{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "4d5d02c8-7702-44c4-bc4b-419ed466d7b5",
   "metadata": {},
   "source": [
    "# Crypto Momentum and Mean Reversion Strategies\n",
    "## Brian Plotnik\n",
    "### October 2024\n",
    "\n",
    "This notebook implements momentum and mean reversion strategies on historical daily cryptocurrency price data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a9f2b48d-86cd-4ad6-8f3a-e165b00354be",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import statements\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from binance.client import Client as bnb_client\n",
    "from datetime import datetime\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import statsmodels.api as sm\n",
    "\n",
    "from skopt import gp_minimize\n",
    "from skopt.space import Integer\n",
    "from skopt.utils import use_named_args"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42a93680-8424-487f-984a-0cd29948216f",
   "metadata": {},
   "source": [
    "# Data Collection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c1ed894e-e568-418e-94de-03ba003eba4b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Pull price timeseries data from Binance\n",
    "def get_binance_px(symbol,freq,start_ts = '2017-01-01',end_ts='2024-10-01'):\n",
    "    data = client.get_historical_klines(symbol,freq,start_ts,end_ts)\n",
    "    columns = ['open_time','open','high','low','close','volume','close_time','quote_volume',\n",
    "    'num_trades','taker_base_volume','taker_quote_volume','ignore']\n",
    "\n",
    "    data = pd.DataFrame(data,columns = columns)\n",
    "    \n",
    "    # Convert from POSIX timestamp (number of millisecond since jan 1, 1970)\n",
    "    data['open_time'] = data['open_time'].map(lambda x: datetime.utcfromtimestamp(x/1000))\n",
    "    data['close_time'] = data['close_time'].map(lambda x: datetime.utcfromtimestamp(x/1000))\n",
    "    return data  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d1718f07-c7cc-49c8-84ea-f16dbc98d9f2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['BTCUSD4', 'ETHUSD4', 'XRPUSD', 'BCHUSD4', 'LTCUSD4', 'USDTUSD4', 'BTCUSDT', 'ETHUSDT', 'XRPUSDT', 'BCHUSDT', 'LTCUSDT', 'BNBUSD4', 'BNBUSDT', 'ETHBTC', 'XRPBTC', 'BNBBTC', 'LTCBTC', 'BCHBTC', 'ADAUSD4', 'BATUSD4', 'ETCUSD4', 'XLMUSD4', 'ZRXUSD4', 'ADAUSDT', 'BATUSDT', 'ETCUSDT', 'XLMUSDT', 'ZRXUSDT', 'LINKUSD4', 'RVNUSD4', 'DASHUSD4', 'ZECUSD4', 'ALGOUSD4', 'IOTAUSD4', 'BUSDUSD4', 'BTCBUSD', 'DOGEUSDT', 'WAVESUSD4', 'ATOMUSDT', 'ATOMUSD4', 'NEOUSDT', 'NEOUSD4', 'VETUSDT', 'QTUMUSDT', 'QTUMUSD4', 'NANOUSD', 'ICXUSD4', 'ENJUSD4', 'ONTUSD4', 'ONTUSDT', 'ZILUSD4', 'ZILBUSD', 'VETUSD4', 'BNBBUSD', 'XRPBUSD', 'ETHBUSD', 'ALGOBUSD', 'XTZUSD4', 'XTZBUSD', 'HBARUSD4', 'HBARBUSD', 'OMGUSD4', 'OMGBUSD', 'MATICUSD4', 'MATICBUSD', 'XTZBTC', 'ADABTC', 'REPBUSD', 'REPUSD', 'EOSBUSD', 'EOSUSD4', 'DOGEUSD4', 'KNCUSD4', 'KNCUSDT', 'VTHOUSDT', 'VTHOUSD4', 'USDCUSD4', 'COMPUSDT', 'COMPUSD4', 'MANAUSD4', 'HNTUSD4', 'HNTUSDT', 'MKRUSD4', 'MKRUSDT', 'DAIUSD4', 'ONEUSDT', 'ONEUSD4', 'BANDUSDT', 'BANDUSD4', 'STORJUSDT', 'STORJUSD4', 'BUSDUSDT', 'UNIUSD4', 'UNIUSDT', 'SOLUSD4', 'SOLUSDT', 'LINKBTC', 'VETBTC', 'UNIBTC', 'EGLDUSDT', 'EGLDUSD4', 'PAXGUSDT', 'PAXGUSD4', 'OXTUSDT', 'OXTUSD4', 'ZENUSDT', 'ZENUSD4', 'BTCUSDC', 'ONEBUSD', 'FILUSDT', 'FILUSD4', 'AAVEUSDT', 'AAVEUSD4', 'GRTUSDT', 'GRTUSD4', 'SUSHIUSD4', 'ANKRUSD4', 'AMPUSD', 'SHIBUSDT', 'SHIBBUSD', 'CRVUSDT', 'CRVUSD4', 'AXSUSDT', 'AXSUSD4', 'SOLBTC', 'AVAXUSDT', 'AVAXUSD4', 'CTSIUSDT', 'CTSIUSD4', 'DOTUSDT', 'DOTUSD4', 'YFIUSDT', 'YFIUSD4', '1INCHUSDT', '1INCHUSD4', 'FTMUSDT', 'FTMUSD4', 'USDCUSDT', 'ETHUSDC', 'USDCBUSD', 'MATICUSDT', 'MANAUSDT', 'MANABUSD', 'ALGOUSDT', 'ADABUSD', 'SOLBUSD', 'LINKUSDT', 'EOSUSDT', 'ZECUSDT', 'ENJUSDT', 'NEARUSDT', 'NEARBUSD', 'NEARUSD4', 'OMGUSDT', 'SUSHIUSDT', 'LRCUSDT', 'LRCUSD4', 'LRCBTC', 'KSHIBUSD4', 'LPTUSDT', 'LPTBUSD', 'LPTUSD4', 'POLYUSDT', 'POLYBUSD', 'POLYUSD', 'POLYBTC', 'MATICBTC', 'DOTBTC', 'NMRUSDT', 'NMRUSD4', 'SLPUSDT', 'SLPUSD4', 'ANTUSDT', 'ANTUSD4', 'XNOUSD4', 'CHZUSDT', 'CHZUSD4', 'OGNUSDT', 'OGNUSD4', 'GALAUSDT', 'GALAUSD4', 'TLMUSDT', 'TLMUSD4', 'SNXUSDT', 'SNXUSD4', 'AUDIOUSDT', 'AUDIOUSD4', 'ENSUSDT', 'MANABTC', 'ATOMBTC', 'AVAXBTC', 'WBTCBTC', 'REQUSDT', 'REQUSD4', 'APEUSDT', 'APEUSD4', 'FLUXUSDT', 'FLUXUSD4', 'TRXBTC', 'TRXBUSD', 'TRXUSDT', 'TRXUSD4', 'COTIUSDT', 'COTIUSD4', 'VOXELUSDT', 'VOXELUSD4', 'RLCUSDT', 'RLCUSD4', 'USTUSDT', 'USTUSD', 'BICOUSDT', 'BICOUSD4', 'API3USDT', 'API3USD4', 'ENSUSD4', 'BTCUST', 'BNTUSDT', 'BNTUSD4', 'IMXUSDT', 'IMXUSD4', 'SPELLUSDT', 'SPELLUSD4', 'JASMYUSDT', 'JASMYUSD4', 'FLOWUSDT', 'FLOWUSD4', 'GTCUSDT', 'GTCUSD4', 'THETAUSDT', 'THETAUSD4', 'TFUELUSDT', 'TFUELUSD4', 'OCEANUSDT', 'OCEANUSD4', 'LAZIOUSDT', 'LAZIOUSD4', 'SANTOSUSDT', 'SANTOSUSD4', 'ALPINEUSDT', 'ALPINEUSD4', 'PORTOUSDT', 'PORTOUSD4', 'RENUSDT', 'RENUSD4', 'CELRUSDT', 'CELRUSD4', 'SKLUSDT', 'SKLUSD4', 'VITEUSDT', 'VITEUSD4', 'WAXPUSDT', 'WAXPUSD4', 'LTOUSDT', 'LTOUSD4', 'FETUSDT', 'FETUSD4', 'BONDUSDT', 'BONDUSD4', 'LOKAUSDT', 'LOKAUSD4', 'ICPUSDT', 'ICPUSD4', 'TUSDT', 'TUSD4', 'OPUSDT', 'OPUSD4', 'ROSEUSDT', 'ROSEUSD4', 'CELOUSDT', 'CELOUSD4', 'KDAUSDT', 'KDAUSD4', 'KSMUSDT', 'KSMUSD4', 'ACHUSDT', 'ACHUSD4', 'DARUSDT', 'DARUSD4', 'RNDRUSDT', 'RNDRUSD4', 'SYSUSDT', 'SYSUSD4', 'RADUSDT', 'RADUSD4', 'ILVUSDT', 'ILVUSD4', 'LDOUSDT', 'LDOUSD4', 'RAREUSDT', 'RAREUSD4', 'LSKUSDT', 'LSKUSD4', 'DGBUSDT', 'DGBUSD4', 'REEFUSDT', 'REEFUSD4', 'SRMUSDT', 'SRMUSD', 'ALICEUSDT', 'ALICEUSD4', 'FORTHUSDT', 'FORTHUSD4', 'ASTRUSDT', 'ASTRUSD4', 'BTRSTUSDT', 'BTRSTUSD4', 'GALUSDT', 'GALUSD4', 'SANDUSDT', 'SANDUSD4', 'BALUSDT', 'BALUSD4', 'POLYXUSD4', 'GLMUSDT', 'GLMUSD4', 'CLVUSDT', 'CLVUSD4', 'TUSDUSDT', 'TUSDUSD4', 'QNTUSDT', 'QNTUSD4', 'STGUSDT', 'STGUSD4', 'AXLUSDT', 'AXLUSD4', 'KAVAUSDT', 'KAVAUSD4', 'APTUSDT', 'APTUSD4', 'MASKUSDT', 'MASKUSD4', 'BOSONUSDT', 'BOSONUSD4', 'PONDUSDT', 'PONDUSD4', 'SOLUSDC', 'ADAUSDC', 'MXCUSDT', 'MXCUSD4', 'JAMUSDT', 'JAMUSD4', 'TRACUSDT', 'PROMUSDT', 'PROMUSD4', 'DIAUSDT', 'DIAUSD4', 'BTCDAI', 'ETHDAI', 'ADAETH', 'DOGEBTC', 'LOOMUSDT', 'LOOMUSD4', 'STMXUSDT', 'BTCUSD', 'ETHUSD', 'BCHUSD', 'LTCUSD', 'USDTUSD', 'BNBUSD', 'ADAUSD', 'BATUSD', 'ETCUSD', 'XLMUSD', 'ZRXUSD', 'LINKUSD', 'RVNUSD', 'DASHUSD', 'ZECUSD', 'ALGOUSD', 'IOTAUSD', 'BUSDUSD', 'WAVESUSD', 'ATOMUSD', 'NEOUSD', 'QTUMUSD', 'ICXUSD', 'ENJUSD', 'ONTUSD', 'ZILUSD', 'VETUSD', 'XTZUSD', 'HBARUSD', 'OMGUSD', 'MATICUSD', 'EOSUSD', 'DOGEUSD', 'KNCUSD', 'VTHOUSD', 'USDCUSD', 'COMPUSD', 'MANAUSD', 'HNTUSD', 'MKRUSD', 'DAIUSD', 'ONEUSD', 'BANDUSD', 'STORJUSD', 'UNIUSD', 'SOLUSD', 'EGLDUSD', 'PAXGUSD', 'OXTUSD', 'ZENUSD', 'FILUSD', 'AAVEUSD', 'GRTUSD', 'SUSHIUSD', 'ANKRUSD', 'CRVUSD', 'AXSUSD', 'AVAXUSD', 'CTSIUSD', 'DOTUSD', 'YFIUSD', '1INCHUSD', 'FTMUSD', 'NEARUSD', 'LRCUSD', 'KSHIBUSD', 'LPTUSD', 'NMRUSD', 'SLPUSD', 'ANTUSD', 'XNOUSD', 'CHZUSD', 'OGNUSD', 'GALAUSD', 'TLMUSD', 'SNXUSD', 'AUDIOUSD', 'REQUSD', 'APEUSD', 'FLUXUSD', 'TRXUSD', 'COTIUSD', 'VOXELUSD', 'RLCUSD', 'BICOUSD', 'API3USD', 'ENSUSD', 'BNTUSD', 'IMXUSD', 'SPELLUSD', 'JASMYUSD', 'FLOWUSD', 'GTCUSD', 'THETAUSD', 'TFUELUSD', 'OCEANUSD', 'LAZIOUSD', 'SANTOSUSD', 'ALPINEUSD', 'PORTOUSD', 'RENUSD', 'CELRUSD', 'SKLUSD', 'VITEUSD', 'WAXPUSD', 'LTOUSD', 'FETUSD', 'BONDUSD', 'LOKAUSD', 'ICPUSD', 'TUSD', 'OPUSD', 'ROSEUSD', 'CELOUSD', 'KDAUSD', 'KSMUSD', 'ACHUSD', 'DARUSD', 'RNDRUSD', 'SYSUSD', 'RADUSD', 'ILVUSD', 'LDOUSD', 'RAREUSD', 'LSKUSD', 'DGBUSD', 'REEFUSD', 'ALICEUSD', 'FORTHUSD', 'ASTRUSD', 'BTRSTUSD', 'GALUSD', 'SANDUSD', 'BALUSD', 'POLYXUSD', 'GLMUSD', 'CLVUSD', 'TUSDUSD', 'QNTUSD', 'STGUSD', 'AXLUSD', 'KAVAUSD', 'APTUSD', 'MASKUSD', 'BOSONUSD', 'PONDUSD', 'MXCUSD', 'JAMUSD', 'PROMUSD', 'DIAUSD', 'LOOMUSD', 'STMXUSD', 'SHIBUSD', 'TRACUSD', 'POLYXUSDT', 'IOSTUSDT', 'IOSTUSD', 'MATICETH', 'SOLETH', 'FLOKI8USDT', 'FLOKI8USD', 'ARBUSDT', 'ARBUSD', 'FLOKIUSDT', 'FLOKIUSD', 'XECUSDT', 'XECUSD', 'BLURUSDT', 'BLURUSD', 'ANKRUSDT', 'DAIUSDT', 'DASHUSDT', 'HBARUSDT', 'ICXUSDT', 'IOTAUSDT', 'RVNUSDT', 'WAVESUSDT', 'XNOUSDT', 'XTZUSDT', 'ZILUSDT', 'ORBSUSDT', 'CUDOSUSDT', 'ADXUSDT', 'FORTUSDT', 'SUIUSDT', 'ONGUSDT', 'GUSDT', 'RENDERUSDT', 'BONKUSDT', 'MAGICUSDT']\n"
     ]
    }
   ],
   "source": [
    "# Collect tickers available through Binance API\n",
    "client = bnb_client(tld='US') # if you're in the US, please use: client=bnb_client(tld='US')'\n",
    "exchange_info = client.get_exchange_info()\n",
    "\n",
    "symbols = [i['symbol'] for i in exchange_info['symbols']]\n",
    "print(symbols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a5ef5f5b-3ae1-4e83-a136-98df4dd233d4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['BTCUSDT', 'ETHUSDT', 'XRPUSDT', 'BCHUSDT', 'LTCUSDT', 'BNBUSDT', 'ADAUSDT', 'BATUSDT', 'ETCUSDT', 'XLMUSDT', 'ZRXUSDT', 'DOGEUSDT', 'ATOMUSDT', 'NEOUSDT', 'VETUSDT', 'QTUMUSDT', 'ONTUSDT', 'KNCUSDT', 'VTHOUSDT', 'COMPUSDT', 'HNTUSDT', 'MKRUSDT', 'ONEUSDT', 'BANDUSDT', 'STORJUSDT', 'BUSDUSDT', 'UNIUSDT', 'SOLUSDT', 'EGLDUSDT', 'PAXGUSDT', 'OXTUSDT', 'ZENUSDT', 'FILUSDT', 'AAVEUSDT', 'GRTUSDT', 'SHIBUSDT', 'CRVUSDT', 'AXSUSDT', 'AVAXUSDT', 'CTSIUSDT', 'DOTUSDT', 'YFIUSDT', '1INCHUSDT', 'FTMUSDT', 'USDCUSDT', 'MATICUSDT', 'MANAUSDT', 'ALGOUSDT', 'LINKUSDT', 'EOSUSDT', 'ZECUSDT', 'ENJUSDT', 'NEARUSDT', 'OMGUSDT', 'SUSHIUSDT', 'LRCUSDT', 'LPTUSDT', 'POLYUSDT', 'NMRUSDT', 'SLPUSDT', 'ANTUSDT', 'CHZUSDT', 'OGNUSDT', 'GALAUSDT', 'TLMUSDT', 'SNXUSDT', 'AUDIOUSDT', 'ENSUSDT', 'REQUSDT', 'APEUSDT', 'FLUXUSDT', 'TRXUSDT', 'COTIUSDT', 'VOXELUSDT', 'RLCUSDT', 'USTUSDT', 'BICOUSDT', 'API3USDT', 'BNTUSDT', 'IMXUSDT', 'SPELLUSDT', 'JASMYUSDT', 'FLOWUSDT', 'GTCUSDT', 'THETAUSDT', 'TFUELUSDT', 'OCEANUSDT', 'LAZIOUSDT', 'SANTOSUSDT', 'ALPINEUSDT', 'PORTOUSDT', 'RENUSDT', 'CELRUSDT', 'SKLUSDT', 'VITEUSDT', 'WAXPUSDT', 'LTOUSDT', 'FETUSDT', 'BONDUSDT', 'LOKAUSDT', 'ICPUSDT', 'TUSDT', 'OPUSDT', 'ROSEUSDT', 'CELOUSDT', 'KDAUSDT', 'KSMUSDT', 'ACHUSDT', 'DARUSDT', 'RNDRUSDT', 'SYSUSDT', 'RADUSDT', 'ILVUSDT', 'LDOUSDT', 'RAREUSDT', 'LSKUSDT', 'DGBUSDT', 'REEFUSDT', 'SRMUSDT', 'ALICEUSDT', 'FORTHUSDT', 'ASTRUSDT', 'BTRSTUSDT', 'GALUSDT', 'SANDUSDT', 'BALUSDT', 'GLMUSDT', 'CLVUSDT', 'TUSDUSDT', 'QNTUSDT', 'STGUSDT', 'AXLUSDT', 'KAVAUSDT', 'APTUSDT', 'MASKUSDT', 'BOSONUSDT', 'PONDUSDT', 'MXCUSDT', 'JAMUSDT', 'TRACUSDT', 'PROMUSDT', 'DIAUSDT', 'LOOMUSDT', 'STMXUSDT', 'POLYXUSDT', 'IOSTUSDT', 'FLOKI8USDT', 'ARBUSDT', 'FLOKIUSDT', 'XECUSDT', 'BLURUSDT', 'ANKRUSDT', 'DAIUSDT', 'DASHUSDT', 'HBARUSDT', 'ICXUSDT', 'IOTAUSDT', 'RVNUSDT', 'WAVESUSDT', 'XNOUSDT', 'XTZUSDT', 'ZILUSDT', 'ORBSUSDT', 'CUDOSUSDT', 'ADXUSDT', 'FORTUSDT', 'SUIUSDT', 'ONGUSDT', 'GUSDT', 'RENDERUSDT', 'BONKUSDT', 'MAGICUSDT']\n",
      "172 pairs\n"
     ]
    }
   ],
   "source": [
    "# Filter for just the Crypto - USDT pairs\n",
    "univ = [symbol for symbol in symbols if symbol.endswith('USDT')]\n",
    "print(univ)\n",
    "print(str(len(univ)) + \" pairs\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5f8f3b78-7c81-4d55-9ee5-0df825a52701",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>BTCUSDT</th>\n",
       "      <th>ETHUSDT</th>\n",
       "      <th>XRPUSDT</th>\n",
       "      <th>BCHUSDT</th>\n",
       "      <th>LTCUSDT</th>\n",
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       "      <th>ADAUSDT</th>\n",
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       "      <th>ETCUSDT</th>\n",
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       "      <th>BONKUSDT</th>\n",
       "      <th>MAGICUSDT</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-09-27</th>\n",
       "      <td>0.011284</td>\n",
       "      <td>0.023668</td>\n",
       "      <td>-0.000510</td>\n",
       "      <td>0.027444</td>\n",
       "      <td>0.039264</td>\n",
       "      <td>0.017770</td>\n",
       "      <td>0.001497</td>\n",
       "      <td>-0.002568</td>\n",
       "      <td>0.031683</td>\n",
       "      <td>0.006122</td>\n",
       "      <td>...</td>\n",
       "      <td>0.016841</td>\n",
       "      <td>-0.004863</td>\n",
       "      <td>0.054264</td>\n",
       "      <td>0.031426</td>\n",
       "      <td>0.005435</td>\n",
       "      <td>0.078882</td>\n",
       "      <td>0.044895</td>\n",
       "      <td>0.062371</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-09-28</th>\n",
       "      <td>0.001861</td>\n",
       "      <td>-0.007199</td>\n",
       "      <td>0.043338</td>\n",
       "      <td>-0.018920</td>\n",
       "      <td>-0.017416</td>\n",
       "      <td>-0.011860</td>\n",
       "      <td>-0.002990</td>\n",
       "      <td>-0.009784</td>\n",
       "      <td>-0.016315</td>\n",
       "      <td>0.010142</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.014722</td>\n",
       "      <td>-0.172368</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.001563</td>\n",
       "      <td>0.017058</td>\n",
       "      <td>-0.099885</td>\n",
       "      <td>-0.018168</td>\n",
       "      <td>-0.024350</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-09-29</th>\n",
       "      <td>-0.003603</td>\n",
       "      <td>-0.006664</td>\n",
       "      <td>0.044958</td>\n",
       "      <td>-0.003403</td>\n",
       "      <td>-0.008290</td>\n",
       "      <td>-0.007001</td>\n",
       "      <td>-0.009495</td>\n",
       "      <td>-0.011440</td>\n",
       "      <td>-0.012683</td>\n",
       "      <td>0.020080</td>\n",
       "      <td>...</td>\n",
       "      <td>0.016810</td>\n",
       "      <td>0.163318</td>\n",
       "      <td>-0.000613</td>\n",
       "      <td>0.017214</td>\n",
       "      <td>0.038328</td>\n",
       "      <td>0.053086</td>\n",
       "      <td>-0.005001</td>\n",
       "      <td>0.023886</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-09-30</th>\n",
       "      <td>-0.036221</td>\n",
       "      <td>-0.020766</td>\n",
       "      <td>-0.046298</td>\n",
       "      <td>-0.046670</td>\n",
       "      <td>-0.037187</td>\n",
       "      <td>-0.051704</td>\n",
       "      <td>-0.058527</td>\n",
       "      <td>-0.064703</td>\n",
       "      <td>-0.042490</td>\n",
       "      <td>-0.031496</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.020206</td>\n",
       "      <td>-0.001228</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.020000</td>\n",
       "      <td>0.018144</td>\n",
       "      <td>-0.099605</td>\n",
       "      <td>-0.067856</td>\n",
       "      <td>-0.034844</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-10-01</th>\n",
       "      <td>-0.036221</td>\n",
       "      <td>-0.057193</td>\n",
       "      <td>-0.023864</td>\n",
       "      <td>-0.024478</td>\n",
       "      <td>-0.048952</td>\n",
       "      <td>-0.029740</td>\n",
       "      <td>-0.054930</td>\n",
       "      <td>-0.055118</td>\n",
       "      <td>-0.060888</td>\n",
       "      <td>-0.047764</td>\n",
       "      <td>...</td>\n",
       "      <td>0.140607</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.107909</td>\n",
       "      <td>-0.037677</td>\n",
       "      <td>-0.005642</td>\n",
       "      <td>-0.055649</td>\n",
       "      <td>-0.068752</td>\n",
       "      <td>-0.103967</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1836 rows × 172 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             BTCUSDT   ETHUSDT   XRPUSDT   BCHUSDT   LTCUSDT   BNBUSDT  \\\n",
       "2019-09-23       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "2019-09-24 -0.142038 -0.202625 -0.148182 -0.279268 -0.216227 -0.201381   \n",
       "2019-09-25 -0.009905  0.017416  0.057557  0.022830 -0.002948  0.011949   \n",
       "2019-09-26 -0.043967 -0.019471 -0.012275 -0.047116 -0.039652 -0.052480   \n",
       "2019-09-27  0.014176  0.041994 -0.008600  0.022404  0.006519  0.025075   \n",
       "...              ...       ...       ...       ...       ...       ...   \n",
       "2024-09-27  0.011284  0.023668 -0.000510  0.027444  0.039264  0.017770   \n",
       "2024-09-28  0.001861 -0.007199  0.043338 -0.018920 -0.017416 -0.011860   \n",
       "2024-09-29 -0.003603 -0.006664  0.044958 -0.003403 -0.008290 -0.007001   \n",
       "2024-09-30 -0.036221 -0.020766 -0.046298 -0.046670 -0.037187 -0.051704   \n",
       "2024-10-01 -0.036221 -0.057193 -0.023864 -0.024478 -0.048952 -0.029740   \n",
       "\n",
       "             ADAUSDT   BATUSDT   ETCUSDT   XLMUSDT  ...  ORBSUSDT  CUDOSUSDT  \\\n",
       "2019-09-23       NaN       NaN       NaN       NaN  ...       NaN        NaN   \n",
       "2019-09-24       NaN       NaN       NaN       NaN  ...       NaN        NaN   \n",
       "2019-09-25       NaN       NaN       NaN       NaN  ...       NaN        NaN   \n",
       "2019-09-26 -0.028462 -0.061849 -0.068501  0.023398  ...       NaN        NaN   \n",
       "2019-09-27  0.032990  0.079634  0.000612 -0.022522  ...       NaN        NaN   \n",
       "...              ...       ...       ...       ...  ...       ...        ...   \n",
       "2024-09-27  0.001497 -0.002568  0.031683  0.006122  ...  0.016841  -0.004863   \n",
       "2024-09-28 -0.002990 -0.009784 -0.016315  0.010142  ... -0.014722  -0.172368   \n",
       "2024-09-29 -0.009495 -0.011440 -0.012683  0.020080  ...  0.016810   0.163318   \n",
       "2024-09-30 -0.058527 -0.064703 -0.042490 -0.031496  ... -0.020206  -0.001228   \n",
       "2024-10-01 -0.054930 -0.055118 -0.060888 -0.047764  ...  0.140607   0.000000   \n",
       "\n",
       "             ADXUSDT  FORTUSDT   SUIUSDT   ONGUSDT     GUSDT  RENDERUSDT  \\\n",
       "2019-09-23       NaN       NaN       NaN       NaN       NaN         NaN   \n",
       "2019-09-24       NaN       NaN       NaN       NaN       NaN         NaN   \n",
       "2019-09-25       NaN       NaN       NaN       NaN       NaN         NaN   \n",
       "2019-09-26       NaN       NaN       NaN       NaN       NaN         NaN   \n",
       "2019-09-27       NaN       NaN       NaN       NaN       NaN         NaN   \n",
       "...              ...       ...       ...       ...       ...         ...   \n",
       "2024-09-27  0.054264  0.031426  0.005435  0.078882  0.044895    0.062371   \n",
       "2024-09-28  0.000000 -0.001563  0.017058 -0.099885 -0.018168   -0.024350   \n",
       "2024-09-29 -0.000613  0.017214  0.038328  0.053086 -0.005001    0.023886   \n",
       "2024-09-30  0.000000 -0.020000  0.018144 -0.099605 -0.067856   -0.034844   \n",
       "2024-10-01 -0.107909 -0.037677 -0.005642 -0.055649 -0.068752   -0.103967   \n",
       "\n",
       "            BONKUSDT  MAGICUSDT  \n",
       "2019-09-23       NaN        NaN  \n",
       "2019-09-24       NaN        NaN  \n",
       "2019-09-25       NaN        NaN  \n",
       "2019-09-26       NaN        NaN  \n",
       "2019-09-27       NaN        NaN  \n",
       "...              ...        ...  \n",
       "2024-09-27       NaN        NaN  \n",
       "2024-09-28       NaN        NaN  \n",
       "2024-09-29       NaN        NaN  \n",
       "2024-09-30       NaN        NaN  \n",
       "2024-10-01       NaN        NaN  \n",
       "\n",
       "[1836 rows x 172 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Collect daily cryptocurrency close prices\n",
    "freq = '1d'\n",
    "px = {}\n",
    "for x in univ:\n",
    "    data = get_binance_px(x,freq)\n",
    "    px[x] = data.set_index('open_time')['close']\n",
    "\n",
    "px = pd.DataFrame(px).astype(float)\n",
    "px = px.reindex(pd.date_range(px.index[0],px.index[-1],freq=freq))\n",
    "ret = px.pct_change()\n",
    "ret"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "41fae84d-23f5-4c3c-b09c-131bf75cfc85",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2019-09-23         NaN\n",
       "2019-09-24   -0.142038\n",
       "2019-09-25   -0.009905\n",
       "2019-09-26   -0.043967\n",
       "2019-09-27    0.014176\n",
       "                ...   \n",
       "2024-09-27    0.011284\n",
       "2024-09-28    0.001861\n",
       "2024-09-29   -0.003603\n",
       "2024-09-30   -0.036221\n",
       "2024-10-01   -0.036221\n",
       "Freq: D, Name: BTCUSDT, Length: 1836, dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Separate out bitcoin data to be used as benchmark\n",
    "btc = px['BTCUSDT']\n",
    "btc_ret = btc.pct_change()\n",
    "btc_ret"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "dc000fa2-f1b3-487f-95ce-5507c60b3d9c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ETHUSDT</th>\n",
       "      <th>XRPUSDT</th>\n",
       "      <th>BCHUSDT</th>\n",
       "      <th>LTCUSDT</th>\n",
       "      <th>BNBUSDT</th>\n",
       "      <th>ADAUSDT</th>\n",
       "      <th>BATUSDT</th>\n",
       "      <th>ETCUSDT</th>\n",
       "      <th>XLMUSDT</th>\n",
       "      <th>ZRXUSDT</th>\n",
       "      <th>...</th>\n",
       "      <th>ORBSUSDT</th>\n",
       "      <th>CUDOSUSDT</th>\n",
       "      <th>ADXUSDT</th>\n",
       "      <th>FORTUSDT</th>\n",
       "      <th>SUIUSDT</th>\n",
       "      <th>ONGUSDT</th>\n",
       "      <th>GUSDT</th>\n",
       "      <th>RENDERUSDT</th>\n",
       "      <th>BONKUSDT</th>\n",
       "      <th>MAGICUSDT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-09-23</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-24</th>\n",
       "      <td>-0.202625</td>\n",
       "      <td>-0.148182</td>\n",
       "      <td>-0.279268</td>\n",
       "      <td>-0.216227</td>\n",
       "      <td>-0.201381</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-25</th>\n",
       "      <td>0.017416</td>\n",
       "      <td>0.057557</td>\n",
       "      <td>0.022830</td>\n",
       "      <td>-0.002948</td>\n",
       "      <td>0.011949</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-26</th>\n",
       "      <td>-0.019471</td>\n",
       "      <td>-0.012275</td>\n",
       "      <td>-0.047116</td>\n",
       "      <td>-0.039652</td>\n",
       "      <td>-0.052480</td>\n",
       "      <td>-0.028462</td>\n",
       "      <td>-0.061849</td>\n",
       "      <td>-0.068501</td>\n",
       "      <td>0.023398</td>\n",
       "      <td>0.038059</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-27</th>\n",
       "      <td>0.041994</td>\n",
       "      <td>-0.008600</td>\n",
       "      <td>0.022404</td>\n",
       "      <td>0.006519</td>\n",
       "      <td>0.025075</td>\n",
       "      <td>0.032990</td>\n",
       "      <td>0.079634</td>\n",
       "      <td>0.000612</td>\n",
       "      <td>-0.022522</td>\n",
       "      <td>-0.022915</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-09-27</th>\n",
       "      <td>0.023668</td>\n",
       "      <td>-0.000510</td>\n",
       "      <td>0.027444</td>\n",
       "      <td>0.039264</td>\n",
       "      <td>0.017770</td>\n",
       "      <td>0.001497</td>\n",
       "      <td>-0.002568</td>\n",
       "      <td>0.031683</td>\n",
       "      <td>0.006122</td>\n",
       "      <td>0.046755</td>\n",
       "      <td>...</td>\n",
       "      <td>0.016841</td>\n",
       "      <td>-0.004863</td>\n",
       "      <td>0.054264</td>\n",
       "      <td>0.031426</td>\n",
       "      <td>0.005435</td>\n",
       "      <td>0.078882</td>\n",
       "      <td>0.044895</td>\n",
       "      <td>0.062371</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-09-28</th>\n",
       "      <td>-0.007199</td>\n",
       "      <td>0.043338</td>\n",
       "      <td>-0.018920</td>\n",
       "      <td>-0.017416</td>\n",
       "      <td>-0.011860</td>\n",
       "      <td>-0.002990</td>\n",
       "      <td>-0.009784</td>\n",
       "      <td>-0.016315</td>\n",
       "      <td>0.010142</td>\n",
       "      <td>-0.038170</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.014722</td>\n",
       "      <td>-0.172368</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.001563</td>\n",
       "      <td>0.017058</td>\n",
       "      <td>-0.099885</td>\n",
       "      <td>-0.018168</td>\n",
       "      <td>-0.024350</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-09-29</th>\n",
       "      <td>-0.006664</td>\n",
       "      <td>0.044958</td>\n",
       "      <td>-0.003403</td>\n",
       "      <td>-0.008290</td>\n",
       "      <td>-0.007001</td>\n",
       "      <td>-0.009495</td>\n",
       "      <td>-0.011440</td>\n",
       "      <td>-0.012683</td>\n",
       "      <td>0.020080</td>\n",
       "      <td>0.019702</td>\n",
       "      <td>...</td>\n",
       "      <td>0.016810</td>\n",
       "      <td>0.163318</td>\n",
       "      <td>-0.000613</td>\n",
       "      <td>0.017214</td>\n",
       "      <td>0.038328</td>\n",
       "      <td>0.053086</td>\n",
       "      <td>-0.005001</td>\n",
       "      <td>0.023886</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-09-30</th>\n",
       "      <td>-0.020766</td>\n",
       "      <td>-0.046298</td>\n",
       "      <td>-0.046670</td>\n",
       "      <td>-0.037187</td>\n",
       "      <td>-0.051704</td>\n",
       "      <td>-0.058527</td>\n",
       "      <td>-0.064703</td>\n",
       "      <td>-0.042490</td>\n",
       "      <td>-0.031496</td>\n",
       "      <td>-0.079492</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.020206</td>\n",
       "      <td>-0.001228</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.020000</td>\n",
       "      <td>0.018144</td>\n",
       "      <td>-0.099605</td>\n",
       "      <td>-0.067856</td>\n",
       "      <td>-0.034844</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-10-01</th>\n",
       "      <td>-0.057193</td>\n",
       "      <td>-0.023864</td>\n",
       "      <td>-0.024478</td>\n",
       "      <td>-0.048952</td>\n",
       "      <td>-0.029740</td>\n",
       "      <td>-0.054930</td>\n",
       "      <td>-0.055118</td>\n",
       "      <td>-0.060888</td>\n",
       "      <td>-0.047764</td>\n",
       "      <td>-0.089655</td>\n",
       "      <td>...</td>\n",
       "      <td>0.140607</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.107909</td>\n",
       "      <td>-0.037677</td>\n",
       "      <td>-0.005642</td>\n",
       "      <td>-0.055649</td>\n",
       "      <td>-0.068752</td>\n",
       "      <td>-0.103967</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1836 rows × 171 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             ETHUSDT   XRPUSDT   BCHUSDT   LTCUSDT   BNBUSDT   ADAUSDT  \\\n",
       "2019-09-23       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "2019-09-24 -0.202625 -0.148182 -0.279268 -0.216227 -0.201381       NaN   \n",
       "2019-09-25  0.017416  0.057557  0.022830 -0.002948  0.011949       NaN   \n",
       "2019-09-26 -0.019471 -0.012275 -0.047116 -0.039652 -0.052480 -0.028462   \n",
       "2019-09-27  0.041994 -0.008600  0.022404  0.006519  0.025075  0.032990   \n",
       "...              ...       ...       ...       ...       ...       ...   \n",
       "2024-09-27  0.023668 -0.000510  0.027444  0.039264  0.017770  0.001497   \n",
       "2024-09-28 -0.007199  0.043338 -0.018920 -0.017416 -0.011860 -0.002990   \n",
       "2024-09-29 -0.006664  0.044958 -0.003403 -0.008290 -0.007001 -0.009495   \n",
       "2024-09-30 -0.020766 -0.046298 -0.046670 -0.037187 -0.051704 -0.058527   \n",
       "2024-10-01 -0.057193 -0.023864 -0.024478 -0.048952 -0.029740 -0.054930   \n",
       "\n",
       "             BATUSDT   ETCUSDT   XLMUSDT   ZRXUSDT  ...  ORBSUSDT  CUDOSUSDT  \\\n",
       "2019-09-23       NaN       NaN       NaN       NaN  ...       NaN        NaN   \n",
       "2019-09-24       NaN       NaN       NaN       NaN  ...       NaN        NaN   \n",
       "2019-09-25       NaN       NaN       NaN       NaN  ...       NaN        NaN   \n",
       "2019-09-26 -0.061849 -0.068501  0.023398  0.038059  ...       NaN        NaN   \n",
       "2019-09-27  0.079634  0.000612 -0.022522 -0.022915  ...       NaN        NaN   \n",
       "...              ...       ...       ...       ...  ...       ...        ...   \n",
       "2024-09-27 -0.002568  0.031683  0.006122  0.046755  ...  0.016841  -0.004863   \n",
       "2024-09-28 -0.009784 -0.016315  0.010142 -0.038170  ... -0.014722  -0.172368   \n",
       "2024-09-29 -0.011440 -0.012683  0.020080  0.019702  ...  0.016810   0.163318   \n",
       "2024-09-30 -0.064703 -0.042490 -0.031496 -0.079492  ... -0.020206  -0.001228   \n",
       "2024-10-01 -0.055118 -0.060888 -0.047764 -0.089655  ...  0.140607   0.000000   \n",
       "\n",
       "             ADXUSDT  FORTUSDT   SUIUSDT   ONGUSDT     GUSDT  RENDERUSDT  \\\n",
       "2019-09-23       NaN       NaN       NaN       NaN       NaN         NaN   \n",
       "2019-09-24       NaN       NaN       NaN       NaN       NaN         NaN   \n",
       "2019-09-25       NaN       NaN       NaN       NaN       NaN         NaN   \n",
       "2019-09-26       NaN       NaN       NaN       NaN       NaN         NaN   \n",
       "2019-09-27       NaN       NaN       NaN       NaN       NaN         NaN   \n",
       "...              ...       ...       ...       ...       ...         ...   \n",
       "2024-09-27  0.054264  0.031426  0.005435  0.078882  0.044895    0.062371   \n",
       "2024-09-28  0.000000 -0.001563  0.017058 -0.099885 -0.018168   -0.024350   \n",
       "2024-09-29 -0.000613  0.017214  0.038328  0.053086 -0.005001    0.023886   \n",
       "2024-09-30  0.000000 -0.020000  0.018144 -0.099605 -0.067856   -0.034844   \n",
       "2024-10-01 -0.107909 -0.037677 -0.005642 -0.055649 -0.068752   -0.103967   \n",
       "\n",
       "            BONKUSDT  MAGICUSDT  \n",
       "2019-09-23       NaN        NaN  \n",
       "2019-09-24       NaN        NaN  \n",
       "2019-09-25       NaN        NaN  \n",
       "2019-09-26       NaN        NaN  \n",
       "2019-09-27       NaN        NaN  \n",
       "...              ...        ...  \n",
       "2024-09-27       NaN        NaN  \n",
       "2024-09-28       NaN        NaN  \n",
       "2024-09-29       NaN        NaN  \n",
       "2024-09-30       NaN        NaN  \n",
       "2024-10-01       NaN        NaN  \n",
       "\n",
       "[1836 rows x 171 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remove BTC from backtesting data as it will be the benchmark\n",
    "ret = ret.drop(columns=['BTCUSDT'])\n",
    "ret"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1844e2f9-71f0-4168-9b4e-0f0f0833b23b",
   "metadata": {},
   "source": [
    "# Performance Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d9df3c9a-7468-46f4-9b57-3030e8ef3f8c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculates and prints stats for backtest\n",
    "def performance_stats(strat_ret):\n",
    "    stats = {}\n",
    "    stats['SR'] = strat_ret.mean() / strat_ret.std() * np.sqrt(365)\n",
    "    stats['ret'] = strat_ret.mean()*365\n",
    "    stats['vol'] = strat_ret.std()*np.sqrt(365)\n",
    "    stats = pd.Series(stats)\n",
    "    return stats\n",
    "\n",
    "def print_performance_stats(stats):\n",
    "    print(\"Sharpe Ratio:\", round(stats['SR'],4))\n",
    "    print(\"Annualized Returns:\", round(stats['ret'],4))\n",
    "    print(\"Volatility:\", round(stats['vol'],4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "5c108ebb-78bc-496f-aabe-2955b4e3d154",
   "metadata": {},
   "outputs": [],
   "source": [
    "def regression_stats(strat_ret, btc_ret):\n",
    "    X = btc_ret\n",
    "    X = sm.add_constant(X)\n",
    "    Y = strat_ret\n",
    "    X = X.dropna()\n",
    "    Y = Y.dropna().iloc[1:]\n",
    "    results = sm.OLS(Y, X).fit()\n",
    "\n",
    "    stats = {}\n",
    "    stats['beta_contr'] = results.params['BTCUSDT']*X['BTCUSDT']\n",
    "    stats['prediction'] = results.params['BTCUSDT']*X['BTCUSDT'] + results.params['const']\n",
    "    stats['alpha_contr'] = results.resid\n",
    "    stats['t-stat'] = results.tvalues['const']\n",
    "    stats['const'] = results.params['const']\n",
    "    stats = pd.Series(stats)\n",
    "    return stats\n",
    "\n",
    "def print_regression_stats(stats):\n",
    "    daily_alpha = stats['const']\n",
    "    print(f\"Daily Alpha: {daily_alpha:.6f}\")\n",
    "    alpha = (1 + daily_alpha) ** 365 - 1\n",
    "    information_ratio = daily_alpha/stats['alpha_contr'].std()*np.sqrt(365)\n",
    "    t_stat = stats['t-stat']\n",
    "\n",
    "    print(\"Alpha:\", round(alpha,4))\n",
    "    print(\"Information Ratio:\", round(information_ratio,4))\n",
    "    print(\"T-stat:\", round(t_stat,4))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e85149ec-14a1-4c9a-b005-813df8a140d8",
   "metadata": {},
   "source": [
    "# Momentum-Based Strategy\n",
    "Below is an implementation of a z-score based momentum strategy along with associated parameter optimization\n",
    "\n",
    "**Findings: This strategy, when backtested over historical daily cryptocurrency prices since 2017, has a sharpe ratio of ~1.2. The best values for short-window and long-window appear to be around 15 days and 150 days**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "0c5b6046-dd76-4517-aec0-8a158eb5f28c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Z-Score Momentum signal generation\n",
    "def z_score_momentum(ret, short_window, long_window, skip_first_day = True):\n",
    "    # Z-score inputs\n",
    "    short_term_return = ret.rolling(short_window,min_periods=1).mean()\n",
    "    rolling_mean = ret.rolling(long_window, min_periods=1).mean()\n",
    "    rolling_std = ret.rolling(long_window, min_periods=1).std()\n",
    "\n",
    "    # Z-score calculation - code below calculates z-scores and takes the tanh of the results in order to smoothen outliers\n",
    "    z_scores = (short_term_return - rolling_mean) / rolling_std\n",
    "    z_scores = np.tanh(z_scores)\n",
    "    z_scores = z_scores.div(z_scores.abs().sum(axis=1),axis=0)\n",
    "\n",
    "    # Gross return calculation, skips the first bar for momentum to avoid exposure to reversal in first time period after signal. \n",
    "    # Optionality for skipping first day or not\n",
    "    if skip_first_day:\n",
    "        gross_return = (z_scores.shift(2)*ret).sum(1)\n",
    "    else:\n",
    "        gross_return = (z_scores.shift(1)*ret).sum(1)\n",
    "        \n",
    "\n",
    "    # Calculate turnover and net returns (minus T-costs)\n",
    "    # Place limit orders (7 bps) with a success rate of ~80% and then the remaining ~20% are filled as market orders (20 bps)\n",
    "    limit_cost = 7\n",
    "    market_cost = 20\n",
    "    limit_fill_rate = 0.8\n",
    "    to = (z_scores.fillna(0) - z_scores.shift().fillna(0)).abs().sum(axis=1)\n",
    "    filled_at_limit = np.random.binomial(1, limit_fill_rate, len(to))\n",
    "    transaction_costs = np.where(filled_at_limit, to * limit_cost * 1e-4, to * market_cost * 1e-4)\n",
    "    net_return = gross_return.subtract(transaction_costs,fill_value=0)\n",
    "    return net_return"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "fcf9dfed-05bb-483c-95ac-8a9821c436e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best short window: 32\n",
      "Best long window: 157\n",
      "Best Sharpe Ratio: 1.0944671265321069\n"
     ]
    }
   ],
   "source": [
    "# Bayesian Optimization for momentum strategy parameters (rolling windows) - v1 skipping first day\n",
    "# Best sharpe ratio is ~1.1\n",
    "\n",
    "space  = [\n",
    "    Integer(3, 50, name='short_window'),  # Short window: between 3 and 50 days\n",
    "    Integer(20, 200, name='long_window')  # Long window: between 20 and 200 days\n",
    "]\n",
    "\n",
    "results_list = []\n",
    "def objective(params):\n",
    "    short_window = params[0]\n",
    "    long_window = params[1]\n",
    "\n",
    "    skip_first_day = True\n",
    "    if long_window >= 2 * short_window:\n",
    "        returns = z_score_momentum(ret, short_window, long_window, skip_first_day)\n",
    "        sharpe = performance_stats(returns)['SR']\n",
    "        results_list.append((short_window, long_window, sharpe))\n",
    "        return -sharpe\n",
    "\n",
    "    else:\n",
    "        return 5\n",
    "\n",
    "res = gp_minimize(objective,\n",
    "                  space,\n",
    "                  n_calls=100,\n",
    "                  random_state=0,\n",
    "                  n_initial_points=10,\n",
    "                  acq_func=\"EI\")\n",
    "\n",
    "# Convert the results to a DataFrame for easy manipulation\n",
    "results_df = pd.DataFrame(results_list, columns=['short_window', 'long_window', 'sharpe_ratio'])\n",
    "\n",
    "# Plot a 2D scatter plot with color representing the Sharpe ratio\n",
    "plt.scatter(results_df['short_window'], results_df['long_window'], c=results_df['sharpe_ratio'], cmap='viridis', s=100)\n",
    "plt.colorbar(label='Sharpe Ratio')\n",
    "plt.xlabel('Short Window')\n",
    "plt.ylabel('Long Window')\n",
    "plt.title('Bayesian Optimization Results - Sharpe Ratios')\n",
    "plt.show()\n",
    "\n",
    "# Best parameters\n",
    "print(\"Best short window:\", res.x[0])\n",
    "print(\"Best long window:\", res.x[1])\n",
    "\n",
    "# Best result\n",
    "print(\"Best Sharpe Ratio:\", -res.fun)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "75705848-d8fd-4c21-821d-e83672652059",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/BrianPlotnik/opt/anaconda3/envs/py38-env/lib/python3.8/site-packages/skopt/optimizer/optimizer.py:517: UserWarning: The objective has been evaluated at point [33, 160] before, using random point [20, 134]\n",
      "  warnings.warn(\n",
      "/Users/BrianPlotnik/opt/anaconda3/envs/py38-env/lib/python3.8/site-packages/skopt/optimizer/optimizer.py:517: UserWarning: The objective has been evaluated at point [33, 160] before, using random point [8, 152]\n",
      "  warnings.warn(\n",
      "/Users/BrianPlotnik/opt/anaconda3/envs/py38-env/lib/python3.8/site-packages/skopt/optimizer/optimizer.py:517: UserWarning: The objective has been evaluated at point [33, 160] before, using random point [48, 183]\n",
      "  warnings.warn(\n",
      "/Users/BrianPlotnik/opt/anaconda3/envs/py38-env/lib/python3.8/site-packages/skopt/optimizer/optimizer.py:517: UserWarning: The objective has been evaluated at point [33, 160] before, using random point [49, 109]\n",
      "  warnings.warn(\n",
      "/Users/BrianPlotnik/opt/anaconda3/envs/py38-env/lib/python3.8/site-packages/skopt/optimizer/optimizer.py:517: UserWarning: The objective has been evaluated at point [33, 158] before, using random point [44, 120]\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best short window: 33\n",
      "Best long window: 158\n",
      "Best Sharpe Ratio: 0.6508092037534201\n"
     ]
    }
   ],
   "source": [
    "# Bayesian Optimization for momentum strategy parameters (rolling windows) - v2 not skipping first day\n",
    "# Best sharpe ratio is ~0.66\n",
    "\n",
    "space  = [\n",
    "    Integer(3, 50, name='short_window'),  # Short window: between 3 and 50 days\n",
    "    Integer(20, 200, name='long_window')  # Long window: between 20 and 200 days\n",
    "]\n",
    "\n",
    "results_list = []\n",
    "def objective(params):\n",
    "    short_window = params[0]\n",
    "    long_window = params[1]\n",
    "\n",
    "    skip_first_day = False\n",
    "    if long_window >= 2 * short_window:\n",
    "        returns = z_score_momentum(ret, short_window, long_window, skip_first_day)\n",
    "        sharpe = performance_stats(returns)['SR']\n",
    "        results_list.append((short_window, long_window, sharpe))\n",
    "        return -sharpe\n",
    "\n",
    "    else:\n",
    "        return 5\n",
    "\n",
    "res = gp_minimize(objective,\n",
    "                  space,\n",
    "                  n_calls=100,\n",
    "                  random_state=0,\n",
    "                  n_initial_points=10,\n",
    "                  acq_func=\"EI\")\n",
    "\n",
    "# Convert the results to a DataFrame for easy manipulation\n",
    "results_df = pd.DataFrame(results_list, columns=['short_window', 'long_window', 'sharpe_ratio'])\n",
    "\n",
    "# Plot a 2D scatter plot with color representing the Sharpe ratio\n",
    "plt.scatter(results_df['short_window'], results_df['long_window'], c=results_df['sharpe_ratio'], cmap='viridis', s=100)\n",
    "plt.colorbar(label='Sharpe Ratio')\n",
    "plt.xlabel('Short Window')\n",
    "plt.ylabel('Long Window')\n",
    "plt.title('Bayesian Optimization Results - Sharpe Ratios')\n",
    "plt.show()\n",
    "\n",
    "# Best parameters\n",
    "print(\"Best short window:\", res.x[0])\n",
    "print(\"Best long window:\", res.x[1])\n",
    "\n",
    "# Best result\n",
    "print(\"Best Sharpe Ratio:\", -res.fun)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "fdc266d6-e763-4b0b-b363-c6839ee79596",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Z_score Momentum Strategy Results\n",
      "\n",
      "Sharpe Ratio: 0.9599\n",
      "Annualized Returns: 0.6748\n",
      "Volatility: 0.703\n",
      "Alpha: 1.116\n",
      "Information Ratio: 1.0748\n",
      "T-stat: 2.4066\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Simple Z_score momentum results with optimized parameters and skipping first day\n",
    "portfolio_returns = z_score_momentum(ret,30,150, True)\n",
    "portfolio_returns.cumsum().plot()\n",
    "\n",
    "print(\"Z_score Momentum Strategy Results\\n\")\n",
    "stats = performance_stats(portfolio_returns)\n",
    "regression = regression_stats(portfolio_returns, btc_ret)\n",
    "print_performance_stats(stats)\n",
    "print_regression_stats(regression)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "112bc186-5e31-4569-8427-0d320027b1d0",
   "metadata": {},
   "source": [
    "# Z-Score Momentum Strategy with Volatility Filter\n",
    "\n",
    "In an attempt to improve my strategy's sharpe ratio, I implemented a volatility threshold to see if the strategy would give better results when only traded during times with higher volatility."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a2a65334-8f19-4715-8d2f-20e3d47a771a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Z-Score Momentum with Volatility Filter signal generation\n",
    "def z_score_momentum_with_volatility_filter(ret, short_window, long_window, vol_threshold):\n",
    "    # Rolling Volatility\n",
    "    vol_window = 14\n",
    "    rolling_volatility = ret.rolling(window=vol_window, min_periods=1).std()\n",
    "\n",
    "    # Volatility Threshold\n",
    "    volatility_mean = rolling_volatility.rolling(window=long_window, min_periods=1).mean()  # Long-term average of volatility\n",
    "    volatility_filter = rolling_volatility < (volatility_mean * vol_threshold) \n",
    "    \n",
    "    # Z-score inputs\n",
    "    short_term_return = ret.rolling(short_window,min_periods=1).mean()\n",
    "    rolling_mean = ret.rolling(long_window, min_periods=1).mean()\n",
    "    rolling_std = ret.rolling(long_window, min_periods=1).std()\n",
    "\n",
    "    # Z-score calculation - code below calculates z-scores and takes the tanh of the results in order to smoothen outliers\n",
    "    z_scores = (short_term_return - rolling_mean) / rolling_std\n",
    "    z_scores = np.tanh(z_scores)\n",
    "    z_scores = z_scores.div(z_scores.abs().sum(axis=1),axis=0)\n",
    "\n",
    "    # Apply volatility filter\n",
    "    z_scores = z_scores.where(volatility_filter, 0)\n",
    "\n",
    "    # Gross return calculation\n",
    "    gross_return = (z_scores.shift(2)*ret).sum(1)\n",
    "\n",
    "    # Calculate turnover and net returns (minus T-costs)\n",
    "    # Place limit orders (7 bps) with a success rate of 80% and then the remaining 20% are filled as market orders (20 bps)\n",
    "    limit_cost = 7\n",
    "    market_cost = 20\n",
    "    limit_fill_rate = 0.8\n",
    "    to = (z_scores.fillna(0) - z_scores.shift().fillna(0)).abs().sum(axis=1)\n",
    "    filled_at_limit = np.random.binomial(1, limit_fill_rate, len(to))\n",
    "    transaction_costs = np.where(filled_at_limit, to * limit_cost * 1e-4, to * market_cost * 1e-4)\n",
    "    net_return = gross_return.subtract(transaction_costs,fill_value=0)\n",
    "    return net_return"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "15bbab25",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/BrianPlotnik/opt/anaconda3/envs/py38-env/lib/python3.8/site-packages/skopt/optimizer/optimizer.py:517: UserWarning: The objective has been evaluated at point [30, 500] before, using random point [32, 33]\n",
      "  warnings.warn(\n",
      "/Users/BrianPlotnik/opt/anaconda3/envs/py38-env/lib/python3.8/site-packages/skopt/optimizer/optimizer.py:517: UserWarning: The objective has been evaluated at point [30, 500] before, using random point [27, 30]\n",
      "  warnings.warn(\n",
      "/Users/BrianPlotnik/opt/anaconda3/envs/py38-env/lib/python3.8/site-packages/skopt/optimizer/optimizer.py:517: UserWarning: The objective has been evaluated at point [32, 425] before, using random point [37, 440]\n",
      "  warnings.warn(\n",
      "/Users/BrianPlotnik/opt/anaconda3/envs/py38-env/lib/python3.8/site-packages/skopt/optimizer/optimizer.py:517: UserWarning: The objective has been evaluated at point [32, 430] before, using random point [13, 72]\n",
      "  warnings.warn(\n",
      "/Users/BrianPlotnik/opt/anaconda3/envs/py38-env/lib/python3.8/site-packages/skopt/optimizer/optimizer.py:517: UserWarning: The objective has been evaluated at point [32, 412] before, using random point [12, 204]\n",
      "  warnings.warn(\n",
      "/Users/BrianPlotnik/opt/anaconda3/envs/py38-env/lib/python3.8/site-packages/skopt/optimizer/optimizer.py:517: UserWarning: The objective has been evaluated at point [32, 434] before, using random point [14, 64]\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best short window: 32\n",
      "Best long window: 432\n",
      "Best Sharpe Ratio: 1.275056048571435\n"
     ]
    }
   ],
   "source": [
    "# Bayesian Optimization for momentum strategy parameters (rolling windows), using volatility threshold of 1 as starting point\n",
    "\n",
    "space  = [\n",
    "    Integer(3, 40, name='short_window'),  # Short window: between 3 and 40 days\n",
    "    Integer(20, 500, name='long_window'),  # Long window: between 20 and 500 days\n",
    "]\n",
    "\n",
    "results_list = []\n",
    "def objective(params):\n",
    "    short_window = params[0]\n",
    "    long_window = params[1]\n",
    "    \n",
    "    if long_window >= 2 * short_window:\n",
    "        volatility_filter = 2\n",
    "        returns = z_score_momentum_with_volatility_filter(ret, short_window, long_window, volatility_filter)\n",
    "        sharpe = performance_stats(returns)['SR']\n",
    "        results_list.append((short_window, long_window, sharpe))\n",
    "        return -sharpe\n",
    "\n",
    "    else:\n",
    "        return 5\n",
    "\n",
    "res = gp_minimize(objective,\n",
    "                  space,\n",
    "                  n_calls=100,\n",
    "                  random_state=0,\n",
    "                  n_initial_points=10,\n",
    "                  acq_func=\"EI\")\n",
    "\n",
    "# Convert the results to a DataFrame for easy manipulation\n",
    "results_df = pd.DataFrame(results_list, columns=['short_window', 'long_window','sharpe_ratio'])\n",
    "\n",
    "# Plot a 2D scatter plot with color representing the Sharpe ratio\n",
    "plt.scatter(results_df['short_window'], results_df['long_window'], c=results_df['sharpe_ratio'], cmap='viridis', s=100)\n",
    "plt.colorbar(label='Sharpe Ratio')\n",
    "plt.xlabel('Short Window')\n",
    "plt.ylabel('Long Window')\n",
    "plt.title('Bayesian Optimization Results - Sharpe Ratios')\n",
    "plt.show()\n",
    "\n",
    "# Best parameters\n",
    "print(\"Best short window:\", res.x[0])\n",
    "print(\"Best long window:\", res.x[1])\n",
    "\n",
    "# Best result\n",
    "print(\"Best Sharpe Ratio:\", -res.fun)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "bb86d6b6-1ba0-409d-9f58-e08340e45b1e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Z_score Momentum With Volatility Filter Strategy Results\n",
      "\n",
      "Sharpe Ratio: 1.2415\n",
      "Annualized Returns: 0.8011\n",
      "Volatility: 0.6453\n",
      "Daily Alpha: 0.002410\n",
      "Alpha: 1.4074\n",
      "Information Ratio: 1.3756\n",
      "T-stat: 3.0802\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Simple Z_score momentum results with optimized parameters and skipping first day\n",
    "momentum_with_volatility = z_score_momentum_with_volatility_filter(ret,30,450, 2)\n",
    "momentum_with_volatility.cumsum().plot()\n",
    "\n",
    "print(\"Z_score Momentum With Volatility Filter Strategy Results\\n\")\n",
    "stats = performance_stats(momentum_with_volatility)\n",
    "regression = regression_stats(momentum_with_volatility, btc_ret)\n",
    "print_performance_stats(stats)\n",
    "print_regression_stats(regression)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "e5bca193-2d57-4a77-aa2f-52110884f5fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Combine strategy and benchmark returns into a single DataFrame\n",
    "combined = pd.concat([momentum_with_volatility, btc_ret], axis=1)\n",
    "combined.columns = ['strat_ret', 'btc_ret']\n",
    "\n",
    "# Drop rows with missing values\n",
    "combined = combined.dropna()\n",
    "\n",
    "# Prepare data for regression\n",
    "X = sm.add_constant(combined['btc_ret'])  # Benchmark returns with constant\n",
    "Y = combined['strat_ret']                # Strategy returns\n",
    "\n",
    "# Ensure indices match and are sorted\n",
    "assert (X.index == Y.index).all(), \"Indices of X and Y do not match!\"\n",
    "assert X.index.is_monotonic_increasing, \"Indices are not sorted!\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b83c33c9-efc3-4ec4-a6b9-91bf8112386a",
   "metadata": {},
   "source": [
    "## Preliminary Findings\n",
    "The inverse volatility filter I applied seems to improve the strategy's sharpe ratio by trading the momentum strategy during periods of lower volatility. Below I test different volatility filter values to see which range works best for the inverse volatility filter\n",
    "\n",
    "**Findings: It seems applying a volatility filter results in slightly higher sharpe ratios (of ~1.2) than the strategy without volatility filters**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "8a83349e-c512-4511-a8f1-95947a4ba2d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{1: -0.7341520152547673,\n",
       " 2: -0.4420176636817225,\n",
       " 3: 0.17408385511421123,\n",
       " 4: 0.39537494862695055,\n",
       " 5: 0.3906089106467004,\n",
       " 6: 0.6030499541449025,\n",
       " 7: 0.5265170090581387,\n",
       " 8: 0.7164362151081451,\n",
       " 9: 0.8450096902009326,\n",
       " 10: 0.8037824122665829,\n",
       " 11: 1.0461339245424572,\n",
       " 12: 1.1161022019621543,\n",
       " 13: 1.1257596821088816,\n",
       " 14: 1.170666441249135,\n",
       " 15: 1.1556278420907677,\n",
       " 16: 1.1669551401082787,\n",
       " 17: 1.2311436074894095,\n",
       " 18: 1.26460131822454,\n",
       " 19: 1.2552453167899933,\n",
       " 20: 1.2438353470685997,\n",
       " 21: 1.1962122239333826,\n",
       " 22: 1.1827108156894672,\n",
       " 23: 1.2113931267122193,\n",
       " 24: 1.2106693980310537,\n",
       " 25: 1.207973168881344,\n",
       " 26: 1.215806689612254,\n",
       " 27: 1.1902179683894898,\n",
       " 28: 1.1669346415084307,\n",
       " 29: 1.1591039131302265}"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Testing different volatility thresholds\n",
    "vols = {}\n",
    "for i in range(1,30,1):\n",
    "    vols[i] = performance_stats(z_score_momentum_with_volatility_filter(ret, 30, 450, i*0.1))['SR']\n",
    "vols"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9745d158-fc49-478f-82c7-bdd828988bb7",
   "metadata": {},
   "source": [
    "## Additional Findings\n",
    "\n",
    "It seems that this momentum-based z-score strategy performed well before 2021 but has not shown great returns since 2021. Below I will implement a reversion strategy to see what kind of results it gives. Perhaps if it does well during the period after 2021, I can combine the 2 strategies to allocate money towards the strategy that is most effective during the regime at that time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f4b7fc0c-8461-4c7b-8745-b1d20b607308",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Z_score Momentum With Volatility Filter Strategy Results\n",
      "\n",
      "Sharpe Ratio: 1.2434\n",
      "Annualized Returns: 0.8026\n",
      "Volatility: 0.6455\n",
      "Alpha: 1.4114\n",
      "Information Ratio: 1.3779\n",
      "T-stat: 3.0853\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Example of z-score momentum with volatility_filter = 2\n",
    "momentum_with_volatility = z_score_momentum_with_volatility_filter(ret, 30, 450, 2)\n",
    "momentum_with_volatility.cumsum().plot()\n",
    "print(\"Z_score Momentum With Volatility Filter Strategy Results\\n\")\n",
    "stats = performance_stats(momentum_with_volatility)\n",
    "regression = regression_stats(momentum_with_volatility, btc_ret)\n",
    "print_performance_stats(stats)\n",
    "print_regression_stats(regression)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e1ed66f-86af-4e3a-9f77-d23f4f6d5f32",
   "metadata": {},
   "source": [
    "# Reversal Strategies\n",
    "\n",
    "Below is an implementation of a several mean reversion strategy that trade on daily crypto data. In these strategies, we use the residuals in order to remove the BTC component of the returns. I tested a z-score mean reversion strategy that uses normalized weights with a z-score threshold (sell if over the threshold and buy if under)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51514c4b-e884-4a5e-a0df-c2c0e54d6b51",
   "metadata": {},
   "source": [
    "## Reversal Backtest without T-costs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "dafbf52b-1630-42a8-b70a-94927dc870d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ETHUSDT</th>\n",
       "      <th>XRPUSDT</th>\n",
       "      <th>BCHUSDT</th>\n",
       "      <th>LTCUSDT</th>\n",
       "      <th>BNBUSDT</th>\n",
       "      <th>ADAUSDT</th>\n",
       "      <th>BATUSDT</th>\n",
       "      <th>ETCUSDT</th>\n",
       "      <th>XLMUSDT</th>\n",
       "      <th>ZRXUSDT</th>\n",
       "      <th>...</th>\n",
       "      <th>XTZUSDT</th>\n",
       "      <th>ZILUSDT</th>\n",
       "      <th>ORBSUSDT</th>\n",
       "      <th>CUDOSUSDT</th>\n",
       "      <th>ADXUSDT</th>\n",
       "      <th>FORTUSDT</th>\n",
       "      <th>SUIUSDT</th>\n",
       "      <th>ONGUSDT</th>\n",
       "      <th>GUSDT</th>\n",
       "      <th>RENDERUSDT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-09-23</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-24</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-25</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-26</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-27</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-09-27</th>\n",
       "      <td>0.014295</td>\n",
       "      <td>-0.006401</td>\n",
       "      <td>0.018907</td>\n",
       "      <td>0.032027</td>\n",
       "      <td>0.009232</td>\n",
       "      <td>-0.006988</td>\n",
       "      <td>-0.010506</td>\n",
       "      <td>0.023551</td>\n",
       "      <td>-0.000462</td>\n",
       "      <td>0.038851</td>\n",
       "      <td>...</td>\n",
       "      <td>0.017006</td>\n",
       "      <td>0.043025</td>\n",
       "      <td>0.012325</td>\n",
       "      <td>-0.009284</td>\n",
       "      <td>0.051553</td>\n",
       "      <td>0.029449</td>\n",
       "      <td>-0.001839</td>\n",
       "      <td>0.075339</td>\n",
       "      <td>0.035873</td>\n",
       "      <td>0.054847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-09-28</th>\n",
       "      <td>-0.008742</td>\n",
       "      <td>0.042373</td>\n",
       "      <td>-0.020326</td>\n",
       "      <td>-0.018605</td>\n",
       "      <td>-0.013266</td>\n",
       "      <td>-0.004386</td>\n",
       "      <td>-0.011091</td>\n",
       "      <td>-0.017652</td>\n",
       "      <td>0.009061</td>\n",
       "      <td>-0.039468</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.021414</td>\n",
       "      <td>-0.039687</td>\n",
       "      <td>-0.015459</td>\n",
       "      <td>-0.173067</td>\n",
       "      <td>-0.000436</td>\n",
       "      <td>-0.001883</td>\n",
       "      <td>0.015854</td>\n",
       "      <td>-0.100460</td>\n",
       "      <td>-0.019651</td>\n",
       "      <td>-0.025585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-09-29</th>\n",
       "      <td>-0.003688</td>\n",
       "      <td>0.046793</td>\n",
       "      <td>-0.000651</td>\n",
       "      <td>-0.005794</td>\n",
       "      <td>-0.004292</td>\n",
       "      <td>-0.006811</td>\n",
       "      <td>-0.008940</td>\n",
       "      <td>-0.010122</td>\n",
       "      <td>0.022130</td>\n",
       "      <td>0.022185</td>\n",
       "      <td>...</td>\n",
       "      <td>0.013672</td>\n",
       "      <td>0.010624</td>\n",
       "      <td>0.018195</td>\n",
       "      <td>0.164577</td>\n",
       "      <td>0.000214</td>\n",
       "      <td>0.017788</td>\n",
       "      <td>0.040630</td>\n",
       "      <td>0.054202</td>\n",
       "      <td>-0.002130</td>\n",
       "      <td>0.026264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-09-30</th>\n",
       "      <td>0.009118</td>\n",
       "      <td>-0.027610</td>\n",
       "      <td>-0.018551</td>\n",
       "      <td>-0.011375</td>\n",
       "      <td>-0.024361</td>\n",
       "      <td>-0.031376</td>\n",
       "      <td>-0.039429</td>\n",
       "      <td>-0.016657</td>\n",
       "      <td>-0.010707</td>\n",
       "      <td>-0.054327</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.027843</td>\n",
       "      <td>-0.070374</td>\n",
       "      <td>-0.006243</td>\n",
       "      <td>0.011349</td>\n",
       "      <td>0.008225</td>\n",
       "      <td>-0.014155</td>\n",
       "      <td>0.041161</td>\n",
       "      <td>-0.088211</td>\n",
       "      <td>-0.038440</td>\n",
       "      <td>-0.010687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-10-01</th>\n",
       "      <td>-0.027131</td>\n",
       "      <td>-0.005217</td>\n",
       "      <td>0.003682</td>\n",
       "      <td>-0.022891</td>\n",
       "      <td>-0.002431</td>\n",
       "      <td>-0.027703</td>\n",
       "      <td>-0.029729</td>\n",
       "      <td>-0.035060</td>\n",
       "      <td>-0.026814</td>\n",
       "      <td>-0.064473</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.026945</td>\n",
       "      <td>-0.050952</td>\n",
       "      <td>0.153290</td>\n",
       "      <td>0.012330</td>\n",
       "      <td>-0.099698</td>\n",
       "      <td>-0.031697</td>\n",
       "      <td>0.017319</td>\n",
       "      <td>-0.044081</td>\n",
       "      <td>-0.038884</td>\n",
       "      <td>-0.078906</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1836 rows × 169 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             ETHUSDT   XRPUSDT   BCHUSDT   LTCUSDT   BNBUSDT   ADAUSDT  \\\n",
       "2019-09-23       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "2019-09-24       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "2019-09-25       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "2019-09-26       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "2019-09-27       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "...              ...       ...       ...       ...       ...       ...   \n",
       "2024-09-27  0.014295 -0.006401  0.018907  0.032027  0.009232 -0.006988   \n",
       "2024-09-28 -0.008742  0.042373 -0.020326 -0.018605 -0.013266 -0.004386   \n",
       "2024-09-29 -0.003688  0.046793 -0.000651 -0.005794 -0.004292 -0.006811   \n",
       "2024-09-30  0.009118 -0.027610 -0.018551 -0.011375 -0.024361 -0.031376   \n",
       "2024-10-01 -0.027131 -0.005217  0.003682 -0.022891 -0.002431 -0.027703   \n",
       "\n",
       "             BATUSDT   ETCUSDT   XLMUSDT   ZRXUSDT  ...   XTZUSDT   ZILUSDT  \\\n",
       "2019-09-23       NaN       NaN       NaN       NaN  ...       NaN       NaN   \n",
       "2019-09-24       NaN       NaN       NaN       NaN  ...       NaN       NaN   \n",
       "2019-09-25       NaN       NaN       NaN       NaN  ...       NaN       NaN   \n",
       "2019-09-26       NaN       NaN       NaN       NaN  ...       NaN       NaN   \n",
       "2019-09-27       NaN       NaN       NaN       NaN  ...       NaN       NaN   \n",
       "...              ...       ...       ...       ...  ...       ...       ...   \n",
       "2024-09-27 -0.010506  0.023551 -0.000462  0.038851  ...  0.017006  0.043025   \n",
       "2024-09-28 -0.011091 -0.017652  0.009061 -0.039468  ... -0.021414 -0.039687   \n",
       "2024-09-29 -0.008940 -0.010122  0.022130  0.022185  ...  0.013672  0.010624   \n",
       "2024-09-30 -0.039429 -0.016657 -0.010707 -0.054327  ... -0.027843 -0.070374   \n",
       "2024-10-01 -0.029729 -0.035060 -0.026814 -0.064473  ... -0.026945 -0.050952   \n",
       "\n",
       "            ORBSUSDT  CUDOSUSDT   ADXUSDT  FORTUSDT   SUIUSDT   ONGUSDT  \\\n",
       "2019-09-23       NaN        NaN       NaN       NaN       NaN       NaN   \n",
       "2019-09-24       NaN        NaN       NaN       NaN       NaN       NaN   \n",
       "2019-09-25       NaN        NaN       NaN       NaN       NaN       NaN   \n",
       "2019-09-26       NaN        NaN       NaN       NaN       NaN       NaN   \n",
       "2019-09-27       NaN        NaN       NaN       NaN       NaN       NaN   \n",
       "...              ...        ...       ...       ...       ...       ...   \n",
       "2024-09-27  0.012325  -0.009284  0.051553  0.029449 -0.001839  0.075339   \n",
       "2024-09-28 -0.015459  -0.173067 -0.000436 -0.001883  0.015854 -0.100460   \n",
       "2024-09-29  0.018195   0.164577  0.000214  0.017788  0.040630  0.054202   \n",
       "2024-09-30 -0.006243   0.011349  0.008225 -0.014155  0.041161 -0.088211   \n",
       "2024-10-01  0.153290   0.012330 -0.099698 -0.031697  0.017319 -0.044081   \n",
       "\n",
       "               GUSDT  RENDERUSDT  \n",
       "2019-09-23       NaN         NaN  \n",
       "2019-09-24       NaN         NaN  \n",
       "2019-09-25       NaN         NaN  \n",
       "2019-09-26       NaN         NaN  \n",
       "2019-09-27       NaN         NaN  \n",
       "...              ...         ...  \n",
       "2024-09-27  0.035873    0.054847  \n",
       "2024-09-28 -0.019651   -0.025585  \n",
       "2024-09-29 -0.002130    0.026264  \n",
       "2024-09-30 -0.038440   -0.010687  \n",
       "2024-10-01 -0.038884   -0.078906  \n",
       "\n",
       "[1836 rows x 169 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate Residuals for mean reversion strategies - stripping out BTC component of returns, using mininum periods of 30 days to avoid\n",
    "# noise at the beginning\n",
    "def calc_resid(ret, btc_ret):\n",
    "    corr = ret.rolling(180, min_periods=30).corr(btc_ret)\n",
    "    vol = ret.rolling(180, min_periods=30).std()\n",
    "    beta = (corr*vol).divide(vol,axis=0)\n",
    "    resid = ret - beta.multiply(btc_ret,0)\n",
    "    return resid\n",
    "\n",
    "resid = calc_resid(ret, btc_ret)\n",
    "resid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "523f2eba-9290-402a-92e4-9494bcc15343",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Normalized Z-score mean reversion (without T-costs)\n",
    "def z_score_mean_reversion(ret, short_window, long_window, z_threshold=1):\n",
    "    # Z-score inputs\n",
    "    short_term_return = ret.rolling(short_window, min_periods=1).mean()\n",
    "    rolling_mean = ret.rolling(long_window, min_periods=1).mean()\n",
    "    rolling_std = ret.rolling(long_window, min_periods=1).std()\n",
    "\n",
    "    # Z-score calculation: no tanh applied since mean-reversion is looking for extremes\n",
    "    z_scores = (ret - rolling_mean) / rolling_std\n",
    "\n",
    "    # Generate signals: Buy when Z-score < -z_threshold (oversold), Sell when Z-score > z_threshold (overbought)\n",
    "    buy_signals = z_scores < -z_threshold\n",
    "    sell_signals = z_scores > z_threshold\n",
    "\n",
    "    # Combine buy and sell signals into a single DataFrame: 1 for Buy, -1 for Sell, 0 otherwise\n",
    "    signals = pd.DataFrame(0, index=ret.index, columns=ret.columns)\n",
    "    signals[buy_signals] = 1  # Buy signal\n",
    "    signals[sell_signals] = -1  # Sell signal\n",
    "\n",
    "    # Normalize signals to ensure full investment, but adjust total exposure based on the number of active assets\n",
    "    signal_sum = signals.abs().sum(axis=1)\n",
    "    num_active_signals = (signals != 0).sum(axis=1)\n",
    "    normalized_signals = signals.div(signal_sum, axis=0).fillna(0)\n",
    "\n",
    "    # Dynamically scale exposure based on number of active assets\n",
    "    scaled_signals = normalized_signals.multiply(num_active_signals / signal_sum.max(), axis=0)\n",
    "\n",
    "    # Calculate gross returns: Shift signals by 1 to avoid look-ahead bias (next-day return)\n",
    "    gross_return = (scaled_signals.shift(1) * ret).sum(axis=1)\n",
    "\n",
    "    \"\"\"\n",
    "    to = signals.diff().abs().sum(axis=1)\n",
    "\n",
    "    # Place limit orders (80% success) and market orders (20%)\n",
    "    limit_cost = 7\n",
    "    market_cost = 20\n",
    "    limit_fill_rate = 0.8\n",
    "    filled_at_limit = np.random.binomial(1, limit_fill_rate, len(to))  # Generate limit order fills\n",
    "    transaction_costs = np.where(filled_at_limit, to * limit_cost * 1e-4, to * market_cost * 1e-4)\n",
    "\n",
    "    # Calculate net returns after transaction costs\n",
    "    net_return = gross_return.subtract(transaction_costs, fill_value=0)\n",
    "    \"\"\"\n",
    "    # Returns gross returns to show strategy effectiveness before incorporating T-costs\n",
    "    return gross_return"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "ef4efd9c-a19c-4eb4-90cb-3c30702f7f2c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/BrianPlotnik/opt/anaconda3/envs/py38-env/lib/python3.8/site-packages/skopt/optimizer/optimizer.py:517: UserWarning: The objective has been evaluated at point [5, 100] before, using random point [78, 260]\n",
      "  warnings.warn(\n",
      "/Users/BrianPlotnik/opt/anaconda3/envs/py38-env/lib/python3.8/site-packages/skopt/optimizer/optimizer.py:517: UserWarning: The objective has been evaluated at point [6, 100] before, using random point [54, 248]\n",
      "  warnings.warn(\n",
      "/Users/BrianPlotnik/opt/anaconda3/envs/py38-env/lib/python3.8/site-packages/skopt/optimizer/optimizer.py:517: UserWarning: The objective has been evaluated at point [6, 103] before, using random point [31, 31]\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best short window: 7\n",
      "Best long window: 100\n",
      "Best Sharpe Ratio: 2.8035577032576042\n"
     ]
    }
   ],
   "source": [
    "# Bayesian Optimization for Reversal strategy parameters (rolling windows), using z-score threshold = 1 as starting point\n",
    "\n",
    "space  = [\n",
    "    Integer(1, 100, name='short_window'),  # Short window: between 1 and 100 days\n",
    "    Integer(1, 300, name='long_window'),  # Long window: between 1 and 300 days\n",
    "]\n",
    "\n",
    "results_list = []\n",
    "def objective(params):\n",
    "    short_window = params[0]\n",
    "    long_window = params[1]\n",
    "    \n",
    "    if long_window >= 2 * short_window:\n",
    "        returns = z_score_mean_reversion(resid, short_window, long_window, 1)\n",
    "        sharpe = performance_stats(returns)['SR']\n",
    "        results_list.append((short_window, long_window, sharpe))\n",
    "        return -sharpe\n",
    "\n",
    "    else:\n",
    "        return 5\n",
    "\n",
    "res = gp_minimize(objective,\n",
    "                  space,\n",
    "                  n_calls=100,\n",
    "                  random_state=0,\n",
    "                  n_initial_points=10,\n",
    "                  acq_func=\"EI\")\n",
    "\n",
    "# Convert the results to a DataFrame for easy manipulation\n",
    "results_df = pd.DataFrame(results_list, columns=['short_window', 'long_window','sharpe_ratio'])\n",
    "\n",
    "# Plot a 2D scatter plot with color representing the Sharpe ratio\n",
    "plt.scatter(results_df['short_window'], results_df['long_window'], c=results_df['sharpe_ratio'], cmap='viridis', s=100)\n",
    "plt.colorbar(label='Sharpe Ratio')\n",
    "plt.xlabel('Short Window')\n",
    "plt.ylabel('Long Window')\n",
    "plt.title('Bayesian Optimization Results - Sharpe Ratios')\n",
    "plt.show()\n",
    "\n",
    "# Best parameters\n",
    "print(\"Best short window:\", res.x[0])\n",
    "print(\"Best long window:\", res.x[1])\n",
    "\n",
    "# Best result\n",
    "print(\"Best Sharpe Ratio:\", -res.fun)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "42e2585f-242d-4fd2-97dc-a67d93cf803c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Z_score Reversion Strategy Results (no T-Costs)\n",
      "\n",
      "Sharpe Ratio: 2.8036\n",
      "Annualized Returns: 0.4058\n",
      "Volatility: 0.1447\n",
      "Alpha: 0.4782\n",
      "Information Ratio: 2.7197\n",
      "T-stat: 6.09\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "z_score_mean_reversion_results = z_score_mean_reversion(resid, 7, 100, 1)\n",
    "portfolio_returns.cumsum().plot()\n",
    "print(\"Z_score Reversion Strategy Results (no T-Costs)\\n\")\n",
    "stats = performance_stats(z_score_mean_reversion_results)\n",
    "regression = regression_stats(z_score_mean_reversion_results, btc_ret)\n",
    "print_performance_stats(stats)\n",
    "print_regression_stats(regression)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2db1820-085c-4428-89a4-65ac8481ae95",
   "metadata": {},
   "source": [
    "## Reversal Strategy with T-costs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "e3f94dea-b6b6-4fd6-87b5-ff1f076bc555",
   "metadata": {},
   "outputs": [],
   "source": [
    "def z_score_mean_reversion_net(ret, short_window, long_window, z_threshold=1):\n",
    "    # Z-score inputs\n",
    "    short_term_return = ret.rolling(short_window, min_periods=1).mean()\n",
    "    rolling_mean = ret.rolling(long_window, min_periods=1).mean()\n",
    "    rolling_std = ret.rolling(long_window, min_periods=1).std()\n",
    "\n",
    "    # Z-score calculation: no tanh applied since mean-reversion is looking for extremes\n",
    "    z_scores = (ret - rolling_mean) / rolling_std\n",
    "\n",
    "    # Generate signals: Buy when Z-score < -z_threshold (oversold), Sell when Z-score > z_threshold (overbought)\n",
    "    buy_signals = z_scores < -z_threshold\n",
    "    sell_signals = z_scores > z_threshold\n",
    "\n",
    "    # Combine buy and sell signals into a single DataFrame: 1 for Buy, -1 for Sell, 0 otherwise\n",
    "    signals = pd.DataFrame(0, index=ret.index, columns=ret.columns)\n",
    "    signals[buy_signals] = 1  # Buy signal\n",
    "    signals[sell_signals] = -1  # Sell signal\n",
    "\n",
    "    # Normalize signals to ensure full investment, but adjust total exposure based on the number of active assets\n",
    "    signal_sum = signals.abs().sum(axis=1)\n",
    "    num_active_signals = (signals != 0).sum(axis=1)\n",
    "    normalized_signals = signals.div(signal_sum, axis=0).fillna(0)\n",
    "\n",
    "    # Dynamically scale exposure based on number of active assets\n",
    "    scaled_signals = normalized_signals.multiply(num_active_signals / signal_sum.max(), axis=0)\n",
    "\n",
    "    # Calculate gross returns: Shift signals by 1 to avoid look-ahead bias (next-day return)\n",
    "    gross_return = (scaled_signals.shift(1) * ret).sum(axis=1)\n",
    "\n",
    "    to = scaled_signals.diff().abs().sum(axis=1)\n",
    "    \n",
    "    # Place limit orders (80% success) and market orders (20%)\n",
    "    limit_cost = 7\n",
    "    market_cost = 20\n",
    "    limit_fill_rate = 0.8\n",
    "    filled_at_limit = np.random.binomial(1, limit_fill_rate, len(to))  # Generate limit order fills\n",
    "    transaction_costs = np.where(filled_at_limit, to * limit_cost * 1e-4, to * market_cost * 1e-4)\n",
    "\n",
    "    # Calculate net returns after transaction costs\n",
    "    net_return = gross_return.subtract(transaction_costs, fill_value=0)\n",
    "    return net_return"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "78bfb1ab-63f9-4770-a198-d9d132d3d5bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best short window: 65\n",
      "Best long window: 309\n",
      "Best Sharpe Ratio: 2.3114332041517818\n"
     ]
    }
   ],
   "source": [
    "# Bayesian Optimization for Reversal strategy parameters (rolling windows), using z-score threshold = 1.5 as starting point\n",
    "\n",
    "space  = [\n",
    "    Integer(1, 200, name='short_window'),  # Short window: between 1 and 200 days\n",
    "    Integer(1, 400, name='long_window'),  # Long window: between 1 and 400 days\n",
    "]\n",
    "\n",
    "results_list = []\n",
    "def objective(params):\n",
    "    short_window = params[0]\n",
    "    long_window = params[1]\n",
    "    \n",
    "    if long_window >= 2 * short_window:\n",
    "        returns = z_score_mean_reversion_net(resid, short_window, long_window, 1)\n",
    "        sharpe = performance_stats(returns)['SR']\n",
    "        results_list.append((short_window, long_window, sharpe))\n",
    "        return -sharpe\n",
    "\n",
    "    else:\n",
    "        return 5\n",
    "\n",
    "res = gp_minimize(objective,\n",
    "                  space,\n",
    "                  n_calls=100,\n",
    "                  random_state=0,\n",
    "                  n_initial_points=10,\n",
    "                  acq_func=\"EI\")\n",
    "\n",
    "# Convert the results to a DataFrame for easy manipulation\n",
    "results_df = pd.DataFrame(results_list, columns=['short_window', 'long_window','sharpe_ratio'])\n",
    "\n",
    "# Plot a 2D scatter plot with color representing the Sharpe ratio\n",
    "plt.scatter(results_df['short_window'], results_df['long_window'], c=results_df['sharpe_ratio'], cmap='viridis', s=100)\n",
    "plt.colorbar(label='Sharpe Ratio')\n",
    "plt.xlabel('Short Window')\n",
    "plt.ylabel('Long Window')\n",
    "plt.title('Bayesian Optimization Results - Sharpe Ratios')\n",
    "plt.show()\n",
    "\n",
    "# Best parameters\n",
    "print(\"Best short window:\", res.x[0])\n",
    "print(\"Best long window:\", res.x[1])\n",
    "\n",
    "# Best result\n",
    "print(\"Best Sharpe Ratio:\", -res.fun)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "2567582c-8e41-425d-b4c4-0f8f18150a96",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sharpe Ratio: 2.2693\n",
      "Annualized Returns: 0.319\n",
      "Volatility: 0.1406\n",
      "Alpha: 0.3551\n",
      "Information Ratio: 2.1783\n",
      "T-stat: 4.8776\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mean_reversion_net = z_score_mean_reversion_net(resid, 100, 300, 1)\n",
    "mean_reversion_net.cumsum().plot()\n",
    "stats = performance_stats(mean_reversion_net)\n",
    "regression = regression_stats(mean_reversion_net, btc_ret)\n",
    "print_performance_stats(stats)\n",
    "print_regression_stats(regression)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1d410b3-4ce8-41a7-868e-ea9e76e33cfd",
   "metadata": {},
   "source": [
    "# Combining Strategies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "ca35496f-63fd-4279-85e4-e5fa9769903e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Return_momentum</th>\n",
       "      <th>Return_mean_reversion</th>\n",
       "      <th>Combined_Return</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-09-23</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-24</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-25</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-26</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-27</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-09-27</th>\n",
       "      <td>0.004274</td>\n",
       "      <td>-0.000428</td>\n",
       "      <td>0.001923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-09-28</th>\n",
       "      <td>-0.019052</td>\n",
       "      <td>0.005163</td>\n",
       "      <td>-0.006944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-09-29</th>\n",
       "      <td>0.010533</td>\n",
       "      <td>0.005846</td>\n",
       "      <td>0.008189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-09-30</th>\n",
       "      <td>-0.043668</td>\n",
       "      <td>-0.001917</td>\n",
       "      <td>-0.022792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-10-01</th>\n",
       "      <td>-0.065352</td>\n",
       "      <td>-0.014528</td>\n",
       "      <td>-0.039940</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1836 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Return_momentum  Return_mean_reversion  Combined_Return\n",
       "2019-09-23         0.000000               0.000000         0.000000\n",
       "2019-09-24         0.000000               0.000000         0.000000\n",
       "2019-09-25         0.000000               0.000000         0.000000\n",
       "2019-09-26         0.000000               0.000000         0.000000\n",
       "2019-09-27         0.000000               0.000000         0.000000\n",
       "...                     ...                    ...              ...\n",
       "2024-09-27         0.004274              -0.000428         0.001923\n",
       "2024-09-28        -0.019052               0.005163        -0.006944\n",
       "2024-09-29         0.010533               0.005846         0.008189\n",
       "2024-09-30        -0.043668              -0.001917        -0.022792\n",
       "2024-10-01        -0.065352              -0.014528        -0.039940\n",
       "\n",
       "[1836 rows x 3 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined_df = pd.concat([momentum_with_volatility, mean_reversion_net], axis=1)\n",
    "combined_df.columns = ['Return_momentum', 'Return_mean_reversion']\n",
    "\n",
    "# Step 2: Calculate the combined 50/50 returns\n",
    "combined_df['Combined_Return'] = 0.5 * combined_df['Return_momentum'] + 0.5 * combined_df['Return_mean_reversion']\n",
    "combined_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "c67725f9-ce48-4652-af1b-581924c29d94",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sharpe Ratio: 1.7108\n",
      "Annualized Returns: 0.5608\n",
      "Volatility: 0.3278\n",
      "Alpha: 0.8079\n",
      "Information Ratio: 1.8182\n",
      "T-stat: 4.0712\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "combined_df.cumsum().plot()\n",
    "stats = performance_stats(combined_df['Combined_Return'])\n",
    "regression = regression_stats(combined_df['Combined_Return'], btc_ret)\n",
    "print_performance_stats(stats)\n",
    "print_regression_stats(regression)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c1825dc-d84f-4880-8868-0e49f4d062fd",
   "metadata": {},
   "source": [
    "# Findings and Next Steps\n",
    "\n",
    "I was able to successfully backtest momentum and mean reversion strategy on daily crypto price data. Both of these strategies displayed alpha over the market (BTC returns) and quite good Sharpe Ratios. I was able to create a combined portfolio that invest half of its capital in each strategy and this combined strategy has a sharpe ratio of ~1.7 generating consistent returns.\n",
    "\n",
    "In order to improve these strategies I would like to explore dynamic portfolio allocation between the momentum and mean reversion strategies to more effectively allocate capital to the strategy that is expected to perform best during a given market regime. I will also continue to explore different signals like RSI signals and bollinger bands to see if these signals provide better results in the crypto market.\n",
    "\n",
    "Additionally, my current implementation uses the entire dataset for parameter testing. I would like to explore walk-forward analysis and corss-validation to see if the strategy holds up out of sample."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec933e26-f0a8-4d18-87ae-864db2dec7ae",
   "metadata": {},
   "source": [
    "# Work in Progress Signals"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1130f41e-4069-4ceb-9b0e-845bf315be63",
   "metadata": {},
   "source": [
    "### Simple Z-score reversal (using raw z-score for weights)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "539ae84f-12af-4119-97f5-23f23cbc7428",
   "metadata": {},
   "outputs": [],
   "source": [
    "def simple_reversal_zscore(resid, short_window, long_window):\n",
    "    short_term_return = resid.rolling(short_window, min_periods=1).mean()\n",
    "    rolling_mean = resid.rolling(long_window, min_periods=1).mean()\n",
    "    rolling_std = resid.rolling(long_window, min_periods=1).std()\n",
    "\n",
    "    # Z-score calculation: no tanh applied since mean-reversion is looking for extremes\n",
    "    z_scores = (short_term_return - rolling_mean) / rolling_std\n",
    "    gross_return = (-z_scores.shift(1) * resid).sum(axis=1)\n",
    "    return gross_return"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "3aac226d-a6f5-414c-acef-8af6fe54a5e0",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/BrianPlotnik/opt/anaconda3/envs/py38-env/lib/python3.8/site-packages/skopt/optimizer/optimizer.py:517: UserWarning: The objective has been evaluated at point [1, 28] before, using random point [37, 190]\n",
      "  warnings.warn(\n",
      "/Users/BrianPlotnik/opt/anaconda3/envs/py38-env/lib/python3.8/site-packages/skopt/optimizer/optimizer.py:517: UserWarning: The objective has been evaluated at point [1, 28] before, using random point [19, 174]\n",
      "  warnings.warn(\n",
      "/Users/BrianPlotnik/opt/anaconda3/envs/py38-env/lib/python3.8/site-packages/skopt/optimizer/optimizer.py:517: UserWarning: The objective has been evaluated at point [1, 28] before, using random point [66, 137]\n",
      "  warnings.warn(\n",
      "/Users/BrianPlotnik/opt/anaconda3/envs/py38-env/lib/python3.8/site-packages/skopt/optimizer/optimizer.py:517: UserWarning: The objective has been evaluated at point [1, 28] before, using random point [74, 201]\n",
      "  warnings.warn(\n",
      "/Users/BrianPlotnik/opt/anaconda3/envs/py38-env/lib/python3.8/site-packages/skopt/optimizer/optimizer.py:517: UserWarning: The objective has been evaluated at point [1, 28] before, using random point [76, 224]\n",
      "  warnings.warn(\n",
      "/Users/BrianPlotnik/opt/anaconda3/envs/py38-env/lib/python3.8/site-packages/skopt/optimizer/optimizer.py:517: UserWarning: The objective has been evaluated at point [1, 28] before, using random point [11, 40]\n",
      "  warnings.warn(\n",
      "/Users/BrianPlotnik/opt/anaconda3/envs/py38-env/lib/python3.8/site-packages/skopt/optimizer/optimizer.py:517: UserWarning: The objective has been evaluated at point [1, 28] before, using random point [47, 65]\n",
      "  warnings.warn(\n",
      "/Users/BrianPlotnik/opt/anaconda3/envs/py38-env/lib/python3.8/site-packages/skopt/optimizer/optimizer.py:517: UserWarning: The objective has been evaluated at point [1, 28] before, using random point [19, 220]\n",
      "  warnings.warn(\n",
      "/Users/BrianPlotnik/opt/anaconda3/envs/py38-env/lib/python3.8/site-packages/skopt/optimizer/optimizer.py:517: UserWarning: The objective has been evaluated at point [1, 28] before, using random point [37, 128]\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best short window: 1\n",
      "Best long window: 29\n",
      "Best Sharpe Ratio: 2.3638897760733024\n"
     ]
    }
   ],
   "source": [
    "# Bayesian Optimization for Reversal strategy parameters (rolling windows), using z-score threshold = 1 as starting point\n",
    "\n",
    "space  = [\n",
    "    Integer(1, 100, name='short_window'),  # Short window: between 1 and 100 days\n",
    "    Integer(1, 300, name='long_window'),  # Long window: between 1 and 300 days\n",
    "]\n",
    "\n",
    "results_list = []\n",
    "def objective(params):\n",
    "    short_window = params[0]\n",
    "    long_window = params[1]\n",
    "    \n",
    "    if long_window >= 2 * short_window:\n",
    "        returns = simple_reversal_zscore(resid, short_window, long_window)\n",
    "        sharpe = performance_stats(returns)['SR']\n",
    "        results_list.append((short_window, long_window, sharpe))\n",
    "        return -sharpe\n",
    "\n",
    "    else:\n",
    "        return 5\n",
    "\n",
    "res = gp_minimize(objective,\n",
    "                  space,\n",
    "                  n_calls=100,\n",
    "                  random_state=0,\n",
    "                  n_initial_points=10,\n",
    "                  acq_func=\"EI\")\n",
    "\n",
    "# Convert the results to a DataFrame for easy manipulation\n",
    "results_df = pd.DataFrame(results_list, columns=['short_window', 'long_window','sharpe_ratio'])\n",
    "\n",
    "# Plot a 2D scatter plot with color representing the Sharpe ratio\n",
    "plt.scatter(results_df['short_window'], results_df['long_window'], c=results_df['sharpe_ratio'], cmap='viridis', s=100)\n",
    "plt.colorbar(label='Sharpe Ratio')\n",
    "plt.xlabel('Short Window')\n",
    "plt.ylabel('Long Window')\n",
    "plt.title('Bayesian Optimization Results - Sharpe Ratios')\n",
    "plt.show()\n",
    "\n",
    "# Best parameters\n",
    "print(\"Best short window:\", res.x[0])\n",
    "print(\"Best long window:\", res.x[1])\n",
    "\n",
    "# Best result\n",
    "print(\"Best Sharpe Ratio:\", -res.fun)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "2cc362d7-fcd6-4735-8509-f2c5539f96eb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Simple Z_score Reversion Strategy Results\n",
      "\n",
      "Sharpe Ratio: 2.3639\n",
      "Annualized Returns: 158.2541\n",
      "Volatility: 66.9465\n",
      "Alpha: 0.4207\n",
      "Information Ratio: 2.3005\n",
      "T-stat: 5.1513\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "portfolio_returns = simple_reversal_zscore(resid, 1, 29)\n",
    "portfolio_returns.cumsum().plot()\n",
    "print(\"Simple Z_score Reversion Strategy Results\\n\")\n",
    "stats = performance_stats(portfolio_returns)\n",
    "regression = regression_stats(portfolio_returns, btc_ret)\n",
    "print_performance_stats(stats)\n",
    "print_regression_stats(regression)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dd0e243c-34b9-4496-b145-aaa821b691e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# RSI Signal Generator\n",
    "def rsi_signal(ret, hor=14):\n",
    "    delta = ret.diff()\n",
    "    gain = (delta.where(delta > 0, 0)).rolling(hor, min_periods=1).mean()\n",
    "    loss = (-delta.where(delta < 0, 0)).rolling(hor, min_periods=1).mean()\n",
    "    rs = gain / loss\n",
    "    rsi = 100 - (100 / (1+rs))\n",
    "\n",
    "    rsi_long = (30 - rsi).clip(lower=0) / 30\n",
    "    rsi_short = (rsi - 70).clip(lower=0) / 30\n",
    "    weights = rsi_long - rsi_short\n",
    "    gross_return = (weights.shift()*ret).sum(1)\n",
    "\n",
    "    #Calculate Net Returns\n",
    "    #to = (weights.fillna(0) - weights.shift().fillna(0)).abs().sum()\n",
    "    to = 0\n",
    "    net_return = gross_return.subtract(to*7*1e-4,fill_value=0)\n",
    "    return net_return"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fadd0b86-e251-49cc-a73a-623547489055",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Z-score Signal Generator\n",
    "def z_score_reversal(ret, short_window, long_window):\n",
    "    # Z-score inputs\n",
    "    short_term_return = ret.rolling(short_window,min_periods=1).mean()\n",
    "    rolling_mean = ret.rolling(long_window, min_periods=1).mean()\n",
    "    rolling_std = ret.rolling(long_window, min_periods=1).std()\n",
    "\n",
    "    # Z-score calculation - code below calculates z-scores and takes the tanh of the results in order to smoothen outliers\n",
    "    z_scores = (short_term_return - rolling_mean) / rolling_std\n",
    "    z_scores = -np.tanh(z_scores)\n",
    "    z_scores = z_scores.div(z_scores.abs().sum(axis=1),axis=0)\n",
    "\n",
    "    # Gross return calculation\n",
    "    gross_return = (z_scores.shift()*ret).sum(1)\n",
    "\n",
    "    # Calculate turnover and net returns (minus T-costs)\n",
    "    # Place limit orders (7 bps) with a success rate of 80% and then the remaining 20% are filled as market orders (20 bps)\n",
    "    limit_cost = 7\n",
    "    market_cost = 20\n",
    "    limit_fill_rate = 0.8\n",
    "    to = (z_scores.fillna(0) - z_scores.shift().fillna(0)).abs().sum(axis=1)\n",
    "    filled_at_limit = np.random.binomial(1, limit_fill_rate, len(to))\n",
    "    transaction_costs = np.where(filled_at_limit, to * limit_cost * 1e-4, to * market_cost * 1e-4)\n",
    "    net_return = gross_return.subtract(transaction_costs,fill_value=0)\n",
    "    return net_return"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0eaebf76-dbd5-4277-be36-bf264fc45054",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Rank Demeaned Normalized Reversal Signal Generator\n",
    "def rank_demeaned_normalized(ret, hor):\n",
    "    avg_ret = (-1*ret.rolling(hor,min_periods=1).mean()).rank(1)\n",
    "    avg_ret = avg_ret.subtract(avg_ret.mean(1),0)\n",
    "    avg_ret = avg_ret.divide(avg_ret.abs().sum(1),0)\n",
    "    #to = (avg_ret.fillna(0) - avg_ret.shift().fillna(0)).abs().sum()\n",
    "    to = 0\n",
    "    gross_ret = (avg_ret.shift()*ret).sum(1)\n",
    "    net_ret = gross_ret.subtract(to*20*1e-4,fill_value=0)\n",
    "    return net_ret"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79926c9c-a505-4997-97ef-28b810094bbc",
   "metadata": {},
   "source": [
    "## Bollinger Bands"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "ddf4ea41-e936-4c76-982e-3aee31a0b03c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BTCUSDT</th>\n",
       "      <th>ETHUSDT</th>\n",
       "      <th>ADAUSDT</th>\n",
       "      <th>BNBUSDT</th>\n",
       "      <th>XRPUSDT</th>\n",
       "      <th>DOTUSDT</th>\n",
       "      <th>MATICUSDT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-09-23 08:00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-23 12:00:00</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-23 16:00:00</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-23 20:00:00</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-24 00:00:00</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-08-31 08:00:00</th>\n",
       "      <td>-0.001016</td>\n",
       "      <td>0.001610</td>\n",
       "      <td>0.000574</td>\n",
       "      <td>-0.001495</td>\n",
       "      <td>0.003705</td>\n",
       "      <td>-0.000932</td>\n",
       "      <td>-0.003298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-08-31 12:00:00</th>\n",
       "      <td>-0.001008</td>\n",
       "      <td>-0.002875</td>\n",
       "      <td>-0.000287</td>\n",
       "      <td>-0.001123</td>\n",
       "      <td>-0.005098</td>\n",
       "      <td>-0.005361</td>\n",
       "      <td>-0.002127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-08-31 16:00:00</th>\n",
       "      <td>-0.003572</td>\n",
       "      <td>-0.008049</td>\n",
       "      <td>-0.004878</td>\n",
       "      <td>-0.003373</td>\n",
       "      <td>0.002297</td>\n",
       "      <td>-0.007734</td>\n",
       "      <td>-0.011843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-08-31 20:00:00</th>\n",
       "      <td>0.002695</td>\n",
       "      <td>0.006041</td>\n",
       "      <td>-0.006344</td>\n",
       "      <td>-0.000752</td>\n",
       "      <td>-0.002115</td>\n",
       "      <td>0.004015</td>\n",
       "      <td>0.005034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-09-01 00:00:00</th>\n",
       "      <td>-0.007274</td>\n",
       "      <td>-0.009725</td>\n",
       "      <td>0.000290</td>\n",
       "      <td>-0.013358</td>\n",
       "      <td>-0.009186</td>\n",
       "      <td>-0.008469</td>\n",
       "      <td>-0.011686</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10829 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      BTCUSDT   ETHUSDT   ADAUSDT   BNBUSDT   XRPUSDT  \\\n",
       "2019-09-23 08:00:00       NaN       NaN       NaN       NaN       NaN   \n",
       "2019-09-23 12:00:00  0.000000  0.000000       NaN  0.000000  0.000000   \n",
       "2019-09-23 16:00:00  0.000000  0.000000       NaN  0.000000  0.000000   \n",
       "2019-09-23 20:00:00  0.000000  0.000000       NaN  0.000000  0.000000   \n",
       "2019-09-24 00:00:00  0.000000  0.000000       NaN  0.000000  0.000000   \n",
       "...                       ...       ...       ...       ...       ...   \n",
       "2024-08-31 08:00:00 -0.001016  0.001610  0.000574 -0.001495  0.003705   \n",
       "2024-08-31 12:00:00 -0.001008 -0.002875 -0.000287 -0.001123 -0.005098   \n",
       "2024-08-31 16:00:00 -0.003572 -0.008049 -0.004878 -0.003373  0.002297   \n",
       "2024-08-31 20:00:00  0.002695  0.006041 -0.006344 -0.000752 -0.002115   \n",
       "2024-09-01 00:00:00 -0.007274 -0.009725  0.000290 -0.013358 -0.009186   \n",
       "\n",
       "                      DOTUSDT  MATICUSDT  \n",
       "2019-09-23 08:00:00       NaN        NaN  \n",
       "2019-09-23 12:00:00       NaN        NaN  \n",
       "2019-09-23 16:00:00       NaN        NaN  \n",
       "2019-09-23 20:00:00       NaN        NaN  \n",
       "2019-09-24 00:00:00       NaN        NaN  \n",
       "...                       ...        ...  \n",
       "2024-08-31 08:00:00 -0.000932  -0.003298  \n",
       "2024-08-31 12:00:00 -0.005361  -0.002127  \n",
       "2024-08-31 16:00:00 -0.007734  -0.011843  \n",
       "2024-08-31 20:00:00  0.004015   0.005034  \n",
       "2024-09-01 00:00:00 -0.008469  -0.011686  \n",
       "\n",
       "[10829 rows x 7 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Collect Data\n",
    "client = bnb_client(tld='US') # if you're in the US, please use: client=bnb_client(tld='US')'\n",
    "univ = ['BTCUSDT','ETHUSDT','ADAUSDT','BNBUSDT','XRPUSDT','DOTUSDT','MATICUSDT']\n",
    "\n",
    "freq = '4h'\n",
    "px = {}\n",
    "for x in univ:\n",
    "    data = get_binance_px(x,freq)\n",
    "    px[x] = data.set_index('open_time')['close']\n",
    "\n",
    "px = pd.DataFrame(px).astype(float)\n",
    "px = px.reindex(pd.date_range(px.index[0],px.index[-1],freq=freq))\n",
    "ret = px.pct_change()\n",
    "ret"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "0d749ca4-5893-47e6-b21e-98099e1a0418",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Bollinger Bands\n",
    "def bollinger_bands(px, window, num_std):\n",
    "    df = {}\n",
    "    rolling_mean = px.rolling(window=window, min_periods=1).mean()\n",
    "    rolling_std = px.rolling(window=window, min_periods=1).std()\n",
    "    upper_band = rolling_mean + (rolling_std * num_std)\n",
    "    lower_band = rolling_mean - (rolling_std * num_std)\n",
    "\n",
    "    return rolling_mean, upper_band, lower_band"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "7ae97a1f-d526-40ca-b2f0-93a9869f9bee",
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_filters(df, window_ma, window_adx, window_atr):\n",
    "    # Moving Average (Trend Filter) for each coin\n",
    "    ma = df.rolling(window=window_ma, min_periods=1).mean()\n",
    "\n",
    "    # ATR using absolute price changes for each coin\n",
    "    price_diff = df.diff().abs()\n",
    "    atr = price_diff.rolling(window=window_atr, min_periods=1).mean()\n",
    "\n",
    "    # Calculate the \"directional movement\" for ADX using single price changes for each coin\n",
    "    plus_dm = df.diff().clip(lower=0)  # Upward movement\n",
    "    minus_dm = (-df.diff()).clip(lower=0)  # Downward movement\n",
    "\n",
    "    plus_di = 100 * (plus_dm.rolling(window=window_adx).mean() / atr)\n",
    "    minus_di = 100 * (minus_dm.rolling(window=window_adx).mean() / atr)\n",
    "\n",
    "    dx = (np.abs(plus_di - minus_di) / (plus_di + minus_di)) * 100\n",
    "    adx = dx.rolling(window=window_adx).mean()\n",
    "\n",
    "    return ma, atr, adx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "cd877585-4502-4742-a9dd-b1f12847cc8e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_signals(df, upper_band, lower_band, middle_band, ma, atr, adx, atr_threshold=1, adx_threshold=20):\n",
    "    signals = pd.DataFrame(index=df.index, columns=df.columns)\n",
    "\n",
    "    # Trend Filter: Only buy when price is above the moving average, and sell when below for each coin\n",
    "    buy_condition = (df > upper_band) & (df > ma)\n",
    "    sell_condition = (df < lower_band) & (df < ma)\n",
    "\n",
    "    # ADX Filter: Only trade when the ADX is above a threshold for each coin\n",
    "    strong_trend_condition = adx > adx_threshold\n",
    "\n",
    "    # ATR Filter: Only trade when ATR is above a minimum threshold for each coin\n",
    "    volatile_market_condition = atr > atr_threshold\n",
    "\n",
    "    # Combine conditions: Only trade when all filters are satisfied for each coin\n",
    "    signals[buy_condition & strong_trend_condition & volatile_market_condition] = 1  # Buy Signal\n",
    "    signals[sell_condition & strong_trend_condition & volatile_market_condition] = -1  # Sell Signal\n",
    "\n",
    "    # Forward fill the signals to maintain positions until the next signal for each coin\n",
    "    signals = signals.fillna(0).replace(0, method='ffill')\n",
    "\n",
    "    return signals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "767a3eaa-028f-4c82-8879-eb1a6a83fa5b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def normalize_weights(signals):\n",
    "    weights = signals.div(signals.abs().sum(axis=1), axis=0)\n",
    "    return weights.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "c0c0cb94-8097-416d-8687-50d8c1bc8e6b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def backtest(ret, weights):\n",
    "    portfolio_returns = (weights.shift(1) * ret).sum(axis=1)\n",
    "    return portfolio_returns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "3cce1122-82b9-44b7-b06c-d07a886b3f67",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.133076418480648"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rolling_mean, upper_band, lower_band = bollinger_bands(px,15,1)\n",
    "ma, atr, adx = calculate_filters(px, window_ma=50, window_adx=14, window_atr=14)\n",
    "signals = generate_signals(px, upper_band, lower_band, rolling_mean, ma, atr, adx, atr_threshold=1, adx_threshold=5)\n",
    "weights = normalize_weights(signals)\n",
    "portfolio_returns = backtest(ret, weights)\n",
    "portfolio_returns.cumsum().plot()\n",
    "sharpe = portfolio_returns.mean() / portfolio_returns.std() * np.sqrt(365*6)\n",
    "sharpe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "f83807bb-074b-4d4c-910d-fd9f934d4b2a",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'get_stats' 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[66], line 28\u001b[0m\n\u001b[1;32m     25\u001b[0m short_window_range \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;241m3\u001b[39m, \u001b[38;5;241m50\u001b[39m)  \u001b[38;5;66;03m# Test short windows from 3 to 50 days\u001b[39;00m\n\u001b[1;32m     26\u001b[0m long_window_range \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;241m20\u001b[39m, \u001b[38;5;241m200\u001b[39m)  \u001b[38;5;66;03m# Test long windows from 20 to 200 days\u001b[39;00m\n\u001b[0;32m---> 28\u001b[0m best_combination, sharpe, results_df \u001b[38;5;241m=\u001b[39m \u001b[43mgrid_search\u001b[49m\u001b[43m(\u001b[49m\u001b[43mret\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mshort_window_range\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlong_window_range\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     30\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mBest combination of short and long windows:\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m     31\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mShort Window:\u001b[39m\u001b[38;5;124m\"\u001b[39m, best_combination[\u001b[38;5;241m0\u001b[39m])\n",
      "Cell \u001b[0;32mIn[66], line 13\u001b[0m, in \u001b[0;36mgrid_search\u001b[0;34m(signals, short_window_range, long_window_range)\u001b[0m\n\u001b[1;32m     11\u001b[0m returns \u001b[38;5;241m=\u001b[39m z_score_momentum(ret, short_window, long_window)\n\u001b[1;32m     12\u001b[0m cum_returns \u001b[38;5;241m=\u001b[39m returns\u001b[38;5;241m.\u001b[39mcumsum()\u001b[38;5;241m.\u001b[39miloc[\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m]\n\u001b[0;32m---> 13\u001b[0m sharpe \u001b[38;5;241m=\u001b[39m \u001b[43mget_stats\u001b[49m(returns)[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSR\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[1;32m     15\u001b[0m results\u001b[38;5;241m.\u001b[39mappend((short_window, long_window, sharpe, cum_returns))\n\u001b[1;32m     17\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m sharpe \u001b[38;5;241m>\u001b[39m best_sharpe:\n",
      "\u001b[0;31mNameError\u001b[0m: name 'get_stats' is not defined"
     ]
    }
   ],
   "source": [
    "# DO NOT RUN - Optimizing Z-Score Momentum Parameters (Brute force) - this method took too long given it is O(n^2) so I opted for \n",
    "# Bayesian Optimization instead\n",
    "def precompute_rolling_means(data, window_range):\n",
    "    rolling_means = {}\n",
    "    for window in window_range:\n",
    "        rolling_means[window] = data.rolling(window=window).mean()\n",
    "    return rolling_means\n",
    "\n",
    "def precompute_rolling_std(data, window_range):\n",
    "    rolling_stds = {}\n",
    "    for window in window_range:\n",
    "        rolling_stds[window] = data.rolling(window=window).std()\n",
    "    return rolling_stds\n",
    "\n",
    "def grid_search(signals, short_window_range, long_window_range):\n",
    "    best_sharpe = -np.inf\n",
    "    best_combination = None\n",
    "    results = []\n",
    "\n",
    "    precomputed_returns = {}\n",
    "    for short_window in short_window_range:\n",
    "        precomputed_returns[short_window] = z_score_momentum(\n",
    "        for long_window in long_window_range:\n",
    "            if long_window > short_window:\n",
    "                returns = z_score_momentum(ret, short_window, long_window)\n",
    "                cum_returns = returns.cumsum().iloc[-1]\n",
    "                sharpe = get_stats(returns)['SR']\n",
    "\n",
    "                results.append((short_window, long_window, sharpe, cum_returns))\n",
    "\n",
    "                if sharpe > best_sharpe:\n",
    "                    best_sharpe = sharpe\n",
    "                    best_combination = (short_window, long_window, sharpe, cum_returns)\n",
    "\n",
    "    results_df = pd.DataFrame(results, columns=['Short Window', 'Long Window', 'Sharpe', 'Cumulative Return'])\n",
    "\n",
    "    return best_combination, best_sharpe, results_df\n",
    "\n",
    "short_window_range = range(3, 50)  # Test short windows from 3 to 50 days\n",
    "long_window_range = range(20, 200)  # Test long windows from 20 to 200 days\n",
    "\n",
    "best_combination, sharpe, results_df = grid_search(ret, short_window_range, long_window_range)\n",
    "\n",
    "print(\"Best combination of short and long windows:\")\n",
    "print(\"Short Window:\", best_combination[0])\n",
    "print(\"Long Window:\", best_combination[1])\n",
    "print(\"Sharpe Ratio:\", best_combination[2])\n",
    "print(\"Cumulative Return:\", best_combination[3])\n",
    "\n",
    "results_df.sort_values(by='Sharpe', ascending=False)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.20"
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 },
 "nbformat": 4,
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}