmqx4epx'\n",
|
||||
"SWEEP_ID = 'filipstefaniuk/wne-masters-thesis-testing/7p7tdxbn'\n",
|
||||
"train_gmadl_pred_windows = get_sweep_window_predictions(SWEEP_ID, 'train')\n",
|
||||
"valid_gmadl_pred_windows = get_sweep_window_predictions(SWEEP_ID, 'valid')\n",
|
||||
"test_gmadl_pred_windows = get_sweep_window_predictions(SWEEP_ID, 'test')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 51,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"100%|██████████| 1176/1176 [00:22<00:00, 51.45it/s]\n",
|
||||
"100%|██████████| 1176/1176 [00:24<00:00, 48.45it/s]\n",
|
||||
"100%|██████████| 1176/1176 [00:21<00:00, 53.53it/s]\n",
|
||||
"100%|██████████| 1176/1176 [00:24<00:00, 48.36it/s]\n",
|
||||
"100%|██████████| 1176/1176 [00:22<00:00, 51.90it/s]\n",
|
||||
"100%|██████████| 1176/1176 [00:21<00:00, 54.04it/s]\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"MODEL_GMADL_LOSS_FILTER = lambda p: (\n",
|
||||
" ((p['enter_long'] is not None and (p['enter_short'] is not None or p['exit_long'] is not None))\n",
|
||||
" or (p['enter_short'] is not None and (p['exit_short'] is not None or p['enter_long'] is not None)))\n",
|
||||
" and (p['enter_short'] is None or p['exit_long'] is None or (p['exit_long'] > p['enter_short']))\n",
|
||||
" and (p['enter_long'] is None or p['exit_short'] is None or (p['exit_short'] < p['enter_long'])))\n",
|
||||
"\n",
|
||||
"gmadl_model_sweep_results = []\n",
|
||||
"for (in_sample, _), train_preds, valid_preds, test_preds in zip(data_windows, train_gmadl_pred_windows, valid_gmadl_pred_windows, test_gmadl_pred_windows):\n",
|
||||
" data_part = int((1 - VALID_PART) * len(in_sample))\n",
|
||||
" params={\n",
|
||||
" 'predictions': [get_predictions_dataframe(train_preds, valid_preds, test_preds)],\n",
|
||||
" 'enter_long': [None, 0.001, 0.002, 0.003, 0.004, 0.005, 0.006, 0.007],\n",
|
||||
" 'exit_long': [None, -0.001, -0.002, -0.003, -0.004, -0.005, -0.006, -0.007],\n",
|
||||
" 'enter_short': [None, -0.001, -0.002, -0.003, -0.004, -0.005, -0.006, -0.007],\n",
|
||||
" 'exit_short': [None, 0.001, 0.002, 0.003, 0.004, 0.005, 0.006, 0.007],\n",
|
||||
" # 'enter_short': [None],\n",
|
||||
" # 'exit_short': [None],\n",
|
||||
" }\n",
|
||||
" \n",
|
||||
" gmadl_model_sweep_results.append(parameter_sweep(\n",
|
||||
" in_sample[data_part-PADDING:],\n",
|
||||
" ModelGmadlPredictionsStrategy,\n",
|
||||
" params,\n",
|
||||
" params_filter=MODEL_GMADL_LOSS_FILTER,\n",
|
||||
" padding=PADDING,\n",
|
||||
" interval=INTERVAL,\n",
|
||||
" sort_by=METRIC))\n",
|
||||
" \n",
|
||||
"\n",
|
||||
"gmadl_model_best_strategies = [[strat for _, strat in results[:TOP_N]] for results in gmadl_model_sweep_results]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 96,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# 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)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 109,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Persist best strategies, so that they don't have to be recomputed every time\n",
|
||||
"best_strategies = {\n",
|
||||
" # 'buy_and_hold': buyandhold_best_strategies,\n",
|
||||
" # 'macd_strategies': macd_best_strategies,\n",
|
||||
" 'rsi_strategies': rsi_best_strategies,\n",
|
||||
" # 'quantile_model': quantile_model_best_strategies,\n",
|
||||
" # 'gmadl_model': gmadl_model_best_strategies\n",
|
||||
"}\n",
|
||||
"\n",
|
||||
"with open('cache/30min-best-strategies-04.pkl', 'wb') as outp:\n",
|
||||
" pickle.dump(best_strategies, outp, pickle.HIGHEST_PROTOCOL)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Visualizations & Tables"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 53,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"with open('cache/30min-best-strategies.pkl', 'rb') as inpt:\n",
|
||||
" best_strategies = pickle.load(inpt)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Tables with parameters"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 54,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"\\begin{table}\n",
|
||||
"\t\\begin{center}\n",
|
||||
"\t\t\\begin{tabular}{lcccc}\n",
|
||||
"\t\t\t\\textbf{Window} & \\textbf{Fast Window Size} & \\textbf{Slow Window Size} & \\textbf{Signal Window Size} & \\textbf{Short sell} \\\\\n",
|
||||
"\t\t\t\\hline\n",
|
||||
"\t\t\tWindow 1 & 1597 & 2584 & 2584 & True \\\\\n",
|
||||
"\t\t\tWindow 2 & 233 & 2584 & 2584 & True \\\\\n",
|
||||
"\t\t\tWindow 3 & 13 & 2584 & 2584 & True \\\\\n",
|
||||
"\t\t\tWindow 4 & 233 & 987 & 233 & True \\\\\n",
|
||||
"\t\t\tWindow 5 & 144 & 233 & 233 & False \\\\\n",
|
||||
"\t\t\tWindow 6 & 1597 & 2584 & 1597 & False \\\\\n",
|
||||
"\t\t\\end{tabular}\n",
|
||||
"\t\\end{center}\n",
|
||||
"\\end{table}\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Best hparams for MACD strategy\n",
|
||||
"table_macd_params = Texttable()\n",
|
||||
"table_macd_params.set_deco(Texttable.HEADER)\n",
|
||||
"table_macd_params.set_cols_align([\"l\", \"c\", \"c\", \"c\", \"c\"])\n",
|
||||
"table_macd_params.header([\n",
|
||||
" \"\\\\textbf{Window}\",\n",
|
||||
" \"\\\\textbf{Fast Window Size}\",\n",
|
||||
" \"\\\\textbf{Slow Window Size}\",\n",
|
||||
" \"\\\\textbf{Signal Window Size}\",\n",
|
||||
" \"\\\\textbf{Short sell}\"\n",
|
||||
"])\n",
|
||||
"\n",
|
||||
"for i, macd_strategy in enumerate(best_strategies['macd_strategies']):\n",
|
||||
" macd_strategy_info = macd_strategy[0].info()\n",
|
||||
" table_macd_params.add_row([\n",
|
||||
" f\"Window {i+1}\",\n",
|
||||
" macd_strategy_info['fast_window_size'],\n",
|
||||
" macd_strategy_info['slow_window_size'],\n",
|
||||
" macd_strategy_info['signal_window_size'],\n",
|
||||
" macd_strategy_info['short_sell']\n",
|
||||
" ])\n",
|
||||
"print(latextable.draw_latex(table_macd_params))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 56,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"\\begin{table}\n",
|
||||
"\t\\begin{center}\n",
|
||||
"\t\t\\begin{tabular}{lccccc}\n",
|
||||
"\t\t\t\\textbf{Window} & \\textbf{\\textit{window}} & \\textbf{\\textit{enter long}} & \\textbf{\\textit{exit long}} & \\textbf{\\textit{enter short}} & \\textbf{\\textit{exit short}} \\\\\n",
|
||||
"\t\t\t\\hline\n",
|
||||
"\t\t\tW1-30min & 5 & 95 & - & 5 & - \\\\\n",
|
||||
"\t\t\tW2-30min & 21 & 70 & - & 30 & - \\\\\n",
|
||||
"\t\t\tW3-30min & 5 & 95 & - & 15 & 85 \\\\\n",
|
||||
"\t\t\tW4-30min & 8 & 95 & 5 & - & 90 \\\\\n",
|
||||
"\t\t\tW5-30min & 13 & 70 & - & 5 & - \\\\\n",
|
||||
"\t\t\tW6-30min & 34 & 80 & 25 & - & - \\\\\n",
|
||||
"\t\t\\end{tabular}\n",
|
||||
"\t\\end{center}\n",
|
||||
"\\end{table}\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"table_rsi_params = Texttable()\n",
|
||||
"table_rsi_params.set_deco(Texttable.HEADER)\n",
|
||||
"table_rsi_params.set_cols_align([\"l\", \"c\",\"c\", \"c\", \"c\", \"c\"])\n",
|
||||
"table_rsi_params.header([\n",
|
||||
" \"\\\\textbf{Window}\",\n",
|
||||
" \"\\\\textbf{\\\\textit{window}}\",\n",
|
||||
" \"\\\\textbf{\\\\textit{enter long}}\",\n",
|
||||
" \"\\\\textbf{\\\\textit{exit long}}\",\n",
|
||||
" \"\\\\textbf{\\\\textit{enter short}}\",\n",
|
||||
" \"\\\\textbf{\\\\textit{exit short}}\"\n",
|
||||
"])\n",
|
||||
"\n",
|
||||
"for i, rsi_strategy in enumerate(best_strategies['rsi_strategies']):\n",
|
||||
" rsi_strategy_info = rsi_strategy[0].info()\n",
|
||||
" table_rsi_params.add_row([\n",
|
||||
" f\"W{i+1}-30min\",\n",
|
||||
" rsi_strategy_info['window_size'] or '-',\n",
|
||||
" rsi_strategy_info['enter_long'] or '-',\n",
|
||||
" rsi_strategy_info['exit_long'] or '-',\n",
|
||||
" rsi_strategy_info['enter_short'] or '-',\n",
|
||||
" rsi_strategy_info['exit_short'] or '-'\n",
|
||||
" ])\n",
|
||||
"print(latextable.draw_latex(table_rsi_params))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 59,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"\\begin{table}\n",
|
||||
"\t\\begin{center}\n",
|
||||
"\t\t\\begin{tabular}{lccccc}\n",
|
||||
"\t\t\t\\textbf{Window} & \\textbf{\\textit{enter long}} & \\textbf{\\textit{exit Long}} & \\textbf{\\textit{enter Short}} & \\textbf{\\textit{exit Short}} & \\textbf{\\textit{threshold}} \\\\\n",
|
||||
"\t\t\t\\hline\n",
|
||||
"\t\t\tW1-30min & 0.980 & - & 0.950 & 0.970 & 0.003 \\\\\n",
|
||||
"\t\t\tW2-30min & - & 0.900 & 0.980 & 0.990 & 0.001 \\\\\n",
|
||||
"\t\t\tW3-30min & - & 0.980 & 0.990 & 0.900 & 0.003 \\\\\n",
|
||||
"\t\t\tW4-30min & 0.950 & 0.990 & - & 0.900 & 0.001 \\\\\n",
|
||||
"\t\t\tW5-30min & 0.900 & 0.990 & - & - & 0.001 \\\\\n",
|
||||
"\t\t\tW6-30min & 0.970 & 0.980 & - & 0.950 & 0.003 \\\\\n",
|
||||
"\t\t\\end{tabular}\n",
|
||||
"\t\\end{center}\n",
|
||||
"\\end{table}\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"table_quantile_params = Texttable()\n",
|
||||
"table_quantile_params.set_deco(Texttable.HEADER)\n",
|
||||
"table_quantile_params.set_cols_align([\"l\", \"c\",\"c\", \"c\", \"c\", \"c\"])\n",
|
||||
"table_quantile_params.header([\n",
|
||||
" \"\\\\textbf{Window}\",\n",
|
||||
" \"\\\\textbf{\\\\textit{enter long}}\",\n",
|
||||
" \"\\\\textbf{\\\\textit{exit Long}}\",\n",
|
||||
" \"\\\\textbf{\\\\textit{enter Short}}\",\n",
|
||||
" \"\\\\textbf{\\\\textit{exit Short}}\",\n",
|
||||
" \"\\\\textbf{\\\\textit{threshold}}\"\n",
|
||||
"])\n",
|
||||
"\n",
|
||||
"for i, quantile_strategy in enumerate(best_strategies['quantile_model']):\n",
|
||||
" quantile_strategy_info = quantile_strategy[0].info()\n",
|
||||
" table_quantile_params.add_row([\n",
|
||||
" f\"W{i+1}-{INTERVAL}\",\n",
|
||||
" quantile_strategy_info['quantile_enter_long'] or '-',\n",
|
||||
" quantile_strategy_info['quantile_exit_long'] or '-',\n",
|
||||
" quantile_strategy_info['quantile_enter_short'] or '-',\n",
|
||||
" quantile_strategy_info['quantile_exit_short'] or '-',\n",
|
||||
" quantile_strategy_info['exchange_fee']\n",
|
||||
" ])\n",
|
||||
"print(latextable.draw_latex(table_quantile_params))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 60,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"\\begin{table}\n",
|
||||
"\t\\begin{center}\n",
|
||||
"\t\t\\begin{tabular}{lcccc}\n",
|
||||
"\t\t\t\\textbf{Window} & \\textbf{\\textit{enter long}} & \\textbf{\\textit{exit Long}} & \\textbf{\\textit{enter Short}} & \\textbf{\\textit{exit Short}} \\\\\n",
|
||||
"\t\t\t\\hline\n",
|
||||
"\t\t\tW1-30min & 0.007 & - & -0.001 & - \\\\\n",
|
||||
"\t\t\tW2-30min & 0.003 & - & -0.007 & - \\\\\n",
|
||||
"\t\t\tW3-30min & - & - & -0.001 & 0.005 \\\\\n",
|
||||
"\t\t\tW4-30min & 0.002 & - & -0.006 & - \\\\\n",
|
||||
"\t\t\tW5-30min & 0.005 & -0.001 & - & - \\\\\n",
|
||||
"\t\t\tW6-30min & 0.003 & - & -0.004 & - \\\\\n",
|
||||
"\t\t\\end{tabular}\n",
|
||||
"\t\\end{center}\n",
|
||||
"\\end{table}\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"table_gmadl_params = Texttable()\n",
|
||||
"table_gmadl_params.set_deco(Texttable.HEADER)\n",
|
||||
"table_gmadl_params.set_cols_align([\"l\", \"c\",\"c\", \"c\", \"c\"])\n",
|
||||
"table_gmadl_params.header([\n",
|
||||
" \"\\\\textbf{Window}\",\n",
|
||||
" \"\\\\textbf{\\\\textit{enter long}}\",\n",
|
||||
" \"\\\\textbf{\\\\textit{exit Long}}\",\n",
|
||||
" \"\\\\textbf{\\\\textit{enter Short}}\",\n",
|
||||
" \"\\\\textbf{\\\\textit{exit Short}}\",\n",
|
||||
"])\n",
|
||||
"\n",
|
||||
"for i, gmadl_strategy in enumerate(best_strategies['gmadl_model']):\n",
|
||||
" gmadl_strategy_info = gmadl_strategy[0].info()\n",
|
||||
" table_gmadl_params.add_row([\n",
|
||||
" f\"W{i+1}-{INTERVAL}\",\n",
|
||||
" gmadl_strategy_info['enter_long'] or '-',\n",
|
||||
" gmadl_strategy_info['exit_long'] or '-',\n",
|
||||
" gmadl_strategy_info['enter_short'] or '-',\n",
|
||||
" gmadl_strategy_info['exit_short'] or '-'\n",
|
||||
" ])\n",
|
||||
"print(latextable.draw_latex(table_gmadl_params))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Evaluation on the windows"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 98,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def results_plot(idx, result_buyandhold, result_macd, result_rsi, result_quantile_model, result_gmadl_model, width=850, height=500, notitle=False):\n",
|
||||
"\n",
|
||||
" fig = go.Figure([\n",
|
||||
" go.Scatter(y=result_buyandhold['portfolio_value'], x=result_buyandhold['time'], name=\"Buy and Hold\"),\n",
|
||||
" go.Scatter(y=result_macd['portfolio_value'], x=result_macd['time'], name=\"MACD Strategy\"),\n",
|
||||
" go.Scatter(y=result_rsi['portfolio_value'], x=result_rsi['time'], name=\"RSI Strategy\"),\n",
|
||||
" go.Scatter(y=result_quantile_model['portfolio_value'], x=result_quantile_model['time'], name='Quantile Informer Strategy'),\n",
|
||||
" go.Scatter(y=result_gmadl_model['portfolio_value'], x=result_gmadl_model['time'], name='GMADL Informer Strategy')\n",
|
||||
" ])\n",
|
||||
" fig.update_layout(\n",
|
||||
" title={\n",
|
||||
" 'text': f\"W{idx}-{INTERVAL}\",\n",
|
||||
" 'y':0.97,\n",
|
||||
" 'x':0.5,\n",
|
||||
" 'xanchor': 'center',\n",
|
||||
" 'yanchor': 'top'} if not notitle else None,\n",
|
||||
" yaxis_title=\"Portfolio Value\",\n",
|
||||
" xaxis_title=\"Date\",\n",
|
||||
" font=dict(\n",
|
||||
" # family=\"Courier New, monospace\",\n",
|
||||
" size=14,\n",
|
||||
" ),\n",
|
||||
" autosize=False,\n",
|
||||
" width=width,\n",
|
||||
" height=height,\n",
|
||||
" margin=dict(l=20, r=20, t=20 if notitle else 110, b=20),\n",
|
||||
" plot_bgcolor='white',\n",
|
||||
" legend=dict(\n",
|
||||
" orientation=\"h\",\n",
|
||||
" yanchor=\"bottom\",\n",
|
||||
" y=1.02,\n",
|
||||
" xanchor=\"left\",\n",
|
||||
" x=0.02\n",
|
||||
" )\n",
|
||||
" )\n",
|
||||
" fig.update_xaxes(\n",
|
||||
" mirror=True,\n",
|
||||
" ticks='outside',\n",
|
||||
" showline=True,\n",
|
||||
" linecolor='black',\n",
|
||||
" gridcolor='lightgrey'\n",
|
||||
" )\n",
|
||||
" fig.update_yaxes(\n",
|
||||
" mirror=True,\n",
|
||||
" ticks='outside',\n",
|
||||
" showline=True,\n",
|
||||
" linecolor='black',\n",
|
||||
" gridcolor='lightgrey'\n",
|
||||
" )\n",
|
||||
" fig.write_image(f\"images/eval-w{idx}-{INTERVAL}.png\")\n",
|
||||
" fig.show()\n",
|
||||
" \n",
|
||||
"def results_table(result_buyandhold, result_macd, result_rsi, result_quantile_model, result_gmadl_model):\n",
|
||||
" table_eval_windows = Texttable()\n",
|
||||
" table_eval_windows.set_deco(Texttable.HEADER)\n",
|
||||
" table_eval_windows.set_cols_align([\"l\", \"c\",\"c\", \"c\", \"c\", \"c\", \"c\", \"c\", \"c\", \"c\"])\n",
|
||||
" table_eval_windows.set_precision(3)\n",
|
||||
"\n",
|
||||
" table_eval_windows.header([\n",
|
||||
" \"\\\\textbf{Strategy}\",\n",
|
||||
" \"\\\\textbf{VAL}\",\n",
|
||||
" \"\\\\textbf{ARC}\",\n",
|
||||
" \"\\\\textbf{ASD}\",\n",
|
||||
" \"\\\\textbf{IR*}\",\n",
|
||||
" \"\\\\textbf{MD}\",\n",
|
||||
" \"\\\\textbf{IR**}\",\n",
|
||||
" \"\\\\textbf{N}\",\n",
|
||||
" \"\\\\textbf{LONG}\",\n",
|
||||
" \"\\\\textbf{SHORT}\",\n",
|
||||
" ])\n",
|
||||
"\n",
|
||||
" strategy_name_result = [\n",
|
||||
" ('Buy and Hold', result_buyandhold),\n",
|
||||
" ('MACD Strategy', result_macd),\n",
|
||||
" ('RSI Strategy', result_rsi),\n",
|
||||
" ('Quantile Informer', result_quantile_model),\n",
|
||||
" ('GMADL Informer', result_gmadl_model)\n",
|
||||
" ]\n",
|
||||
" for strategy_name, result in strategy_name_result:\n",
|
||||
" table_eval_windows.add_row([\n",
|
||||
" strategy_name,\n",
|
||||
" result['value'],\n",
|
||||
" result['arc'],\n",
|
||||
" result['asd'],\n",
|
||||
" result['ir'],\n",
|
||||
" result['md'],\n",
|
||||
" result['mod_ir'],\n",
|
||||
" result['n_trades'],\n",
|
||||
" f\"{result['long_pos']*100:.2f}\\%\",\n",
|
||||
" f\"{result['short_pos']*100:.2f}\\%\",\n",
|
||||
" ])\n",
|
||||
" print(latextable.draw_latex(table_eval_windows))\n",
|
||||
"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"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_macd = evaluate_strategy(padded_window, [s[0] for s in best_strategies['macd_strategies']][i], padding=PADDING, interval=INTERVAL)\n",
|
||||
"# result_rsi = evaluate_strategy(padded_window, [s[0] for s in best_strategies['rsi_strategies']][i], padding=PADDING, interval=INTERVAL)\n",
|
||||
"# result_quantile_model = evaluate_strategy(padded_window, [s[0] for s in best_strategies['quantile_model']][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_macd, result_rsi, result_quantile_model, result_gmadl_model)\n",
|
||||
"# results_plot(i+1, result_buyandhold, result_macd, result_rsi, result_quantile_model, result_gmadl_model)\n",
|
||||
" \n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Plot & Table for the whole testing period"
|
||||
]
|
||||
},
|
||||
{
|
||||
"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",
|
||||
"# macd_concat = evaluate_strategy(test_data, ConcatenatedStrategies(len(data_windows[0][1]), [s[0] for s in best_strategies['macd_strategies']], padding=PADDING), padding=PADDING, interval=INTERVAL)\n",
|
||||
"# rsi_concat = evaluate_strategy(test_data, ConcatenatedStrategies(len(data_windows[0][1]), [s[0] for s in best_strategies['rsi_strategies']], padding=PADDING), padding=PADDING, interval=INTERVAL)\n",
|
||||
"# quantile_model_concat = evaluate_strategy(test_data, ConcatenatedStrategies(len(data_windows[0][1]), [s[0] for s in best_strategies['quantile_model']], padding=PADDING), 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",
|
||||
"# results_table(buy_and_hold_concat, macd_concat, rsi_concat, quantile_model_concat, gmadl_model_concat)\n",
|
||||
"# results_plot(0, buy_and_hold_concat, macd_concat, rsi_concat, quantile_model_concat, gmadl_model_concat, width=1200, notitle=True)\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"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
|
||||
}
|
||||
789
notebooks/btcusdt_5m_evaluation.ipynb
Normal file
789
notebooks/btcusdt_5m_evaluation.ipynb
Normal file
@ -0,0 +1,789 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 109,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import pandas as pd\n",
|
||||
"import pickle\n",
|
||||
"import plotly.graph_objs as go\n",
|
||||
"import latextable\n",
|
||||
"from texttable import Texttable\n",
|
||||
"from strategy.strategy import (\n",
|
||||
" BuyAndHoldStrategy,\n",
|
||||
" MACDStrategy,\n",
|
||||
" RSIStrategy,\n",
|
||||
" ModelQuantilePredictionsStrategy,\n",
|
||||
" ModelGmadlPredictionsStrategy,\n",
|
||||
" ConcatenatedStrategies\n",
|
||||
")\n",
|
||||
"from strategy.util import (\n",
|
||||
" get_data_windows,\n",
|
||||
" get_sweep_window_predictions,\n",
|
||||
" get_predictions_dataframe\n",
|
||||
")\n",
|
||||
"from strategy.evaluation import (\n",
|
||||
" parameter_sweep,\n",
|
||||
" evaluate_strategy\n",
|
||||
")\n",
|
||||
"from strategy.plotting import (\n",
|
||||
" plot_sweep_results\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"PADDING=5000\n",
|
||||
"VALID_PART=0.4\n",
|
||||
"INTERVAL='5min'\n",
|
||||
"METRIC='mod_ir'\n",
|
||||
"TOP_N=10"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 94,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"\u001b[34m\u001b[1mwandb\u001b[0m: Downloading large artifact btc-usdt-5m:latest, 745.12MB. 12 files... \n",
|
||||
"\u001b[34m\u001b[1mwandb\u001b[0m: 12 of 12 files downloaded. \n",
|
||||
"Done. 0:0:0.8\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"data_windows = get_data_windows(\n",
|
||||
" 'wne-masters-thesis-testing',\n",
|
||||
" 'btc-usdt-5m:latest',\n",
|
||||
" min_window=0, \n",
|
||||
" max_window=5\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 95,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def sweeps_on_all_windows(data_windows, strategy_class, params, **kwargs):\n",
|
||||
" result = []\n",
|
||||
" for in_sample, _ in data_windows:\n",
|
||||
" data_part = int((1 - VALID_PART) * len(in_sample))\n",
|
||||
" result.append(parameter_sweep(in_sample[data_part-PADDING:], strategy_class, params, padding=PADDING, interval=INTERVAL, **kwargs))\n",
|
||||
" return result"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 96,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"buyandhold_best_strategies = [BuyAndHoldStrategy() for _ in data_windows] "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 97,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"100%|██████████| 3840/3840 [00:25<00:00, 148.93it/s]\n",
|
||||
"100%|██████████| 3840/3840 [00:25<00:00, 149.05it/s]\n",
|
||||
"100%|██████████| 3840/3840 [00:27<00:00, 138.86it/s]\n",
|
||||
"100%|██████████| 3840/3840 [00:28<00:00, 133.50it/s]\n",
|
||||
"100%|██████████| 3840/3840 [00:25<00:00, 152.79it/s]\n",
|
||||
"100%|██████████| 3840/3840 [00:22<00:00, 168.94it/s]\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"MACD_PARAMS = {\n",
|
||||
" 'fast_window_size': [2, 3, 5, 8, 13, 21, 34, 55, 89, 144, 233, 377, 610, 987, 1597, 2584],\n",
|
||||
" 'slow_window_size': [2, 3, 5, 8, 13, 21, 34, 55, 89, 144, 233, 377, 610, 987, 1597, 2584],\n",
|
||||
" 'signal_window_size': [2, 3, 5, 8, 13, 21, 34, 55, 89, 144, 233, 377, 610, 987, 1597, 2584],\n",
|
||||
" 'short_sell': [True, False]\n",
|
||||
"}\n",
|
||||
"MACD_PARAMS_FILTER = lambda p: (\n",
|
||||
" p['slow_window_size'] > p['fast_window_size']\n",
|
||||
")\n",
|
||||
"macd_sweep_results = sweeps_on_all_windows(\n",
|
||||
" data_windows,\n",
|
||||
" MACDStrategy,\n",
|
||||
" MACD_PARAMS,\n",
|
||||
" params_filter=MACD_PARAMS_FILTER,\n",
|
||||
" sort_by=METRIC)\n",
|
||||
"macd_best_strategies = [[strat for _, strat in results[:TOP_N]] for results in macd_sweep_results]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 98,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# plot_sweep_results(pd.DataFrame([result for result, _ in macd_sweep_results[0]]), parameters=MACD_PARAMS.keys(), objective=METRIC)\n",
|
||||
"# macd_sweep_results[0][:3]\n",
|
||||
"# macd_best_strategies"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 99,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"100%|██████████| 11088/11088 [01:14<00:00, 149.71it/s]\n",
|
||||
"100%|██████████| 11088/11088 [01:18<00:00, 140.49it/s]\n",
|
||||
"100%|██████████| 11088/11088 [01:14<00:00, 148.45it/s]\n",
|
||||
"100%|██████████| 11088/11088 [01:15<00:00, 147.54it/s]\n",
|
||||
"100%|██████████| 11088/11088 [01:09<00:00, 160.15it/s]\n",
|
||||
"100%|██████████| 11088/11088 [01:10<00:00, 158.01it/s]\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"RSI_FILTER = lambda p: (\n",
|
||||
" ((p['enter_long'] is not None and (p['enter_short'] is not None or p['exit_long'] is not None))\n",
|
||||
" or (p['enter_short'] is not None and (p['exit_short'] is not None or p['enter_long'] is not None)))\n",
|
||||
" and (p['enter_short'] is None or p['exit_long'] is None or (p['exit_long'] > p['enter_short']))\n",
|
||||
" and (p['enter_long'] is None or p['exit_short'] is None or (p['exit_short'] < p['enter_long'])))\n",
|
||||
"\n",
|
||||
"RSI_PARAMS = {\n",
|
||||
" 'window_size': [2, 3, 5, 8, 13, 21, 34, 55, 89, 144, 233, 377, 610, 987, 1597, 2584],\n",
|
||||
" 'enter_long': [None, 70, 75, 80, 85, 90, 95],\n",
|
||||
" 'exit_long': [None, 5, 10, 15, 20, 25, 30],\n",
|
||||
" 'enter_short': [None, 5, 10, 15, 20, 25, 30],\n",
|
||||
" 'exit_short': [None, 70, 75, 80, 85, 90, 95],\n",
|
||||
"}\n",
|
||||
"rsi_sweep_results = sweeps_on_all_windows(data_windows, RSIStrategy, RSI_PARAMS, params_filter=RSI_FILTER, sort_by=METRIC)\n",
|
||||
"rsi_best_strategies = [[strat for _, strat in results[:TOP_N]] for results in rsi_sweep_results]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 100,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# plot_sweep_results(pd.DataFrame([result for result, _ in rsi_sweep_results[0]]), parameters=RSI_PARAMS.keys(), objective=METRIC)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 101,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"\u001b[34m\u001b[1mwandb\u001b[0m: 2 of 2 files downloaded. \n",
|
||||
"\u001b[34m\u001b[1mwandb\u001b[0m: 2 of 2 files downloaded. \n",
|
||||
"\u001b[34m\u001b[1mwandb\u001b[0m: 2 of 2 files downloaded. \n",
|
||||
"\u001b[34m\u001b[1mwandb\u001b[0m: 2 of 2 files downloaded. \n",
|
||||
"\u001b[34m\u001b[1mwandb\u001b[0m: 2 of 2 files downloaded. \n",
|
||||
"\u001b[34m\u001b[1mwandb\u001b[0m: 2 of 2 files downloaded. \n",
|
||||
"\u001b[34m\u001b[1mwandb\u001b[0m: 2 of 2 files downloaded. \n",
|
||||
"\u001b[34m\u001b[1mwandb\u001b[0m: 2 of 2 files downloaded. \n",
|
||||
"\u001b[34m\u001b[1mwandb\u001b[0m: 2 of 2 files downloaded. \n",
|
||||
"\u001b[34m\u001b[1mwandb\u001b[0m: 2 of 2 files downloaded. \n",
|
||||
"\u001b[34m\u001b[1mwandb\u001b[0m: 2 of 2 files downloaded. \n",
|
||||
"\u001b[34m\u001b[1mwandb\u001b[0m: 2 of 2 files downloaded. \n",
|
||||
"\u001b[34m\u001b[1mwandb\u001b[0m: 2 of 2 files downloaded. \n",
|
||||
"\u001b[34m\u001b[1mwandb\u001b[0m: 2 of 2 files downloaded. \n",
|
||||
"\u001b[34m\u001b[1mwandb\u001b[0m: 2 of 2 files downloaded. \n",
|
||||
"\u001b[34m\u001b[1mwandb\u001b[0m: 2 of 2 files downloaded. \n",
|
||||
"\u001b[34m\u001b[1mwandb\u001b[0m: 2 of 2 files downloaded. \n",
|
||||
"\u001b[34m\u001b[1mwandb\u001b[0m: 2 of 2 files downloaded. \n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Model with quantile loss\n",
|
||||
"SWEEP_ID = 'filipstefaniuk/wne-masters-thesis-testing/8m3hwwmx'\n",
|
||||
"train_pred_windows = get_sweep_window_predictions(SWEEP_ID, 'train')\n",
|
||||
"valid_pred_windows = get_sweep_window_predictions(SWEEP_ID, 'valid')\n",
|
||||
"test_pred_windows = get_sweep_window_predictions(SWEEP_ID, 'test')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 102,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"100%|██████████| 1125/1125 [02:49<00:00, 6.65it/s]\n",
|
||||
"100%|██████████| 1125/1125 [02:51<00:00, 6.55it/s]\n",
|
||||
"100%|██████████| 1125/1125 [02:56<00:00, 6.36it/s]\n",
|
||||
"100%|██████████| 1125/1125 [02:50<00:00, 6.61it/s]\n",
|
||||
"100%|██████████| 1125/1125 [02:47<00:00, 6.72it/s]\n",
|
||||
"100%|██████████| 1125/1125 [02:48<00:00, 6.66it/s]\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"MODEL_QUANTILE_LOSS_FILTER = lambda p: (\n",
|
||||
" ((p['quantile_enter_long'] is not None and (p['quantile_enter_short'] is not None or p['quantile_exit_long'] is not None))\n",
|
||||
" or (p['quantile_enter_short'] is not None and (p['quantile_exit_short'] is not None or p['quantile_enter_long'] is not None)))\n",
|
||||
" and (p['quantile_enter_short'] is None or p['quantile_exit_long'] is None or (p['quantile_exit_long'] < p['quantile_enter_short']))\n",
|
||||
" and (p['quantile_enter_long'] is None or p['quantile_exit_short'] is None or (p['quantile_exit_short'] < p['quantile_enter_long'])))\n",
|
||||
"\n",
|
||||
"quantile_model_sweep_results = []\n",
|
||||
"for (in_sample, _), train_preds, valid_preds, test_preds in zip(data_windows, train_pred_windows, valid_pred_windows, test_pred_windows):\n",
|
||||
" data_part = int((1 - VALID_PART) * len(in_sample))\n",
|
||||
" params={\n",
|
||||
" 'predictions': [get_predictions_dataframe(train_preds, valid_preds, test_preds)],\n",
|
||||
" 'quantiles': [[0.01, 0.02, 0.03, 0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95, 0.97, 0.98, 0.99]],\n",
|
||||
" 'quantile_enter_long': [None, 0.9, 0.95, 0.97, 0.98, 0.99],\n",
|
||||
" 'quantile_exit_long': [None, 0.9, 0.95, 0.97, 0.98, 0.99],\n",
|
||||
" 'quantile_enter_short': [None, 0.9, 0.95, 0.97, 0.98, 0.99],\n",
|
||||
" 'quantile_exit_short': [None, 0.9, 0.95, 0.97, 0.98, 0.99],\n",
|
||||
" 'exchange_fee': [0.001, 0.002, 0.003],\n",
|
||||
" 'future': [1]\n",
|
||||
" }\n",
|
||||
" \n",
|
||||
" quantile_model_sweep_results.append(parameter_sweep(\n",
|
||||
" in_sample[data_part-PADDING:],\n",
|
||||
" ModelQuantilePredictionsStrategy,\n",
|
||||
" params,\n",
|
||||
" params_filter=MODEL_QUANTILE_LOSS_FILTER,\n",
|
||||
" padding=PADDING,\n",
|
||||
" interval=INTERVAL,\n",
|
||||
" sort_by=METRIC))\n",
|
||||
"\n",
|
||||
"quantile_model_best_strategies = [[strat for _, strat in results[:TOP_N]] for results in quantile_model_sweep_results]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 103,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# plot_sweep_results(pd.DataFrame([result for result, _ in quantile_model_sweep_results[0]]), parameters=['quantile_enter_long', 'quantile_exit_long', 'quantile_enter_short', 'quantile_exit_short', 'future'], objective=METRIC)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 104,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"\u001b[34m\u001b[1mwandb\u001b[0m: 2 of 2 files downloaded. \n",
|
||||
"\u001b[34m\u001b[1mwandb\u001b[0m: 2 of 2 files downloaded. \n",
|
||||
"\u001b[34m\u001b[1mwandb\u001b[0m: 2 of 2 files downloaded. \n",
|
||||
"\u001b[34m\u001b[1mwandb\u001b[0m: 2 of 2 files downloaded. \n",
|
||||
"\u001b[34m\u001b[1mwandb\u001b[0m: 2 of 2 files downloaded. \n",
|
||||
"\u001b[34m\u001b[1mwandb\u001b[0m: 2 of 2 files downloaded. \n",
|
||||
"\u001b[34m\u001b[1mwandb\u001b[0m: 2 of 2 files downloaded. \n",
|
||||
"\u001b[34m\u001b[1mwandb\u001b[0m: 2 of 2 files downloaded. \n",
|
||||
"\u001b[34m\u001b[1mwandb\u001b[0m: 2 of 2 files downloaded. \n",
|
||||
"\u001b[34m\u001b[1mwandb\u001b[0m: 2 of 2 files downloaded. \n",
|
||||
"\u001b[34m\u001b[1mwandb\u001b[0m: 2 of 2 files downloaded. \n",
|
||||
"\u001b[34m\u001b[1mwandb\u001b[0m: 2 of 2 files downloaded. \n",
|
||||
"\u001b[34m\u001b[1mwandb\u001b[0m: 2 of 2 files downloaded. \n",
|
||||
"\u001b[34m\u001b[1mwandb\u001b[0m: 2 of 2 files downloaded. \n",
|
||||
"\u001b[34m\u001b[1mwandb\u001b[0m: 2 of 2 files downloaded. \n",
|
||||
"\u001b[34m\u001b[1mwandb\u001b[0m: 2 of 2 files downloaded. \n",
|
||||
"\u001b[34m\u001b[1mwandb\u001b[0m: 2 of 2 files downloaded. \n",
|
||||
"\u001b[34m\u001b[1mwandb\u001b[0m: 2 of 2 files downloaded. \n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Model with gmadl loss\n",
|
||||
"SWEEP_ID = 'filipstefaniuk/wne-masters-thesis-testing/0pro3i5i'\n",
|
||||
"train_gmadl_pred_windows = get_sweep_window_predictions(SWEEP_ID, 'train')\n",
|
||||
"valid_gmadl_pred_windows = get_sweep_window_predictions(SWEEP_ID, 'valid')\n",
|
||||
"test_gmadl_pred_windows = get_sweep_window_predictions(SWEEP_ID, 'test')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 110,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"100%|██████████| 1176/1176 [02:30<00:00, 7.82it/s]\n",
|
||||
"100%|██████████| 1176/1176 [02:31<00:00, 7.78it/s]\n",
|
||||
"100%|██████████| 1176/1176 [02:31<00:00, 7.77it/s]\n",
|
||||
"100%|██████████| 1176/1176 [02:32<00:00, 7.72it/s]\n",
|
||||
"100%|██████████| 1176/1176 [02:30<00:00, 7.84it/s]\n",
|
||||
"100%|██████████| 1176/1176 [02:30<00:00, 7.80it/s]\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"MODEL_GMADL_LOSS_FILTER = lambda p: (\n",
|
||||
" ((p['enter_long'] is not None and (p['enter_short'] is not None or p['exit_long'] is not None))\n",
|
||||
" or (p['enter_short'] is not None and (p['exit_short'] is not None or p['enter_long'] is not None)))\n",
|
||||
" and (p['enter_short'] is None or p['exit_long'] is None or (p['exit_long'] > p['enter_short']))\n",
|
||||
" and (p['enter_long'] is None or p['exit_short'] is None or (p['exit_short'] < p['enter_long'])))\n",
|
||||
"\n",
|
||||
"gmadl_model_sweep_results = []\n",
|
||||
"for (in_sample, _), train_preds, valid_preds, test_preds in zip(data_windows, train_gmadl_pred_windows, valid_gmadl_pred_windows, test_gmadl_pred_windows):\n",
|
||||
" data_part = int((1 - VALID_PART) * len(in_sample))\n",
|
||||
" params={\n",
|
||||
" 'predictions': [get_predictions_dataframe(train_preds, valid_preds, test_preds)],\n",
|
||||
" 'enter_long': [None, 0.001, 0.002, 0.003, 0.004, 0.005, 0.006, 0.007],\n",
|
||||
" 'exit_long': [None, -0.001, -0.002, -0.003, -0.004, -0.005, -0.006, -0.007],\n",
|
||||
" 'enter_short': [None, -0.001, -0.002, -0.003, -0.004, -0.005, -0.006, -0.007],\n",
|
||||
" 'exit_short': [None, 0.001, 0.002, 0.003, 0.004, 0.005, 0.006, 0.007],\n",
|
||||
" }\n",
|
||||
" \n",
|
||||
" gmadl_model_sweep_results.append(parameter_sweep(\n",
|
||||
" in_sample[data_part-PADDING:],\n",
|
||||
" ModelGmadlPredictionsStrategy,\n",
|
||||
" params,\n",
|
||||
" params_filter=MODEL_GMADL_LOSS_FILTER,\n",
|
||||
" padding=PADDING,\n",
|
||||
" interval=INTERVAL,\n",
|
||||
" sort_by=METRIC))\n",
|
||||
" \n",
|
||||
"\n",
|
||||
"gmadl_model_best_strategies = [[strat for _, strat in results[:TOP_N]] for results in gmadl_model_sweep_results]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 111,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Persist best strategies, so that they don't have to be recomputed every time\n",
|
||||
"best_strategies = {\n",
|
||||
" 'buy_and_hold': buyandhold_best_strategies,\n",
|
||||
" 'macd_strategies': macd_best_strategies,\n",
|
||||
" 'rsi_strategies': rsi_best_strategies,\n",
|
||||
" 'quantile_model': quantile_model_best_strategies,\n",
|
||||
" 'gmadl_model': gmadl_model_best_strategies\n",
|
||||
"}\n",
|
||||
"\n",
|
||||
"with open('cache/5min-best-strategies-04.pkl', 'wb') as outp:\n",
|
||||
" pickle.dump(best_strategies, outp, pickle.HIGHEST_PROTOCOL)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Visualizations & Tables"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 52,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"with open('cache/5min-best-strategies.pkl', 'rb') as inpt:\n",
|
||||
" best_strategies = pickle.load(inpt)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Tables with parameters"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 53,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"\\begin{table}\n",
|
||||
"\t\\begin{center}\n",
|
||||
"\t\t\\begin{tabular}{lcccc}\n",
|
||||
"\t\t\t\\textbf{Window} & \\textbf{Fast Window Size} & \\textbf{Slow Window Size} & \\textbf{Signal Window Size} & \\textbf{Short sell} \\\\\n",
|
||||
"\t\t\t\\hline\n",
|
||||
"\t\t\tWindow 1 & 8 & 2584 & 987 & True \\\\\n",
|
||||
"\t\t\tWindow 2 & 377 & 610 & 610 & True \\\\\n",
|
||||
"\t\t\tWindow 3 & 987 & 2584 & 987 & True \\\\\n",
|
||||
"\t\t\tWindow 4 & 610 & 2584 & 987 & True \\\\\n",
|
||||
"\t\t\tWindow 5 & 987 & 1597 & 987 & False \\\\\n",
|
||||
"\t\t\tWindow 6 & 1597 & 2584 & 377 & False \\\\\n",
|
||||
"\t\t\\end{tabular}\n",
|
||||
"\t\\end{center}\n",
|
||||
"\\end{table}\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Best hparams for MACD strategy\n",
|
||||
"table_macd_params = Texttable()\n",
|
||||
"table_macd_params.set_deco(Texttable.HEADER)\n",
|
||||
"table_macd_params.set_cols_align([\"l\", \"c\", \"c\", \"c\", \"c\"])\n",
|
||||
"table_macd_params.header([\n",
|
||||
" \"\\\\textbf{Window}\",\n",
|
||||
" \"\\\\textbf{Fast Window Size}\",\n",
|
||||
" \"\\\\textbf{Slow Window Size}\",\n",
|
||||
" \"\\\\textbf{Signal Window Size}\",\n",
|
||||
" \"\\\\textbf{Short sell}\"\n",
|
||||
"])\n",
|
||||
"\n",
|
||||
"for i, macd_strategy in enumerate(best_strategies['macd_strategies']):\n",
|
||||
" macd_strategy_info = macd_strategy[0].info()\n",
|
||||
" table_macd_params.add_row([\n",
|
||||
" f\"Window {i+1}\",\n",
|
||||
" macd_strategy_info['fast_window_size'],\n",
|
||||
" macd_strategy_info['slow_window_size'],\n",
|
||||
" macd_strategy_info['signal_window_size'],\n",
|
||||
" macd_strategy_info['short_sell']\n",
|
||||
" ])\n",
|
||||
"print(latextable.draw_latex(table_macd_params))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 55,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"\\begin{table}\n",
|
||||
"\t\\begin{center}\n",
|
||||
"\t\t\\begin{tabular}{lccccc}\n",
|
||||
"\t\t\t\\textbf{Window} & \\textbf{\\textit{window}} & \\textbf{\\textit{enter long}} & \\textbf{\\textit{exit long}} & \\textbf{\\textit{enter short}} & \\textbf{\\textit{exit short}} \\\\\n",
|
||||
"\t\t\t\\hline\n",
|
||||
"\t\t\tW1-5min & 21 & 80 & - & 25 & - \\\\\n",
|
||||
"\t\t\tW2-5min & 34 & 75 & - & 25 & - \\\\\n",
|
||||
"\t\t\tW3-5min & 5 & 95 & - & 15 & - \\\\\n",
|
||||
"\t\t\tW4-5min & 8 & 90 & 5 & - & 85 \\\\\n",
|
||||
"\t\t\tW5-5min & 13 & 90 & 20 & - & 85 \\\\\n",
|
||||
"\t\t\tW6-5min & 21 & 85 & 15 & - & - \\\\\n",
|
||||
"\t\t\\end{tabular}\n",
|
||||
"\t\\end{center}\n",
|
||||
"\\end{table}\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"table_rsi_params = Texttable()\n",
|
||||
"table_rsi_params.set_deco(Texttable.HEADER)\n",
|
||||
"table_rsi_params.set_cols_align([\"l\", \"c\",\"c\", \"c\", \"c\", \"c\"])\n",
|
||||
"table_rsi_params.header([\n",
|
||||
" \"\\\\textbf{Window}\",\n",
|
||||
" \"\\\\textbf{\\\\textit{window}}\",\n",
|
||||
" \"\\\\textbf{\\\\textit{enter long}}\",\n",
|
||||
" \"\\\\textbf{\\\\textit{exit long}}\",\n",
|
||||
" \"\\\\textbf{\\\\textit{enter short}}\",\n",
|
||||
" \"\\\\textbf{\\\\textit{exit short}}\"\n",
|
||||
"])\n",
|
||||
"\n",
|
||||
"for i, rsi_strategy in enumerate(best_strategies['rsi_strategies']):\n",
|
||||
" rsi_strategy_info = rsi_strategy[0].info()\n",
|
||||
" table_rsi_params.add_row([\n",
|
||||
" f\"W{i+1}-5min\",\n",
|
||||
" rsi_strategy_info['window_size'] or '-',\n",
|
||||
" rsi_strategy_info['enter_long'] or '-',\n",
|
||||
" rsi_strategy_info['exit_long'] or '-',\n",
|
||||
" rsi_strategy_info['enter_short'] or '-',\n",
|
||||
" rsi_strategy_info['exit_short'] or '-'\n",
|
||||
" ])\n",
|
||||
"print(latextable.draw_latex(table_rsi_params))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 56,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"\\begin{table}\n",
|
||||
"\t\\begin{center}\n",
|
||||
"\t\t\\begin{tabular}{lccccc}\n",
|
||||
"\t\t\t\\textbf{Window} & \\textbf{\\textit{enter long}} & \\textbf{\\textit{exit Long}} & \\textbf{\\textit{enter Short}} & \\textbf{\\textit{exit Short}} & \\textbf{\\textit{threshold}} \\\\\n",
|
||||
"\t\t\t\\hline\n",
|
||||
"\t\t\tW1-5min & - & 0.970 & 0.980 & 0.970 & 0.003 \\\\\n",
|
||||
"\t\t\tW2-5min & 0.970 & - & 0.950 & - & 0.003 \\\\\n",
|
||||
"\t\t\tW3-5min & 0.990 & - & 0.950 & - & 0.002 \\\\\n",
|
||||
"\t\t\tW4-5min & 0.970 & 0.990 & - & 0.900 & 0.003 \\\\\n",
|
||||
"\t\t\tW5-5min & 0.900 & 0.900 & - & - & 0.002 \\\\\n",
|
||||
"\t\t\tW6-5min & 0.950 & 0.950 & - & - & 0.003 \\\\\n",
|
||||
"\t\t\\end{tabular}\n",
|
||||
"\t\\end{center}\n",
|
||||
"\\end{table}\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"table_quantile_params = Texttable()\n",
|
||||
"table_quantile_params.set_deco(Texttable.HEADER)\n",
|
||||
"table_quantile_params.set_cols_align([\"l\", \"c\",\"c\", \"c\", \"c\", \"c\"])\n",
|
||||
"table_quantile_params.header([\n",
|
||||
" \"\\\\textbf{Window}\",\n",
|
||||
" \"\\\\textbf{\\\\textit{enter long}}\",\n",
|
||||
" \"\\\\textbf{\\\\textit{exit Long}}\",\n",
|
||||
" \"\\\\textbf{\\\\textit{enter Short}}\",\n",
|
||||
" \"\\\\textbf{\\\\textit{exit Short}}\",\n",
|
||||
" \"\\\\textbf{\\\\textit{threshold}}\"\n",
|
||||
"])\n",
|
||||
"\n",
|
||||
"for i, quantile_strategy in enumerate(best_strategies['quantile_model']):\n",
|
||||
" quantile_strategy_info = quantile_strategy[0].info()\n",
|
||||
" table_quantile_params.add_row([\n",
|
||||
" f\"W{i+1}-{INTERVAL}\",\n",
|
||||
" quantile_strategy_info['quantile_enter_long'] or '-',\n",
|
||||
" quantile_strategy_info['quantile_exit_long'] or '-',\n",
|
||||
" quantile_strategy_info['quantile_enter_short'] or '-',\n",
|
||||
" quantile_strategy_info['quantile_exit_short'] or '-',\n",
|
||||
" quantile_strategy_info['exchange_fee']\n",
|
||||
" ])\n",
|
||||
"print(latextable.draw_latex(table_quantile_params))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 57,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"\\begin{table}\n",
|
||||
"\t\\begin{center}\n",
|
||||
"\t\t\\begin{tabular}{lcccc}\n",
|
||||
"\t\t\t\\textbf{Window} & \\textbf{\\textit{enter long}} & \\textbf{\\textit{exit Long}} & \\textbf{\\textit{enter Short}} & \\textbf{\\textit{exit Short}} \\\\\n",
|
||||
"\t\t\t\\hline\n",
|
||||
"\t\t\tW1-5min & 0.004 & - & -0.005 & - \\\\\n",
|
||||
"\t\t\tW2-5min & 0.002 & - & -0.001 & - \\\\\n",
|
||||
"\t\t\tW3-5min & - & - & -0.006 & 0.003 \\\\\n",
|
||||
"\t\t\tW4-5min & 0.002 & - & -0.005 & - \\\\\n",
|
||||
"\t\t\tW5-5min & 0.002 & - & -0.003 & - \\\\\n",
|
||||
"\t\t\tW6-5min & 0.001 & - & -0.007 & - \\\\\n",
|
||||
"\t\t\\end{tabular}\n",
|
||||
"\t\\end{center}\n",
|
||||
"\\end{table}\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"table_gmadl_params = Texttable()\n",
|
||||
"table_gmadl_params.set_deco(Texttable.HEADER)\n",
|
||||
"table_gmadl_params.set_cols_align([\"l\", \"c\",\"c\", \"c\", \"c\"])\n",
|
||||
"table_gmadl_params.header([\n",
|
||||
" \"\\\\textbf{Window}\",\n",
|
||||
" \"\\\\textbf{\\\\textit{enter long}}\",\n",
|
||||
" \"\\\\textbf{\\\\textit{exit Long}}\",\n",
|
||||
" \"\\\\textbf{\\\\textit{enter Short}}\",\n",
|
||||
" \"\\\\textbf{\\\\textit{exit Short}}\",\n",
|
||||
"])\n",
|
||||
"\n",
|
||||
"for i, gmadl_strategy in enumerate(best_strategies['gmadl_model']):\n",
|
||||
" gmadl_strategy_info = gmadl_strategy[0].info()\n",
|
||||
" table_gmadl_params.add_row([\n",
|
||||
" f\"W{i+1}-{INTERVAL}\",\n",
|
||||
" gmadl_strategy_info['enter_long'] or '-',\n",
|
||||
" gmadl_strategy_info['exit_long'] or '-',\n",
|
||||
" gmadl_strategy_info['enter_short'] or '-',\n",
|
||||
" gmadl_strategy_info['exit_short'] or '-'\n",
|
||||
" ])\n",
|
||||
"print(latextable.draw_latex(table_gmadl_params))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Evaluation on the windows"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 61,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def results_plot(idx, result_buyandhold, result_macd, result_rsi, result_quantile_model, result_gmadl_model, width=850, height=500, notitle=False):\n",
|
||||
"\n",
|
||||
" fig = go.Figure([\n",
|
||||
" go.Scatter(y=result_buyandhold['portfolio_value'], x=result_buyandhold['time'], name=\"Buy and Hold\"),\n",
|
||||
" go.Scatter(y=result_macd['portfolio_value'], x=result_macd['time'], name=\"MACD Strategy\"),\n",
|
||||
" go.Scatter(y=result_rsi['portfolio_value'], x=result_rsi['time'], name=\"RSI Strategy\"),\n",
|
||||
" go.Scatter(y=result_quantile_model['portfolio_value'], x=result_quantile_model['time'], name='Quantile Informer Strategy'),\n",
|
||||
" go.Scatter(y=result_gmadl_model['portfolio_value'], x=result_gmadl_model['time'], name='GMADL Informer Strategy')\n",
|
||||
" ])\n",
|
||||
" fig.update_layout(\n",
|
||||
" title={\n",
|
||||
" 'text': f\"W{idx}-{INTERVAL}\",\n",
|
||||
" 'y':0.97,\n",
|
||||
" 'x':0.5,\n",
|
||||
" 'xanchor': 'center',\n",
|
||||
" 'yanchor': 'top'} if not notitle else None,\n",
|
||||
" yaxis_title=\"Portfolio Value\",\n",
|
||||
" xaxis_title=\"Date\",\n",
|
||||
" font=dict(\n",
|
||||
" # family=\"Courier New, monospace\",\n",
|
||||
" size=14,\n",
|
||||
" ),\n",
|
||||
" autosize=False,\n",
|
||||
" width=width,\n",
|
||||
" height=height,\n",
|
||||
" margin=dict(l=20, r=20, t=20 if notitle else 110, b=20),\n",
|
||||
" plot_bgcolor='white',\n",
|
||||
" legend=dict(\n",
|
||||
" orientation=\"h\",\n",
|
||||
" yanchor=\"bottom\",\n",
|
||||
" y=1.02,\n",
|
||||
" xanchor=\"left\",\n",
|
||||
" x=0.02\n",
|
||||
" )\n",
|
||||
" )\n",
|
||||
" fig.update_xaxes(\n",
|
||||
" mirror=True,\n",
|
||||
" ticks='outside',\n",
|
||||
" showline=True,\n",
|
||||
" linecolor='black',\n",
|
||||
" gridcolor='lightgrey'\n",
|
||||
" )\n",
|
||||
" fig.update_yaxes(\n",
|
||||
" mirror=True,\n",
|
||||
" ticks='outside',\n",
|
||||
" showline=True,\n",
|
||||
" linecolor='black',\n",
|
||||
" gridcolor='lightgrey'\n",
|
||||
" )\n",
|
||||
" fig.write_image(f\"images/eval-w{idx}-{INTERVAL}.png\")\n",
|
||||
" fig.show()\n",
|
||||
" \n",
|
||||
"def results_table(result_buyandhold, result_macd, result_rsi, result_quantile_model, result_gmadl_model):\n",
|
||||
" table_eval_windows = Texttable()\n",
|
||||
" table_eval_windows.set_deco(Texttable.HEADER)\n",
|
||||
" table_eval_windows.set_cols_align([\"l\", \"c\",\"c\", \"c\", \"c\", \"c\", \"c\", \"c\", \"c\", \"c\"])\n",
|
||||
" table_eval_windows.set_precision(3)\n",
|
||||
"\n",
|
||||
" table_eval_windows.header([\n",
|
||||
" \"\\\\textbf{Strategy}\",\n",
|
||||
" \"\\\\textbf{VAL}\",\n",
|
||||
" \"\\\\textbf{ARC}\",\n",
|
||||
" \"\\\\textbf{ASD}\",\n",
|
||||
" \"\\\\textbf{IR*}\",\n",
|
||||
" \"\\\\textbf{MD}\",\n",
|
||||
" \"\\\\textbf{IR**}\",\n",
|
||||
" \"\\\\textbf{N}\",\n",
|
||||
" \"\\\\textbf{LONG}\",\n",
|
||||
" \"\\\\textbf{SHORT}\",\n",
|
||||
" ])\n",
|
||||
"\n",
|
||||
" strategy_name_result = [\n",
|
||||
" ('Buy and Hold', result_buyandhold),\n",
|
||||
" ('MACD Strategy', result_macd),\n",
|
||||
" ('RSI Strategy', result_rsi),\n",
|
||||
" ('Quantile Informer', result_quantile_model),\n",
|
||||
" ('GMADL Informer', result_gmadl_model)\n",
|
||||
" ]\n",
|
||||
" for strategy_name, result in strategy_name_result:\n",
|
||||
" table_eval_windows.add_row([\n",
|
||||
" strategy_name,\n",
|
||||
" result['value'],\n",
|
||||
" result['arc'],\n",
|
||||
" result['asd'],\n",
|
||||
" result['ir'],\n",
|
||||
" result['md'],\n",
|
||||
" result['mod_ir'],\n",
|
||||
" result['n_trades'],\n",
|
||||
" f\"{result['long_pos']*100:.2f}\\%\",\n",
|
||||
" f\"{result['short_pos']*100:.2f}\\%\",\n",
|
||||
" ])\n",
|
||||
" print(latextable.draw_latex(table_eval_windows))\n",
|
||||
"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"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_macd = evaluate_strategy(padded_window, [s[0] for s in best_strategies['macd_strategies']][i], padding=PADDING, interval=INTERVAL)\n",
|
||||
"# result_rsi = evaluate_strategy(padded_window, [s[0] for s in best_strategies['rsi_strategies']][i], padding=PADDING, interval=INTERVAL)\n",
|
||||
"# result_quantile_model = evaluate_strategy(padded_window, [s[0] for s in best_strategies['quantile_model']][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_macd, result_rsi, result_quantile_model, result_gmadl_model)\n",
|
||||
"# results_plot(i+1, result_buyandhold, result_macd, result_rsi, result_quantile_model, result_gmadl_model)\n",
|
||||
" \n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"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)\n",
|
||||
"# macd_concat = evaluate_strategy(test_data, ConcatenatedStrategies(len(data_windows[0][1]), [s[0] for s in best_strategies['macd_strategies']], padding=PADDING), padding=PADDING, interval=INTERVAL)\n",
|
||||
"# rsi_concat = evaluate_strategy(test_data, ConcatenatedStrategies(len(data_windows[0][1]), [s[0] for s in best_strategies['rsi_strategies']], padding=PADDING), padding=PADDING, interval=INTERVAL)\n",
|
||||
"# quantile_model_concat = evaluate_strategy(test_data, ConcatenatedStrategies(len(data_windows[0][1]), [s[0] for s in best_strategies['quantile_model']], padding=PADDING), 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",
|
||||
"# results_table(buy_and_hold_concat, macd_concat, rsi_concat, quantile_model_concat, gmadl_model_concat)\n",
|
||||
"# results_plot(0, buy_and_hold_concat, macd_concat, rsi_concat, quantile_model_concat, gmadl_model_concat, width=1200, notitle=True)\n",
|
||||
"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"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
|
||||
}
|
||||
File diff suppressed because it is too large
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546977
notebooks/btcusdt_dataset_analysis.ipynb
Normal file
546977
notebooks/btcusdt_dataset_analysis.ipynb
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106789
notebooks/evaluate.ipynb
106789
notebooks/evaluate.ipynb
File diff suppressed because one or more lines are too long
100610
notebooks/gmadl.ipynb
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100610
notebooks/gmadl.ipynb
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535
notebooks/sensitivity_analysis.ipynb
Normal file
535
notebooks/sensitivity_analysis.ipynb
Normal file
@ -0,0 +1,535 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 11,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import plotly.figure_factory as ff\n",
|
||||
"import pandas as pd\n",
|
||||
"import pickle\n",
|
||||
"import plotly.graph_objs as go\n",
|
||||
"import latextable\n",
|
||||
"from texttable import Texttable\n",
|
||||
"from strategy.strategy import (\n",
|
||||
" BuyAndHoldStrategy,\n",
|
||||
" MACDStrategy,\n",
|
||||
" RSIStrategy,\n",
|
||||
" ModelQuantilePredictionsStrategy,\n",
|
||||
" ModelGmadlPredictionsStrategy,\n",
|
||||
" ConcatenatedStrategies\n",
|
||||
")\n",
|
||||
"from strategy.util import (\n",
|
||||
" get_data_windows,\n",
|
||||
" get_sweep_window_predictions,\n",
|
||||
" get_predictions_dataframe\n",
|
||||
")\n",
|
||||
"from strategy.evaluation import (\n",
|
||||
" parameter_sweep,\n",
|
||||
" evaluate_strategy\n",
|
||||
")\n",
|
||||
"from strategy.plotting import (\n",
|
||||
" plot_sweep_results\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"PADDING=5000\n",
|
||||
"VALID_PART=0.2"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 48,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"\u001b[34m\u001b[1mwandb\u001b[0m: Downloading large artifact btc-usdt-5m:latest, 745.12MB. 12 files... \n",
|
||||
"\u001b[34m\u001b[1mwandb\u001b[0m: 12 of 12 files downloaded. \n",
|
||||
"Done. 0:0:0.9\n",
|
||||
"\u001b[34m\u001b[1mwandb\u001b[0m: Downloading large artifact btc-usdt-15m:latest, 248.65MB. 12 files... \n",
|
||||
"\u001b[34m\u001b[1mwandb\u001b[0m: 12 of 12 files downloaded. \n",
|
||||
"Done. 0:0:0.7\n",
|
||||
"\u001b[34m\u001b[1mwandb\u001b[0m: Downloading large artifact btc-usdt-30m:latest, 124.19MB. 12 files... \n",
|
||||
"\u001b[34m\u001b[1mwandb\u001b[0m: 12 of 12 files downloaded. \n",
|
||||
"Done. 0:0:0.6\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"data_windows_5min = get_data_windows(\n",
|
||||
" 'wne-masters-thesis-testing',\n",
|
||||
" 'btc-usdt-5m:latest',\n",
|
||||
" min_window=0, \n",
|
||||
" max_window=5\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"data_windows_15min = get_data_windows(\n",
|
||||
" 'wne-masters-thesis-testing',\n",
|
||||
" 'btc-usdt-15m:latest',\n",
|
||||
" min_window=0, \n",
|
||||
" max_window=5\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"data_windows_30min = get_data_windows(\n",
|
||||
" 'wne-masters-thesis-testing',\n",
|
||||
" 'btc-usdt-30m:latest',\n",
|
||||
" min_window=0, \n",
|
||||
" max_window=5\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 49,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"with open('cache/5min-best-strategies.pkl', 'rb') as inpt:\n",
|
||||
" best_strategies_5min = pickle.load(inpt)\n",
|
||||
"\n",
|
||||
"with open('cache/15min-best-strategies.pkl', 'rb') as inpt:\n",
|
||||
" best_strategies_15min = pickle.load(inpt)\n",
|
||||
"\n",
|
||||
"with open('cache/30min-best-strategies.pkl', 'rb') as inpt:\n",
|
||||
" best_strategies_30min = pickle.load(inpt)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Best 5 strategies evaluation"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 23,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def results_plot(buy_and_hold_concat, gmadl_5min_model_concat, rsi_5_min_concat, gmadl_15min_model_concat, rsi_30min_concat, macd_30min_concat, width=850, height=500, notitle=False):\n",
|
||||
"\n",
|
||||
" fig = go.Figure([\n",
|
||||
" go.Scatter(y=buy_and_hold_concat['portfolio_value'], x=buy_and_hold_concat['time'], name=\"Buy and Hold\"),\n",
|
||||
" go.Scatter(y=gmadl_5min_model_concat['portfolio_value'], x=gmadl_5min_model_concat['time'], name=\"GMADL Informer Strategy (5min)\"),\n",
|
||||
" go.Scatter(y=rsi_5_min_concat['portfolio_value'], x=rsi_5_min_concat['time'], name=\"RSI Strategy (5min)\"),\n",
|
||||
" go.Scatter(y=gmadl_15min_model_concat['portfolio_value'], x=gmadl_15min_model_concat['time'], name='GNADL Informer Strategy (15min)'),\n",
|
||||
" go.Scatter(y=rsi_30min_concat['portfolio_value'], x=rsi_30min_concat['time'], name='RSI Strategy (30min)'),\n",
|
||||
" go.Scatter(y=macd_30min_concat['portfolio_value'], x=macd_30min_concat['time'], name='MACD Strategy (30min)')\n",
|
||||
" ])\n",
|
||||
" fig.update_layout(\n",
|
||||
" # title={\n",
|
||||
" # 'text': f\"W{idx}-{INTERVAL}\",\n",
|
||||
" # 'y':0.97,\n",
|
||||
" # 'x':0.5,\n",
|
||||
" # 'xanchor': 'center',\n",
|
||||
" # 'yanchor': 'top'} if not notitle else None,\n",
|
||||
" yaxis_title=\"Portfolio Value\",\n",
|
||||
" xaxis_title=\"Date\",\n",
|
||||
" font=dict(\n",
|
||||
" # family=\"Courier New, monospace\",\n",
|
||||
" size=14,\n",
|
||||
" ),\n",
|
||||
" autosize=False,\n",
|
||||
" width=width,\n",
|
||||
" height=height,\n",
|
||||
" margin=dict(l=20, r=20, t=20, b=20),\n",
|
||||
" plot_bgcolor='white',\n",
|
||||
" legend=dict(\n",
|
||||
" orientation=\"h\",\n",
|
||||
" yanchor=\"bottom\",\n",
|
||||
" y=1.02,\n",
|
||||
" xanchor=\"left\",\n",
|
||||
" x=0.02\n",
|
||||
" )\n",
|
||||
" )\n",
|
||||
" fig.update_xaxes(\n",
|
||||
" mirror=True,\n",
|
||||
" ticks='outside',\n",
|
||||
" showline=True,\n",
|
||||
" linecolor='black',\n",
|
||||
" gridcolor='lightgrey'\n",
|
||||
" )\n",
|
||||
" fig.update_yaxes(\n",
|
||||
" mirror=True,\n",
|
||||
" ticks='outside',\n",
|
||||
" showline=True,\n",
|
||||
" linecolor='black',\n",
|
||||
" gridcolor='lightgrey'\n",
|
||||
" )\n",
|
||||
" # fig.write_image(f\"images/eval-w{idx}-{INTERVAL}.png\")\n",
|
||||
" fig.show()\n",
|
||||
" \n",
|
||||
"def results_table(buy_and_hold_concat, gmadl_5min_model_concat, rsi_5_min_concat, gmadl_15min_model_concat, rsi_30min_concat, macd_30min_concat):\n",
|
||||
" table_eval_windows = Texttable()\n",
|
||||
" table_eval_windows.set_deco(Texttable.HEADER)\n",
|
||||
" table_eval_windows.set_cols_align([\"l\", \"c\",\"c\", \"c\", \"c\", \"c\", \"c\", \"c\", \"c\", \"c\"])\n",
|
||||
" table_eval_windows.set_precision(3)\n",
|
||||
"\n",
|
||||
" table_eval_windows.header([\n",
|
||||
" \"\\\\textbf{Strategy}\",\n",
|
||||
" \"\\\\textbf{VAL}\",\n",
|
||||
" \"\\\\textbf{ARC}\",\n",
|
||||
" \"\\\\textbf{ASD}\",\n",
|
||||
" \"\\\\textbf{IR*}\",\n",
|
||||
" \"\\\\textbf{MD}\",\n",
|
||||
" \"\\\\textbf{IR**}\",\n",
|
||||
" \"\\\\textbf{N}\",\n",
|
||||
" \"\\\\textbf{LONG}\",\n",
|
||||
" \"\\\\textbf{SHORT}\",\n",
|
||||
" ])\n",
|
||||
"\n",
|
||||
" strategy_name_result = [\n",
|
||||
" ('Buy and Hold', buy_and_hold_concat),\n",
|
||||
" ('GMADL Informer (5min)', gmadl_5min_model_concat),\n",
|
||||
" ('RSI Strategy (5min)', rsi_5_min_concat),\n",
|
||||
" ('GMADL Informer (15min)', gmadl_15min_model_concat),\n",
|
||||
" ('RSI Strategy (30min)', rsi_30min_concat),\n",
|
||||
" ('MACD Strategy (30min)', macd_30min_concat)\n",
|
||||
" ]\n",
|
||||
" for strategy_name, result in strategy_name_result:\n",
|
||||
" table_eval_windows.add_row([\n",
|
||||
" strategy_name,\n",
|
||||
" result['value'],\n",
|
||||
" result['arc'],\n",
|
||||
" result['asd'],\n",
|
||||
" result['ir'],\n",
|
||||
" result['md'],\n",
|
||||
" result['mod_ir'],\n",
|
||||
" result['n_trades'],\n",
|
||||
" f\"{result['long_pos']*100:.2f}\\%\",\n",
|
||||
" f\"{result['short_pos']*100:.2f}\\%\",\n",
|
||||
" ])\n",
|
||||
" print(latextable.draw_latex(table_eval_windows))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# test_data_5min = pd.concat([data_windows_5min[0][0][-PADDING:]] + [data_window[1] for data_window in data_windows_5min])\n",
|
||||
"# test_data_15min = pd.concat([data_windows_15min[0][0][-PADDING:]] + [data_window[1] for data_window in data_windows_15min])\n",
|
||||
"# test_data_30min = pd.concat([data_windows_30min[0][0][-PADDING:]] + [data_window[1] for data_window in data_windows_30min])\n",
|
||||
"\n",
|
||||
"# buy_and_hold_concat = evaluate_strategy(test_data_5min, BuyAndHoldStrategy(), padding=PADDING, interval='5min')\n",
|
||||
"# gmadl_5min_model_concat = evaluate_strategy(test_data_5min, ConcatenatedStrategies(len(data_windows_5min[0][1]), [s[0] for s in best_strategies_5min['gmadl_model']], padding=PADDING), padding=PADDING, interval='5min')\n",
|
||||
"# gmadl_15min_model_concat = evaluate_strategy(test_data_15min, ConcatenatedStrategies(len(data_windows_15min[0][1]), [s[0] for s in best_strategies_15min['gmadl_model']], padding=PADDING), padding=PADDING, interval='15min')\n",
|
||||
"# rsi_5_min_concat = evaluate_strategy(test_data_5min, ConcatenatedStrategies(len(data_windows_5min[0][1]), [s[0] for s in best_strategies_5min['rsi_strategies']], padding=PADDING), padding=PADDING, interval='5min')\n",
|
||||
"# rsi_30min_concat = evaluate_strategy(test_data_30min, ConcatenatedStrategies(len(data_windows_30min[0][1]), [s[0] for s in best_strategies_30min['rsi_strategies']], padding=PADDING), padding=PADDING, interval='30min')\n",
|
||||
"# macd_30min_concat = evaluate_strategy(test_data_30min, ConcatenatedStrategies(len(data_windows_30min[0][1]), [s[0] for s in best_strategies_30min['macd_strategies']], padding=PADDING), padding=PADDING, interval='30min')\n",
|
||||
"\n",
|
||||
"# results_table(buy_and_hold_concat, gmadl_5min_model_concat, rsi_5_min_concat, gmadl_15min_model_concat, rsi_30min_concat, macd_30min_concat)\n",
|
||||
"# results_plot(buy_and_hold_concat, gmadl_5min_model_concat, gmadl_15min_model_concat, rsi_5_min_concat, rsi_30min_concat, macd_30min_concat, width=1200, notitle=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## TOP N Strategies Sensistivity Analisys"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def results_for_strats(data_windows, best_strategies, interval='5min', 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)\n",
|
||||
" \n",
|
||||
" macd_concat = [evaluate_strategy(test_data, ConcatenatedStrategies(len(data_windows[0][1]), [s[x] for s in best_strategies['macd_strategies']], padding=PADDING), padding=PADDING, interval=interval) for x in range(top_n)]\n",
|
||||
" rsi_concat = [evaluate_strategy(test_data, ConcatenatedStrategies(len(data_windows[0][1]), [s[x] for s in best_strategies['rsi_strategies']], padding=PADDING), padding=PADDING, interval=interval) for x in range(top_n)]\n",
|
||||
" quantile_model_concat = [evaluate_strategy(test_data, ConcatenatedStrategies(len(data_windows[0][1]), [s[x] for s in best_strategies['quantile_model']], padding=PADDING), padding=PADDING, interval=interval) for x in range(top_n)]\n",
|
||||
" gmadl_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=interval) 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 macd_concat])),\n",
|
||||
" list(reversed([round(x['mod_ir'], 3) for x in rsi_concat])),\n",
|
||||
" list(reversed([round(x['mod_ir'], 3) for x in quantile_model_concat])),\n",
|
||||
" list(reversed([round(x['mod_ir'], 3) for x in gmadl_model_concat])),\n",
|
||||
" ]))\n",
|
||||
" x = list(reversed(range(1, top_n+1)))\n",
|
||||
" y = list(reversed([\n",
|
||||
" \"Buy and Hold\",\n",
|
||||
" \"MACD Strategy\",\n",
|
||||
" \"RSI Strategy\",\n",
|
||||
" \"Quantile Informer\",\n",
|
||||
" \"Gmadl Informer\"\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=450,\n",
|
||||
" font=dict(\n",
|
||||
" # family=\"Courier New, monospace\",\n",
|
||||
" size=18, # Set the font size here\n",
|
||||
" # color=\"RebeccaPurple\"\n",
|
||||
" )\n",
|
||||
" )\n",
|
||||
" fig.show()\n",
|
||||
"\n",
|
||||
"# results_for_strats(data_windows_5min, best_strategies_5min, interval='5min', top_n=10) \n",
|
||||
"# results_for_strats(data_windows_15min, best_strategies_15min, interval='15min', top_n=10) \n",
|
||||
"# results_for_strats(data_windows_30min, best_strategies_30min, interval='30min', top_n=10) \n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Validation size for: GMADL Informer (5min)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 27,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"with open('cache/5min-best-strategies-01.pkl', 'rb') as inpt:\n",
|
||||
" best_strategies_5min_01 = pickle.load(inpt)\n",
|
||||
"\n",
|
||||
"with open('cache/5min-best-strategies.pkl', 'rb') as inpt:\n",
|
||||
" best_strategies_5min_02 = pickle.load(inpt)\n",
|
||||
"\n",
|
||||
"with open('cache/5min-best-strategies-03.pkl', 'rb') as inpt:\n",
|
||||
" best_strategies_5min_03 = pickle.load(inpt)\n",
|
||||
"\n",
|
||||
"with open('cache/5min-best-strategies-04.pkl', 'rb') as inpt:\n",
|
||||
" best_strategies_5min_04 = pickle.load(inpt)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"{'strategy_name': 'Unknown model', 'future': 1, 'target': 'close_price', 'enter_long': 0.004, 'exit_long': None, 'enter_short': -0.005, 'exit_short': None}\n",
|
||||
"{'strategy_name': 'Unknown model', 'future': 1, 'target': 'close_price', 'enter_long': 0.007, 'exit_long': -0.003, 'enter_short': None, 'exit_short': None}\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# print(best_strategies_5min_02['gmadl_model'][0][0].info())\n",
|
||||
"# print(best_strategies_5min_03['gmadl_model'][0][0].info())"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 37,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def results_plot2(result_buyandhold, results, names, width=850, height=500):\n",
|
||||
"\n",
|
||||
" fig = go.Figure([\n",
|
||||
" go.Scatter(y=result_buyandhold['portfolio_value'], x=result_buyandhold['time'], name=\"Buy and Hold\")] +[\n",
|
||||
" go.Scatter(y=result['portfolio_value'], x=result['time'], name=name) for name, result in zip(names, results)\n",
|
||||
" ])\n",
|
||||
" # go.Scatter(y=result_macd['portfolio_value'], x=result_macd['time'], name=\"MACD Strategy\"),\n",
|
||||
" # go.Scatter(y=result_rsi['portfolio_value'], x=result_rsi['time'], name=\"RSI Strategy\"),\n",
|
||||
" # go.Scatter(y=result_quantile_model['portfolio_value'], x=result_quantile_model['time'], name='Quantile Informer Strategy'),\n",
|
||||
" # go.Scatter(y=result_gmadl_model['portfolio_value'], x=result_gmadl_model['time'], name='GMADL Informer Strategy')\n",
|
||||
" # ])\n",
|
||||
" fig.update_layout(\n",
|
||||
" yaxis_title=\"Portfolio Value\",\n",
|
||||
" xaxis_title=\"Date\",\n",
|
||||
" font=dict(\n",
|
||||
" # family=\"Courier New, monospace\",\n",
|
||||
" size=14,\n",
|
||||
" ),\n",
|
||||
" autosize=False,\n",
|
||||
" width=width,\n",
|
||||
" height=height,\n",
|
||||
" margin=dict(l=20, r=20, t=20, b=20),\n",
|
||||
" plot_bgcolor='white',\n",
|
||||
" legend=dict(\n",
|
||||
" orientation=\"h\",\n",
|
||||
" yanchor=\"bottom\",\n",
|
||||
" y=1.02,\n",
|
||||
" xanchor=\"left\",\n",
|
||||
" x=0.02\n",
|
||||
" )\n",
|
||||
" )\n",
|
||||
" fig.update_xaxes(\n",
|
||||
" mirror=True,\n",
|
||||
" ticks='outside',\n",
|
||||
" showline=True,\n",
|
||||
" linecolor='black',\n",
|
||||
" gridcolor='lightgrey'\n",
|
||||
" )\n",
|
||||
" fig.update_yaxes(\n",
|
||||
" mirror=True,\n",
|
||||
" ticks='outside',\n",
|
||||
" showline=True,\n",
|
||||
" linecolor='black',\n",
|
||||
" gridcolor='lightgrey'\n",
|
||||
" )\n",
|
||||
" fig.show()\n",
|
||||
" \n",
|
||||
"def results_table2(result_buyandhold, results, names):\n",
|
||||
" table_eval_windows = Texttable()\n",
|
||||
" table_eval_windows.set_deco(Texttable.HEADER)\n",
|
||||
" table_eval_windows.set_cols_align([\"l\", \"c\",\"c\", \"c\", \"c\", \"c\", \"c\", \"c\", \"c\", \"c\"])\n",
|
||||
" table_eval_windows.set_precision(3)\n",
|
||||
"\n",
|
||||
" table_eval_windows.header([\n",
|
||||
" \"\\\\textbf{Strategy}\",\n",
|
||||
" \"\\\\textbf{VAL}\",\n",
|
||||
" \"\\\\textbf{ARC}\",\n",
|
||||
" \"\\\\textbf{ASD}\",\n",
|
||||
" \"\\\\textbf{IR*}\",\n",
|
||||
" \"\\\\textbf{MD}\",\n",
|
||||
" \"\\\\textbf{IR**}\",\n",
|
||||
" \"\\\\textbf{N}\",\n",
|
||||
" \"\\\\textbf{LONG}\",\n",
|
||||
" \"\\\\textbf{SHORT}\",\n",
|
||||
" ])\n",
|
||||
"\n",
|
||||
" strategy_name_result = [\n",
|
||||
" ('Buy and Hold', result_buyandhold)] + list(zip(names, results))\n",
|
||||
" for strategy_name, result in strategy_name_result:\n",
|
||||
" table_eval_windows.add_row([\n",
|
||||
" strategy_name,\n",
|
||||
" result['value'],\n",
|
||||
" result['arc'],\n",
|
||||
" result['asd'],\n",
|
||||
" result['ir'],\n",
|
||||
" result['md'],\n",
|
||||
" result['mod_ir'],\n",
|
||||
" result['n_trades'],\n",
|
||||
" f\"{result['long_pos']*100:.2f}\\%\",\n",
|
||||
" f\"{result['short_pos']*100:.2f}\\%\",\n",
|
||||
" ])\n",
|
||||
" print(latextable.draw_latex(table_eval_windows))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 46,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def results_for_strats(data_windows, best_strategies, names, interval='5min', strat_name='gmadl_model'):\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=interval)\n",
|
||||
" \n",
|
||||
" evaluation_results = []\n",
|
||||
" for best_strat in best_strategies:\n",
|
||||
" evaluation_results.append(\n",
|
||||
" evaluate_strategy(test_data, ConcatenatedStrategies(len(data_windows[0][1]), [s[0] for s in best_strat[strat_name]], padding=PADDING), padding=PADDING, interval=interval)\n",
|
||||
" )\n",
|
||||
" results_plot(buy_and_hold_concat, evaluation_results, names, width=1200)\n",
|
||||
" results_table2(buy_and_hold_concat, evaluation_results, names)\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# results_for_strats(data_windows_5min, [\n",
|
||||
"# best_strategies_5min_01,\n",
|
||||
"# best_strategies_5min_02,\n",
|
||||
"# best_strategies_5min_03,\n",
|
||||
"# best_strategies_5min_04\n",
|
||||
"# ], [\n",
|
||||
"# \"3 months\",\n",
|
||||
"# \"6 months\",\n",
|
||||
"# \"9 months\",\n",
|
||||
"# \"12 months\",\n",
|
||||
"# ])\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 40,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"with open('cache/30min-best-strategies-01.pkl', 'rb') as inpt:\n",
|
||||
" best_strategies_30min_01 = pickle.load(inpt)\n",
|
||||
"\n",
|
||||
"with open('cache/30min-best-strategies.pkl', 'rb') as inpt:\n",
|
||||
" best_strategies_30min_02 = pickle.load(inpt)\n",
|
||||
"\n",
|
||||
"with open('cache/30min-best-strategies-03.pkl', 'rb') as inpt:\n",
|
||||
" best_strategies_30min_03 = pickle.load(inpt)\n",
|
||||
"\n",
|
||||
"with open('cache/30min-best-strategies-04.pkl', 'rb') as inpt:\n",
|
||||
" best_strategies_30min_04 = pickle.load(inpt)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# results_for_strats(data_windows_30min, [\n",
|
||||
"# best_strategies_30min_01,\n",
|
||||
"# best_strategies_30min_02,\n",
|
||||
"# best_strategies_30min_03,\n",
|
||||
"# best_strategies_30min_04\n",
|
||||
"# ], [\n",
|
||||
"# \"3 months\",\n",
|
||||
"# \"6 months\",\n",
|
||||
"# \"9 months\",\n",
|
||||
"# \"12 months\",\n",
|
||||
"# ], strat_name='rsi_strategies', interval='30min')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# test_data = pd.concat([data_window[0] for data_window in data_windows_5min[:1]])\n",
|
||||
"# buy_and_hold_concat = evaluate_strategy(test_data, BuyAndHoldStrategy(), padding=PADDING)\n",
|
||||
"\n",
|
||||
"# res = evaluate_strategy(test_data, ConcatenatedStrategies(1, [s[0] for s in best_strategies_5min_02['gmadl_model'][:1]], padding=PADDING), padding=PADDING, interval='5min')\n",
|
||||
"# results_plot(buy_and_hold_concat, [res], width=1200)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"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
|
||||
}
|
||||
Loading…
x
Reference in New Issue
Block a user