weekly_reversal.ipynb

{
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  {
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   "id": "099a4c53",
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   "source": [
    "# Contrarian Trading\n",
    "\n",
    "_Fortune befriends the bold_ - Emily Dickinson\n",
    "\n",
    "Concepts:\n",
    "- Mean reversion\n",
    "- Implementation shortfall\n",
    "- Structural break with unknown changepoints\n",
    "- Performance evaluation, information coefficient\n",
    "\n",
    "\n",
    "\n",
    "__References__\n",
    "\n",
    "\n",
    "Andrews, D.W.K., \"Tests for parameter instability and structural change with unknown change point\", Econometrica, 61 (1993), 821-856.\n",
    "\n",
    "Grinold, Richard C., 1994, \"Alpha is Volatility Times IC Times Score\", The Journal of Portfolio Management, Summer 1994, 20(4), 9-16\n",
    "\n",
    "Lo, Andrew W. and MacKinlay, A. Craig, 1990, \"When Are Contrarian Profits Due to Stock Market Overreaction?\", The Review of Financial Studies, 3(2), 175–205\n",
    "\n",
    "Perold, Andre, 1988, \"The Implementation Shortfall: Paper versus Reality\", The Journal of Portfolio Management, 14(3), 4-9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e8c10b03",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pandas import DataFrame, Series\n",
    "from tqdm import tqdm\n",
    "import matplotlib.pyplot as plt\n",
    "import rpy2.robjects as ro\n",
    "from rpy2.robjects.packages import importr\n",
    "from finds.database import SQL, RedisDB\n",
    "from finds.structured import BusDay, CRSP, Benchmarks\n",
    "from finds.recipes import fractile_split, least_squares\n",
    "from finds.utils import PyR, row_formatted\n",
    "from secret import credentials, paths, CRSP_DATE\n",
    "VERBOSE = 0\n",
    "#%matplotlib qt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "762f3b16",
   "metadata": {
    "lines_to_next_cell": 2
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   "outputs": [],
   "source": [
    "# open connections\n",
    "sql = SQL(**credentials['sql'], verbose=VERBOSE)\n",
    "rdb = RedisDB(**credentials['redis'])\n",
    "bd = BusDay(sql, verbose=VERBOSE)\n",
    "crsp = CRSP(sql, bd, rdb=rdb, verbose=VERBOSE)\n",
    "bench = Benchmarks(sql, bd)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a56eb805",
   "metadata": {},
   "source": [
    "## Mean reversion\n",
    "\n",
    "The assumption of __mean reversion__ underlies many investment and trading strategies. Similar assets should be priced identically, according to the __law of one price__, otherwise the opportunity for guaranteed __arbitrage__ profits incentize traders to buy the undervalued asset while short-selling the overvalued asset hence driving their prices closer together again. Deviations from long-run expectations, such as yield spreads in fixed income assets, should not persist. __Pairs trading__ involves identifying two related securities, often based on their historical price relationships, and trading them simultaneously to profit from the temporary divergences in price between the two securities. __Statistical arbitrage__, also referred to as stat arb, models statistical relationships between securities and uses computationally intensive trading algorithms to simultaneously buy and sell portfolios of securities in financial market assets such as equities and commodities.  Since such relationships are not risk-free, stat arb strategies seek to diversify as much as possible across a broad set of imperfectly correlated positions (unfortunately, correlations between strategies and\n",
    "asset classes seem to increase inconveniently around financial shocks, perhaps due liquidity constraints and deleveraging effects).\n",
    "\n",
    "\n",
    "We construct and evaluate a __contrarian strategy__ based on the weekly returns of US stocks and motivated by stock mispricing due to investor over-reaction, see Lo and Mackinlay (1990) and others. The __alpha__ of this strategy can be approximated by its __information coefficient__ and __volatility__ components, resembling Grinold (1994)'s decomposition. We apply tests of structural breaks with unknown changepoints to examine the decline of its profitability over time. Trading strategies can experience large differences between the paper return of a portfolio where all trades are assumed to have transacted at the decision price and the actual return of the portfolio using actual transaction prices and amounts executed. The concept of __implementation shortfall__, introduced by Perold (1988), measures the total cost of implementing an investment decision by capturing both explicit and implicit trading costs, such as market impact, delay, as well as opportunity costs.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "baedd4d9",
   "metadata": {},
   "source": [
    "\n",
    "__Weekly returns reversals__\n",
    "\n",
    "Daily returns from CRSP are compounded in to weekly stock returns assuming Wednesday-close to Wednesday-close. This skips over weekend and holiday (which often occur over a long weekend) effects. The backtest starts in January 1974, after an expansion of the stocks universe the previous year, and excludes the smallest market cap quintile (based on NYSE breakpoints) comprising microcap stocks.\n",
    "\n",
    "Let $\\overline{r}_{t}$ and $\\sigma_{t}$ be the\n",
    "cross-sectional mean and standard deviation of stock returns in week\n",
    "$t$. Define $\\tilde{r}_t = r_t - \\overline{r}_t$ as the vector of\n",
    "demeaned stock returns, and $X_{t} = -\\tilde{r}_t / \\sigma_t$ as the\n",
    "vector of normalized scores. In other words, stocks' \"exposures\" to (minus) their respective prior week's returns are standardized to have cross-sectional variance and standard deviation equal to 1.0. \n",
    "\n",
    "Each week, a portfolio is rebalanced to hold an amount in each stock equal to their respective exposure values divided by the number of holdings $w_{t} = X_{t}/n$.  hence the portfolio overall has unit exposure to prior week's returns $w_{t}^TX_{t} = 1$ and is dollar-neutral $\\sum w_{t} = \\sum X_{t}/n = 0$. \n",
    "\n",
    "As an aside, this portfolio construction approach can more generally incorporate additional signals where $X$ may be a matrix with ones in the\n",
    "first column and standardized signal exposures in other columns. Then each row,\n",
    "except the first, of $W = (X' X)^{-1} X'$ contains stock weights\n",
    "of a long-short characteristic porfolio: a dollar-neutral,\n",
    "minimum-norm (in terms of squared weights) portfolio with unit\n",
    "exposure through long positions in stocks with positive exposure and\n",
    "short positions in stocks with negative exposure to the signal,\n",
    "and zero exposure to the other characteristics. \n",
    "\n",
    "The portfolio's realized return in the following week $t+1$ is\n",
    "\n",
    "$$W'_t ~ r_{t+1} = \\dfrac{X'_t \\tilde{r}_t}{n} + \\dfrac{X'_t \\hat{r}_t}{n} = -\\dfrac{\\tilde{r}'_t ~ \\tilde{r}_{t+1} ~ \\sigma_{t+1}} {n ~ \\sigma_{t} ~ \\sigma_{t+1}} =\n",
    "-\\rho_{t,t+1} ~ \\sigma_{t+1}$$ \n",
    "\n",
    "that is the product of the\n",
    "(negative) cross-sectional correlation of stock returns times the amount of cross-sectional stock volatility at week $t+1$.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "080a0c74",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Last FamaFrench Date 2024-02-29 00:00:00\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████▉| 2607/2608 [01:17<00:00, 33.74it/s]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "weekday = 3          # wednesday close-to-close\n",
    "bd = BusDay(sql, endweek=weekday)   # Generate weekly cal\n",
    "begweek = 19740102   # increased stocks coverage in CRSP in Jan 1973\n",
    "endweek = bd.endwk(CRSP_DATE, -1)\n",
    "rebaldates = bd.date_range(begweek, endweek, freq='weekly')\n",
    "retdates = bd.date_tuples(rebaldates)\n",
    "\n",
    "june_universe = 0  # to track date when reached a June end to update universe\n",
    "year = 0           # to track new year to pre-load stocks datas in batch by year\n",
    "results = []\n",
    "lagged_weights = Series(dtype=float) # to track \"turnover\" of stock weights\n",
    "for rebaldate, pastdates, nextdates in tqdm(zip(\n",
    "        rebaldates[1:-1], retdates[:-1], retdates[1:]), total=len(rebaldates)-1):\n",
    "\n",
    "    # screen universe each June: largest 5 size deciles\n",
    "    d = bd.june_universe(rebaldate)\n",
    "    if d != june_universe:  # need next June's universe\n",
    "        june_universe = d                        # update universe every June\n",
    "        univ = crsp.get_universe(june_universe)  # usual CRSP universe screen\n",
    "        univ = univ[univ['decile'] <= 8]         # drop smallest quintile stocks\n",
    "\n",
    "    # retrieve new annual batch of daily prices and returns when start new year\n",
    "    if bd.begyr(rebaldate) != year:\n",
    "        year = bd.begyr(rebaldate)\n",
    "        prc = crsp.get_range(dataset='daily',\n",
    "                             fields=['bidlo', 'askhi', 'prc', 'retx', 'ret'],\n",
    "                             date_field='date',\n",
    "                             beg=year,\n",
    "                             end=bd.offset(bd.endyr(year), 10),\n",
    "                             cache_mode=\"rw\")\n",
    "\n",
    "    # get past week's returns, require price at rebalance (decision) date\n",
    "    past_week = prc[prc.index.get_level_values('date') == rebaldate]['prc']\\\n",
    "        .reset_index()\\\n",
    "        .set_index('permno')\\\n",
    "        .join(crsp.get_ret(*pastdates).reindex(univ.index))\\\n",
    "        .dropna()\n",
    "\n",
    "    # convert past week's returns to desired standardized portfolio weights\n",
    "    weights = ((past_week['ret'].mean() - past_week['ret']) /\n",
    "               (past_week['ret'].std(ddof=0) * len(past_week)))\n",
    "\n",
    "    # adjust past week's holdings by change stock price\n",
    "    lagged_weights = lagged_weights.mul(crsp.get_ret(*pastdates, field='retx')\\\n",
    "                                        .reindex(lagged_weights.index)\\\n",
    "                                        .fillna(0) + 1)\n",
    "\n",
    "    # compute how much to buy (or sell) to achieve desired new portfolio weights\n",
    "    chg_weights = pd.concat([weights, -lagged_weights], axis=1)\\\n",
    "                    .fillna(0)\\\n",
    "                    .sum(axis=1)\n",
    "\n",
    "    # calculate total abs weight as denominator for scaling turnover\n",
    "    total_weight = weights.abs().sum() + lagged_weights.abs().sum()\n",
    "\n",
    "    # get next week's gross returns\n",
    "    next_week = crsp.get_ret(*nextdates).reindex(weights.index).fillna(0)\n",
    "\n",
    "    # get next day's prices to compute one-day slippage cost\n",
    "    next_day = prc[prc.index.get_level_values('date') ==\n",
    "                   bd.offset(rebaldate, 1)]\\\n",
    "                   .reset_index()\\\n",
    "                   .set_index('permno')\\\n",
    "                   .drop(columns='date')\\\n",
    "                   .reindex(chg_weights.index)\n",
    "\n",
    "    # if no trade next day, then enter position at askhi (buy) or bidlo (sell)\n",
    "    bidask = next_day['askhi'].where(chg_weights > 0, next_day['bidlo']).abs()\n",
    "\n",
    "    # spread is relevant askhi or bidlo divided by recorded close, minus 1\n",
    "    spread = next_day['prc'].where(next_day['prc'] > 0, bidask)\\\n",
    "                            .div(next_day['prc'].abs())\\\n",
    "                            .sub(1)\\\n",
    "                            .fillna(0)\n",
    "    \n",
    "    # finally, trade_prc is the next day's close, or the relevent askhi or bidlo\n",
    "    trade_prc = next_day['prc'].where(next_day['prc'] > 0, bidask).fillna(0)\n",
    "\n",
    "    # drift is next day's trade price with dividends over today's decision price\n",
    "    # delay (positive is cost) will be chg_weights * drift \n",
    "    drift = trade_prc.div(next_day['prc'].abs())\\\n",
    "                     .mul(1 + next_day['ret'])\\\n",
    "                     .sub(1)\\\n",
    "                     .fillna(0)\n",
    "    \n",
    "\n",
    "    # exit and enter delay should sum to chg_weights.dot(next_day['ret'])\n",
    "    exit1 = -lagged_weights.dot(next_day['ret'].reindex(lagged_weights.index).fillna(0))\n",
    "    enter1 = weights.dot(next_day['ret'].reindex(weights.index).fillna(0))\n",
    "\n",
    "    # accumulate weekly calculations\n",
    "    results.append(DataFrame(\n",
    "        {'ret': weights.dot(next_week),\n",
    "         'exit1': exit1,\n",
    "         'enter1': enter1,\n",
    "         'delay': chg_weights.dot(next_day['ret'].fillna(0)),  # delay=enter+exit\n",
    "         'spread': chg_weights.dot(spread),\n",
    "         'slippage': chg_weights.dot(drift), # total slippage\n",
    "         'ic': weights.corr(next_week),\n",
    "         'n': len(next_week),\n",
    "         'beg': nextdates[0],\n",
    "         'end': nextdates[1],\n",
    "         'absweight': np.sum(weights.abs()),\n",
    "         'turnover': chg_weights.abs().sum()/total_weight,\n",
    "         'vol': next_week.std(ddof=0)},\n",
    "        index=[rebaldate]))\n",
    "\n",
    "    # carry forward to next week as lagged portfolio weights\n",
    "    lagged_weights = weights    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "345c5754",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Combine accumulated computations and report\n",
    "df = pd.concat(results, axis=0)\n",
    "dates = df.index\n",
    "df.index = pd.DatetimeIndex(df.index.astype(str))\n",
    "df['net'] = df['ret'].sub(df['slippage'])\n",
    "# Show summary\n",
    "cols = ['ic' ,'vol', 'ret', 'slippage', 'net', 'exit1', 'enter1', 'delay',\n",
    "        'spread', 'turnover']\n",
    "indexes = ['Information coefficient', 'Cross-sectional Volatility',\n",
    "           'Gross return (alpha)', 'Slippage cost', 'Net (of slippage) return',\n",
    "           '   Exit one day delay   ', '   Enter one day delay   ', 'Delay cost',\n",
    "           'Spread cost', 'Portfolio turnover']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b0e1d34c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Summary of Weekly Mean Reversion Strategy 19740109-20231220\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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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>mean</th>\n",
       "      <th>std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Information coefficient</th>\n",
       "      <td>0.0379</td>\n",
       "      <td>0.1027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cross-sectional Volatility</th>\n",
       "      <td>0.0535</td>\n",
       "      <td>0.0192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gross return (alpha)</th>\n",
       "      <td>0.0021</td>\n",
       "      <td>0.0080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Slippage cost</th>\n",
       "      <td>0.0017</td>\n",
       "      <td>0.0048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Net (of slippage) return</th>\n",
       "      <td>0.0005</td>\n",
       "      <td>0.0078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exit one day delay</th>\n",
       "      <td>-0.0003</td>\n",
       "      <td>0.0029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Enter one day delay</th>\n",
       "      <td>0.0006</td>\n",
       "      <td>0.0036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Delay cost</th>\n",
       "      <td>0.0004</td>\n",
       "      <td>0.0042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spread cost</th>\n",
       "      <td>0.0013</td>\n",
       "      <td>0.0023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Portfolio turnover</th>\n",
       "      <td>0.7362</td>\n",
       "      <td>0.0408</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              mean     std\n",
       "Information coefficient     0.0379  0.1027\n",
       "Cross-sectional Volatility  0.0535  0.0192\n",
       "Gross return (alpha)        0.0021  0.0080\n",
       "Slippage cost               0.0017  0.0048\n",
       "Net (of slippage) return    0.0005  0.0078\n",
       "   Exit one day delay      -0.0003  0.0029\n",
       "   Enter one day delay      0.0006  0.0036\n",
       "Delay cost                  0.0004  0.0042\n",
       "Spread cost                 0.0013  0.0023\n",
       "Portfolio turnover          0.7362  0.0408"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(f'Summary of Weekly Mean Reversion Strategy {dates[0]}-{dates[-1]}')\n",
    "pd.concat([df[cols].mean(axis=0).rename('mean'),\n",
    "           df[cols].std(axis=0).rename('std')], axis=1)\\\n",
    "  .set_index(pd.Index(indexes)).round(4)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "602d2090",
   "metadata": {},
   "source": [
    "## Implementation shortfall\n",
    "\n",
    "\n",
    "Perold (1988) observed that the a paper portfolio based upon a\n",
    "well-known stock rankings system significantly outperformed the actual\n",
    "track record of funds that make use of the system.  He defined\n",
    "implementation shortfall as the difference in return between a\n",
    "theoretical portfolio and the implemented portfolio, which captures explicit\n",
    "fees and commissions as well as  market impact, delay and\n",
    "opportunity costs.\n",
    "\n",
    "- __Decision price__ is the price at the time the investment decision was\n",
    "  made\n",
    "\n",
    "- __Arrival price__ is the midquote (mid-point of bid-ask prices) at the\n",
    "  time the trader, broker or trading system received the order, or the\n",
    "  trade decision is made.\n",
    "\n",
    "- __Market drift__ is the amount of buys (sells) multiplied\n",
    "  by the increase (decrease) in execution price relative to the\n",
    "  arrival price, due to execution delay.\n",
    "\n",
    "- __Delay__ is the adverse change in execution price relative to the decision price\n",
    "\n",
    "- __Opportunity costs__ are the profits lost due to trades that are cancelled or not executed.\n",
    "\n",
    "-  __Market impact costs__ are bid-ask spreads as well as the amount that buying or selling moves the price against the buyer or seller\n",
    "\n",
    "\n",
    "The __trader's dilemma__ refers to the trade-off between market drift and\n",
    "  impact: one can trade faster with more impact to minimize market drift, or\n",
    "  trade slower to minimize market impact but at the risk of the market drifting\n",
    "  away.\n",
    "\n",
    "Unfortunately, stock prices in CRSP are not available at a finer than daily frequency. We adjust estimated profits for slippage by waiting a full day after the decision price then setting the execution price at the next day's closing price, since the stock market exchanges experience the most liquidity during the trading day at the close. For stocks that did not trade during that day, we assumed the desired buys are executed at the (higher) ask price and sells are executed at the (lower) bid price: in the CRSP database, closing bid and ask quotes are recorded so that the closing price, when not available, is set to the negative of the bid-ask average. Transactions costs are challenging to model and cannot be observed from historical prices in backtest simulations, so this measurement approach feasibly assumes trades are executed at the close a full day after their decision prices are considered.\n",
    "\n",
    "Over the full period, much of the profitability of this version of the strategy appears to be dissipated after considering a one-day execution delay and bid-ask spreads.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08d032f5",
   "metadata": {},
   "source": [
    "## Structural break with unknown changepoint\n",
    "\n",
    "In a linear regression, the Chow test is commonly used to test for the presence of a structural break in the model at a period known _a priori_; it essentially constructs a test of whether the true coefficients on the independent variable split into two subsets are equal. Welch's test that two populations have equal means is a special case with no independent variables and only an intercept whose true values are tested in the two time periods.  However, when the breakpoints are unknown, these standard tests are not applicable. Andrews (1993) and others have developed alternative tests, based on supremum statistics, for the detection of structural change involving an unknown number of breaks in mean with unknown break points.\n",
    "\n",
    "The R library `strucchange` implements tests for structural breaks with unknown breakpoints. The external Python package `rpy2` provides an interface to R libraries and programming language. The `PyR` wrapper class in the FinDS package facilitates converting Pandas DataFrames to and from R objects and function calls."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "43ad5c0e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Structural break test with unknown changepoint\n"
     ]
    },
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2.5 %</th>\n",
       "      <th>breakpoints</th>\n",
       "      <th>97.5 %</th>\n",
       "      <th>statistic</th>\n",
       "      <th>p.value</th>\n",
       "      <th>method</th>\n",
       "      <th>mean(pre)</th>\n",
       "      <th>mean(post)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>sctest</th>\n",
       "      <td>1996-10-23</td>\n",
       "      <td>2001-05-30</td>\n",
       "      <td>2002-11-06</td>\n",
       "      <td>17.226016</td>\n",
       "      <td>0.0</td>\n",
       "      <td>aveF test</td>\n",
       "      <td>0.003112</td>\n",
       "      <td>0.000957</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            2.5 % breakpoints     97.5 %  statistic  p.value     method  \\\n",
       "sctest 1996-10-23  2001-05-30 2002-11-06  17.226016      0.0  aveF test   \n",
       "\n",
       "        mean(pre)  mean(post)  \n",
       "sctest   0.003112    0.000957  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Structural Break Test with Unknown Changepoint\n",
    "importr('strucchange')   # R package to use\n",
    "\n",
    "# Set up data and formulas for R\n",
    "Y = df['ret']\n",
    "formula = ro.Formula('y ~ 1')\n",
    "formula.environment['y'] = PyR(Y.values).ro\n",
    "\n",
    "# Call R strucchange routines to compute breakpoint statistics\n",
    "fstats_r = ro.r['Fstats'](formula, **{'from': 1})    # Fstats at every break\n",
    "breakpoints_r = ro.r['breakpoints'](formula)         # candidate breakpoints\n",
    "confint_r = ro.r['confint'](breakpoints_r, breaks=1) # conf interval for 1 break\n",
    "sctest_r = ro.r['sctest'](fstats_r, **{'type': 'aveF'})\n",
    "\n",
    "# Extract output from R results\n",
    "confint = PyR(confint_r[0]).frame.iloc[0].astype(int) - 1  # R index starts at 1\n",
    "\n",
    "output = dict(zip(confint.index, df.index[confint]))       # confidence interval\n",
    "\n",
    "for k,v in zip(sctest_r.slots['names'][:3], sctest_r[:3]): # significance values\n",
    "    output[k] = PyR(v).values[0]\n",
    "output['mean(pre)'] = Y[df.index <= output['breakpoints']].mean() \n",
    "output['mean(post)'] = Y[df.index > output['breakpoints']].mean()\n",
    "\n",
    "fstat = [0] + list(PyR(fstats_r[0]).values) + [0, 0]  # pad before and after\n",
    "\n",
    "print(\"Structural break test with unknown changepoint\")\n",
    "DataFrame(output, index=['sctest'])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dcecc354",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "source": [
    "Plot breakpoint F-stats\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4f26c437",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_1727373/3930510136.py:5: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
      "  ax.axvspan(df.index[confint[0]], df.index[confint[2]], alpha=0.3, color='grey')\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(num=2, clear=True, figsize=(10, 6))\n",
    "ax.plot(df.index, fstat, color='C0')\n",
    "argmax = np.nanargmax(fstat)              # where maximum fstat\n",
    "ax.axvline(df.index[argmax], color='C1')\n",
    "ax.axvspan(df.index[confint[0]], df.index[confint[2]], alpha=0.3, color='grey')\n",
    "ax.legend(['F-stat', 'Max-F', 'C.I. of Break Date'])\n",
    "ax.annotate(\n",
    "    df.index[argmax].strftime('%Y-%m-%d'), xy=(df.index[argmax], fstat[argmax]))\n",
    "ax.set_ylabel('F-statistic at Breakpoints')\n",
    "ax.set_xlabel('Date of Breakpoints')\n",
    "ax.set_title('Weekly Mean Reversion Structural Break F-stats')\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0539511",
   "metadata": {},
   "source": [
    "## Information coefficient\n",
    "\n",
    "Grinold (1994) linked the expected alpha of a signal to its information coefficient and an asset's signal score and idiosyncratic volatility: $\\alpha = IC \\times volatility \\times score$. \n",
    "\n",
    "- __IC__: The information coefficient can be understood as\n",
    "  the correlation between a signal and residual returns.  It tells how well forecasts align wih actual returns and is a measure of manager forecasting skill.\n",
    "  \n",
    "- __Volatility__: The volatility can be understood as an asset's residual risk. This component allows for forecast alpha to be expressed in units of returns.\n",
    "\n",
    "- __Score__: The score is a standardized measure of an asset's raw\n",
    "  signal exposure, and reflects relative expectations about an asset.\n",
    "  Standardization, by subtracing the cross-sectional mean and\n",
    "  dividing by the cross-sectional standard deviation, allows assets to be\n",
    "  compared to one another and over time. \n",
    "\n",
    "Recall that the weekly reversal portfolio $W_t$ was constructed to be dollar-neutral with exposure equal to 1.0, with weekly profitability $W'_t ~ r_{t+1} = -\\rho_{t,t+1} ~ \\sigma_{t+1}$.  Hence the IC of this strategy is simply measured by the (negative) cross-sectional correlation of stock returns from one week to the next.\n",
    "\n",
    "We plot the 5-year rolling average alpha and its information coefficient and volatility components. Whereas volatility exhibits cyclical changes, the estimated IC showed a sharp downward trend from a peak in the mid 1990's down to 2010."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "39275294",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Plot returns, and rolling avg information coefficient and cross-sectional vol\n",
    "fig, ax = plt.subplots(num=1, clear=True, figsize=(10, 6))\n",
    "df['ret'].cumsum().plot(ax=ax, ls='-', color='r', rot=0)\n",
    "ax.legend(['cumulative returns'], loc='center left')\n",
    "ax.set_ylabel('cumulative returns')\n",
    "bx = ax.twinx()\n",
    "roll = 250  # 250 week rolling average ~ 5 years\n",
    "df['ic'].rolling(roll).mean().plot(ax=bx, ls=':', lw=1, rot=0, color='b')\n",
    "df['vol'].rolling(roll).mean().plot(ax=bx, ls=':', lw=1, rot=0, color='g')\n",
    "#bx.axhline(df['ic'].mean(), linestyle='-', color='C0', lw=2)\n",
    "bx.axhline(0, linestyle='-', color='black', lw=1)\n",
    "bx.legend([f\"information coefficient ({roll}-week average)\",\n",
    "           f\"cross-sectional volatility ({roll}-week average)\"],\n",
    "          loc='center right')\n",
    "ax.set_title(f'Profitability of Weekly Mean Reversion {dates[0]}-{dates[-1]}')\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b43711fc",
   "metadata": {},
   "source": [
    "## Performance evaluation\n",
    "\n",
    "In a CAPM equilibrium, no investor can achieve an abnormal return, and each investment yields an identical risk-adjusted return. In the real world, assets may yield a return in excess of, or below, the return with fair compensation for their risk exposure: portfolio managers rely on market benchmarks to measure the performance of a given investment relative to the CAPM equilibrium risk-return relationship. \n",
    "                                                        \n",
    "- Sharpe ratio - slope of the capital market line is the fair equilibrium compensation: $\\dfrac{R_P - r_f}{\\sigma_P}$\n",
    "- Treynor ratio - uses beta which is an approriate measure of risk for a well-diversified portfolio:  $\\dfrac{R_P - r_f}{\\beta_P}$\n",
    "- Jensen's alpha - the intercept of a CAPM regression should be zero in equilibrium:  $R_P - r_f- \\beta_P(R_M - r_f)$\n",
    "- Appraisal ratio - Jensen's alpha scaled by the volatility of residual returns $\\dfrac{\\alpha}{\\sigma_{P-M}}$\n",
    "- Sortino ratio - focuses on downside risk relative a target required rate of return T: $\\dfrac{R_P - T}{\\sqrt{\\sum_t \\min(0, r_t - T)^2 / N}}$\n",
    "- Information ratio - adjusts performance relative to a benchmark, called the acive return, scaled by the volatility of active returns, called tracking error: $\\dfrac{\\hat{R_P} - \\hat{R_B}}{\\sigma_{R_P-R_B}}$\n",
    "- $M^2$ (Modigliani-squared) - imagines that the given portfolio, P, is mixed with a position in T-bills so that the resulting portfolio P* matches the volatility of the market portfolio. Because the market index and portfolio $P^*$ have the same standard deviation, their performance may be compared by simply subtracting returns: $R_{P^*} - R_M$ \n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "fbddec17",
   "metadata": {},
   "outputs": [],
   "source": [
    "market = bench.get_series(permnos=['Mkt-RF'], field='ret').reset_index()\n",
    "breakpoint = BusDay.to_date(output['breakpoints'])\n",
    "out = dict()\n",
    "for select, period in zip([dates > 0, dates <= breakpoint, dates > breakpoint],\n",
    "                          ['Full', 'Pre-break', 'Post-break']):\n",
    "    res = df[select].copy()\n",
    "    res.index = dates[select]\n",
    "\n",
    "    # align market returns and compute market regression beta\n",
    "    #res['date'] = res.index\n",
    "    res['mkt'] = [(1 + market[market['date'].between(*dt)]['Mkt-RF']).prod() - 1\n",
    "                  for dt in res[['beg','end']].itertuples(index=False)]\n",
    "    # model = lm(res['mkt'], res['ret'], flatten=True)\n",
    "    model = least_squares(data=res, y=['ret'], x=['mkt'], stdres=True)\n",
    "\n",
    "    # save df summary\n",
    "    out[f\"{period} Period\"] = {\n",
    "        'start date': min(res.index),\n",
    "        'end date': max(res.index),\n",
    "        'Sharpe Ratio': np.sqrt(52)*res['ret'].mean()/res['ret'].std(),\n",
    "        'Average Gross Return': res['ret'].mean(),\n",
    "        'Std Dev Returns': res['ret'].std(),\n",
    "        'Market Beta': model.iloc[1],\n",
    "        'Jensen Alpha (annualized)': model.iloc[0] * 52,\n",
    "        'Appraisal Ratio': np.sqrt(52) * model.iloc[0] / model.iloc[2],\n",
    "        'Information Coefficient': res['ic'].mean(),\n",
    "        'Cross-sectional Vol': res['vol'].mean(),\n",
    "        'Total Slippage Cost': res['slippage'].mean(),\n",
    "        'Spread Cost': res['spread'].mean(),\n",
    "        'Delay Cost': res['delay'].mean(),\n",
    "        '  Exit Delay Cost': res['exit1'].mean(),\n",
    "        '  Enter Delay Cost': res['enter1'].mean(),\n",
    "        'Average Net Return': res['net'].mean(),\n",
    "        'Portfolio Turnover': res['turnover'].mean(),\n",
    "        #'Abs Weight': res['absweight'].mean(),\n",
    "        'Average Num Stocks': int(res['n'].mean()),\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8a9aa6dc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Subperiod Performance of Weekly Reversals\n"
     ]
    },
    {
     "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>Full Period</th>\n",
       "      <th>Pre-break Period</th>\n",
       "      <th>Post-break Period</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>start date</th>\n",
       "      <td>19740109</td>\n",
       "      <td>19740109</td>\n",
       "      <td>20010606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>end date</th>\n",
       "      <td>20231220</td>\n",
       "      <td>20010530</td>\n",
       "      <td>20231220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sharpe Ratio</th>\n",
       "      <td>1.9195</td>\n",
       "      <td>3.6667</td>\n",
       "      <td>0.7078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Average Gross Return</th>\n",
       "      <td>0.0021</td>\n",
       "      <td>0.0031</td>\n",
       "      <td>0.0010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Std Dev Returns</th>\n",
       "      <td>0.0080</td>\n",
       "      <td>0.0061</td>\n",
       "      <td>0.0097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Market Beta</th>\n",
       "      <td>0.0905</td>\n",
       "      <td>0.0686</td>\n",
       "      <td>0.1127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jensen Alpha (annualized)</th>\n",
       "      <td>0.1041</td>\n",
       "      <td>0.1566</td>\n",
       "      <td>0.0405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Appraisal Ratio</th>\n",
       "      <td>1.8591</td>\n",
       "      <td>3.6606</td>\n",
       "      <td>0.6001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Information Coefficient</th>\n",
       "      <td>0.0379</td>\n",
       "      <td>0.0578</td>\n",
       "      <td>0.0138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cross-sectional Vol</th>\n",
       "      <td>0.0535</td>\n",
       "      <td>0.0542</td>\n",
       "      <td>0.0526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total Slippage Cost</th>\n",
       "      <td>0.0017</td>\n",
       "      <td>0.0028</td>\n",
       "      <td>0.0003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spread Cost</th>\n",
       "      <td>0.0013</td>\n",
       "      <td>0.0023</td>\n",
       "      <td>0.0002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Delay Cost</th>\n",
       "      <td>0.0004</td>\n",
       "      <td>0.0006</td>\n",
       "      <td>0.0002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exit Delay Cost</th>\n",
       "      <td>-0.0003</td>\n",
       "      <td>-0.0004</td>\n",
       "      <td>-0.0001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Enter Delay Cost</th>\n",
       "      <td>0.0006</td>\n",
       "      <td>0.0010</td>\n",
       "      <td>0.0002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Average Net Return</th>\n",
       "      <td>0.0005</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>0.0006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Portfolio Turnover</th>\n",
       "      <td>0.7362</td>\n",
       "      <td>0.7393</td>\n",
       "      <td>0.7325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Average Num Stocks</th>\n",
       "      <td>1936</td>\n",
       "      <td>1981</td>\n",
       "      <td>1882</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Full Period Pre-break Period Post-break Period\n",
       "start date                   19740109         19740109          20010606\n",
       "end date                     20231220         20010530          20231220\n",
       "Sharpe Ratio                   1.9195           3.6667            0.7078\n",
       "Average Gross Return           0.0021           0.0031            0.0010\n",
       "Std Dev Returns                0.0080           0.0061            0.0097\n",
       "Market Beta                    0.0905           0.0686            0.1127\n",
       "Jensen Alpha (annualized)      0.1041           0.1566            0.0405\n",
       "Appraisal Ratio                1.8591           3.6606            0.6001\n",
       "Information Coefficient        0.0379           0.0578            0.0138\n",
       "Cross-sectional Vol            0.0535           0.0542            0.0526\n",
       "Total Slippage Cost            0.0017           0.0028            0.0003\n",
       "Spread Cost                    0.0013           0.0023            0.0002\n",
       "Delay Cost                     0.0004           0.0006            0.0002\n",
       "  Exit Delay Cost             -0.0003          -0.0004           -0.0001\n",
       "  Enter Delay Cost             0.0006           0.0010            0.0002\n",
       "Average Net Return             0.0005           0.0003            0.0006\n",
       "Portfolio Turnover             0.7362           0.7393            0.7325\n",
       "Average Num Stocks               1936             1981              1882"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display as formatted DataFrame\n",
    "fmts = dict.fromkeys(['start date', 'end date', 'Average Num Stocks'], '{:.0f}')\n",
    "print(\"Subperiod Performance of Weekly Reversals\")\n",
    "row_formatted(DataFrame(out), formats=fmts, default='{:.4f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2976deb8",
   "metadata": {},
   "source": [
    "The structural break test suggeted a statistically significant changepoint in mid-2001 in the time series of the strategy's weekly returns. The performance of the strategy in the periods before and after that dates shows a noticeable decline in annualized Sharpe ratio and average weekly returns, as well as an increase in riskiness. This coincides approximately with the New York and American Stock Exchanges switching to decimalization on Jan 29, 2001, which has led to tighter spreads. Previously, markets in the US utilized fractions in price quotes, where one-sixteenth (1/16) of one dollar ws the smallest price movement that could be presented in a price quote.\n",
    "\n"
   ]
  }
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