{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "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=0.2\n",
    "INTERVAL='min'\n",
    "METRIC='mod_ir'\n",
    "TOP_N=10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "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",
      "\u001b[34m\u001b[1mwandb\u001b[0m: Downloading large artifact btc-usdt-1m:latest, 3717.80MB. 12 files... \n",
      "\u001b[34m\u001b[1mwandb\u001b[0m:   12 of 12 files downloaded.  \n",
      "Done. 0:0:4.7\n"
     ]
    }
   ],
   "source": [
    "data_windows = get_data_windows(\n",
    "    'wne-masters-thesis-testing',\n",
    "    'btc-usdt-1m:latest',\n",
    "    min_window=0, \n",
    "    max_window=5\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "buyandhold_best_strategies = [BuyAndHoldStrategy() for _ in data_windows] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\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"
     ]
    }
   ],
   "source": [
    "# Model with gmadl loss\n",
    "SWEEP_ID = 'filipstefaniuk/wne-masters-thesis-testing/s8goxcbz'\n",
    "# SWEEP_ID = 'filipstefaniuk/wne-masters-thesis-testing/v3epl3qk'\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": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# y = test_gmadl_pred_windows[0][2][:, 0, 0]\n",
    "# fig = go.Figure([\n",
    "#         go.Scatter(y=y[::100]),\n",
    "# ])\n",
    "# fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1176/1176 [04:40<00:00,  4.20it/s]\n",
      "100%|██████████| 1176/1176 [04:40<00:00,  4.20it/s]\n",
      "100%|██████████| 1176/1176 [04:36<00:00,  4.26it/s]\n",
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      "100%|██████████| 1176/1176 [04:30<00:00,  4.35it/s]\n"
     ]
    }
   ],
   "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, _), valid_preds, test_preds in zip(data_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": 3,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'buyandhold_best_strategies' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[3], line 3\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# Persist best strategies, so that they don't have to be recomputed every time\u001b[39;00m\n\u001b[1;32m      2\u001b[0m best_strategies \u001b[38;5;241m=\u001b[39m {\n\u001b[0;32m----> 3\u001b[0m     \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbuy_and_hold\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[43mbuyandhold_best_strategies\u001b[49m,\n\u001b[1;32m      4\u001b[0m     \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mgmadl_model\u001b[39m\u001b[38;5;124m'\u001b[39m: gmadl_model_best_strategies\n\u001b[1;32m      5\u001b[0m }\n\u001b[1;32m      7\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mopen\u001b[39m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcache/1min-best-strategies-v1.pkl\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mwb\u001b[39m\u001b[38;5;124m'\u001b[39m) \u001b[38;5;28;01mas\u001b[39;00m outp:\n\u001b[1;32m      8\u001b[0m     pickle\u001b[38;5;241m.\u001b[39mdump(best_strategies, outp, pickle\u001b[38;5;241m.\u001b[39mHIGHEST_PROTOCOL)\n",
      "\u001b[0;31mNameError\u001b[0m: name 'buyandhold_best_strategies' is not defined"
     ]
    }
   ],
   "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",
    "    'gmadl_model': gmadl_model_best_strategies\n",
    "}\n",
    "\n",
    "with open('cache/1min-best-strategies-v1.pkl', 'wb') as outp:\n",
    "    pickle.dump(best_strategies, outp, pickle.HIGHEST_PROTOCOL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('cache/1min-best-strategies-v1.pkl', 'rb') as inpt:\n",
    "    best_strategies = pickle.load(inpt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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, objective='mod_ir')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def results_plot(idx, result_buyandhold, result_gmadl_model, width=850, height=500, notitle=False, v_lines=None):\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_gmadl_model['portfolio_value'], x=result_gmadl_model['time'], name='GMADL Informer Strategy')\n",
    "        ])\n",
    "    \n",
    "    if v_lines:\n",
    "        for v_line in v_lines:\n",
    "            fig.add_shape(\n",
    "                go.layout.Shape(type=\"line\",\n",
    "                                yref=\"paper\",\n",
    "                                xref=\"x\",\n",
    "                                x0=v_line,\n",
    "                                x1=v_line,\n",
    "                                y0=0,\n",
    "                                y1=1,\n",
    "                                line=dict(dash='dash', color='rgb(140,140,140)')))\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_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",
    "        ('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",
    "            f\"{result['arc']*100:.2f}\\%\",\n",
    "            f\"{result['asd']*100:.2f}\\%\",\n",
    "            result['ir'],\n",
    "            f\"{result['md']*100:.2f}\\%\",\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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\begin{table}\n",
      "\t\\begin{center}\n",
      "\t\t\\begin{tabular}{lccccccccc}\n",
      "\t\t\t\\textbf{Strategy} & \\textbf{VAL} & \\textbf{ARC} & \\textbf{ASD} & \\textbf{IR*} & \\textbf{MD} & \\textbf{IR**} & \\textbf{N} & \\textbf{LONG} & \\textbf{SHORT} \\\\\n",
      "\t\t\t\\hline\n",
      "\t\t\tBuy and Hold & 0.929 & -13.87\\% & 69.66\\% & -0.199 & 52.09\\% & -0.053 & 2 & 100.00\\% & 0.00\\% \\\\\n",
      "\t\t\tGMADL Informer & 1.306 & 71.83\\% & 69.69\\% & 1.031 & 41.57\\% & 1.781 & 50 & 7.29\\% & 92.71\\% \\\\\n",
      "\t\t\\end{tabular}\n",
      "\t\\end{center}\n",
      "\\end{table}\n",
      "\\begin{table}\n",
      "\t\\begin{center}\n",
      "\t\t\\begin{tabular}{lccccccccc}\n",
      "\t\t\t\\textbf{Strategy} & \\textbf{VAL} & \\textbf{ARC} & \\textbf{ASD} & \\textbf{IR*} & \\textbf{MD} & \\textbf{IR**} & \\textbf{N} & \\textbf{LONG} & \\textbf{SHORT} \\\\\n",
      "\t\t\t\\hline\n",
      "\t\t\tBuy and Hold & 0.549 & -70.35\\% & 73.36\\% & -0.959 & 63.40\\% & -1.064 & 2 & 100.00\\% & 0.00\\% \\\\\n",
      "\t\t\tGMADL Informer & 1.837 & 243.15\\% & 73.38\\% & 3.314 & 25.16\\% & 32.024 & 186 & 18.19\\% & 81.81\\% \\\\\n",
      "\t\t\\end{tabular}\n",
      "\t\\end{center}\n",
      "\\end{table}\n",
      "\\begin{table}\n",
      "\t\\begin{center}\n",
      "\t\t\\begin{tabular}{lccccccccc}\n",
      "\t\t\t\\textbf{Strategy} & \\textbf{VAL} & \\textbf{ARC} & \\textbf{ASD} & \\textbf{IR*} & \\textbf{MD} & \\textbf{IR**} & \\textbf{N} & \\textbf{LONG} & \\textbf{SHORT} \\\\\n",
      "\t\t\t\\hline\n",
      "\t\t\tBuy and Hold & 1.016 & 3.33\\% & 52.45\\% & 0.064 & 38.42\\% & 0.006 & 2 & 100.00\\% & 0.00\\% \\\\\n",
      "\t\t\tGMADL Informer & 0.739 & -45.82\\% & 52.21\\% & -0.878 & 42.46\\% & -0.947 & 35 & 4.70\\% & 93.05\\% \\\\\n",
      "\t\t\\end{tabular}\n",
      "\t\\end{center}\n",
      "\\end{table}\n",
      "\\begin{table}\n",
      "\t\\begin{center}\n",
      "\t\t\\begin{tabular}{lccccccccc}\n",
      "\t\t\t\\textbf{Strategy} & \\textbf{VAL} & \\textbf{ARC} & \\textbf{ASD} & \\textbf{IR*} & \\textbf{MD} & \\textbf{IR**} & \\textbf{N} & \\textbf{LONG} & \\textbf{SHORT} \\\\\n",
      "\t\t\t\\hline\n",
      "\t\t\tBuy and Hold & 1.230 & 52.29\\% & 44.30\\% & 1.180 & 22.35\\% & 2.761 & 2 & 100.00\\% & 0.00\\% \\\\\n",
      "\t\t\tGMADL Informer & 1.086 & 18.12\\% & 40.58\\% & 0.446 & 26.30\\% & 0.308 & 11 & 60.03\\% & 23.82\\% \\\\\n",
      "\t\t\\end{tabular}\n",
      "\t\\end{center}\n",
      "\\end{table}\n",
      "\\begin{table}\n",
      "\t\\begin{center}\n",
      "\t\t\\begin{tabular}{lccccccccc}\n",
      "\t\t\t\\textbf{Strategy} & \\textbf{VAL} & \\textbf{ARC} & \\textbf{ASD} & \\textbf{IR*} & \\textbf{MD} & \\textbf{IR**} & \\textbf{N} & \\textbf{LONG} & \\textbf{SHORT} \\\\\n",
      "\t\t\t\\hline\n",
      "\t\t\tBuy and Hold & 1.439 & 109.31\\% & 43.75\\% & 2.498 & 21.12\\% & 12.930 & 2 & 100.00\\% & 0.00\\% \\\\\n",
      "\t\t\tGMADL Informer & 1.010 & 1.98\\% & 43.47\\% & 0.046 & 31.96\\% & 0.003 & 67 & 80.24\\% & 15.37\\% \\\\\n",
      "\t\t\\end{tabular}\n",
      "\t\\end{center}\n",
      "\\end{table}\n",
      "\\begin{table}\n",
      "\t\\begin{center}\n",
      "\t\t\\begin{tabular}{lccccccccc}\n",
      "\t\t\t\\textbf{Strategy} & \\textbf{VAL} & \\textbf{ARC} & \\textbf{ASD} & \\textbf{IR*} & \\textbf{MD} & \\textbf{IR**} & \\textbf{N} & \\textbf{LONG} & \\textbf{SHORT} \\\\\n",
      "\t\t\t\\hline\n",
      "\t\t\tBuy and Hold & 1.561 & 146.58\\% & 53.72\\% & 2.729 & 27.11\\% & 14.756 & 2 & 100.00\\% & 0.00\\% \\\\\n",
      "\t\t\tGMADL Informer & 1.178 & 39.32\\% & 43.06\\% & 0.913 & 18.63\\% & 1.927 & 92 & 54.86\\% & 0.00\\% \\\\\n",
      "\t\t\\end{tabular}\n",
      "\t\\end{center}\n",
      "\\end{table}\n"
     ]
    }
   ],
   "source": [
    "for i, (in_sample, out_of_sample) in enumerate(data_windows):\n",
    "    padded_window = pd.concat([in_sample.iloc[-PADDING:], out_of_sample])\n",
    "    result_buyandhold = evaluate_strategy(padded_window, best_strategies['buy_and_hold'][i], padding=PADDING, interval=INTERVAL)\n",
    "    result_gmadl_model = evaluate_strategy(padded_window, [s[0] for s in best_strategies['gmadl_model']][i], padding=PADDING, interval=INTERVAL)\n",
    "\n",
    "    results_table(result_buyandhold,  result_gmadl_model)\n",
    "    # results_plot(i+1, result_buyandhold, result_gmadl_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# test_data = pd.concat([data_windows[0][0][-PADDING:]] + [data_window[1] for data_window in data_windows])\n",
    "# buy_and_hold_concat = evaluate_strategy(test_data, BuyAndHoldStrategy(), padding=PADDING, interval=INTERVAL)\n",
    "# gmadl_model_concat = evaluate_strategy(test_data, ConcatenatedStrategies(len(data_windows[0][1]), [s[0] for s in best_strategies['gmadl_model']], padding=PADDING), padding=PADDING, interval=INTERVAL)\n",
    "\n",
    "# v_lines=[data_window[1]['close_time'].iloc[-1] for data_window in data_windows][:-1]\n",
    "# results_table(buy_and_hold_concat, gmadl_model_concat)\n",
    "# results_plot(0, buy_and_hold_concat, gmadl_model_concat, width=1300, height=500, notitle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import plotly.figure_factory as ff\n",
    "\n",
    "def results_for_strats(\n",
    "        data_windows, \n",
    "        best_strategies,\n",
    "        top_n=10):\n",
    "    test_data = pd.concat([data_windows[0][0][-PADDING:]] + [data_window[1] for data_window in data_windows])\n",
    "\n",
    "    buy_and_hold_concat = evaluate_strategy(test_data, BuyAndHoldStrategy(), padding=PADDING, interval='min')\n",
    "    gmadl_1min_model_concat = [evaluate_strategy(test_data, ConcatenatedStrategies(len(data_windows[0][1]), [s[x] for s in best_strategies['gmadl_model']], padding=PADDING), padding=PADDING, interval='min') for x in range(top_n)]\n",
    "\n",
    "    z = list(reversed([\n",
    "        list(reversed([round(buy_and_hold_concat['mod_ir'], 3)]*top_n)),\n",
    "        list(reversed([round(x['mod_ir'], 3) for x in gmadl_1min_model_concat])),\n",
    "    ]))\n",
    "    x = list(reversed(range(1, top_n+1)))\n",
    "    y = list(reversed([\n",
    "        \"Buy and Hold\",\n",
    "        \"Gmadl Informer (1 min)\"\n",
    "    ]))\n",
    "    # 'Portland'\n",
    "    fig = ff.create_annotated_heatmap(z, x=x, y=y, colorscale='thermal', zmid=buy_and_hold_concat['mod_ir'])\n",
    "    fig.update_layout(\n",
    "        margin=dict(l=20, r=20, b=20, t=20),\n",
    "        width=1100,\n",
    "        height=650,\n",
    "        font=dict(\n",
    "        #    family=\"Courier New, monospace\",\n",
    "            size=16,  # Set the font size here\n",
    "            # color=\"RebeccaPurple\"\n",
    "        )\n",
    "    )\n",
    "    fig.show()\n",
    "\n",
    "# results_for_strats(data_windows, best_strategies, top_n=10) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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