{ "cells": [ { "cell_type": "markdown", "id": "981c29fe", "metadata": {}, "source": [ "# Introduction to the Macrosynergy package: The \"Learning\" module" ] }, { "cell_type": "markdown", "id": "717daee7", "metadata": {}, "source": [ "The `macrosynergy.learning` subpackage provides functions and classes to help create statistical machine learning signals from panels of JPMaQS data. \n", "It is built to integrate the `macrosynergy` package with the widely used `scikit-learn` library." ] }, { "cell_type": "markdown", "id": "137e71c1", "metadata": {}, "source": [ "Most standard `scikit-learn` classes do not work directly with panel data. \n", "`macrosynergy.learning` provides wrappers that respect the cross-section and time indexing of quantamental dataframes and enable the use of scikit-learn models, feature selection, cross-validation, and metrics in a panel friendly way.\n", "\n", "See also the introductory notebooks where `macrosynergy.learning` is applied:\n", "- [Optimizing macro trading signals - A practical introduction](https://research.macrosynergy.com/optimal-signals/) \n", "- [Regression-based macro trading signals](https://research.macrosynergy.com/regression-signals/)\n" ] }, { "cell_type": "markdown", "id": "3ca39520", "metadata": {}, "source": [ "#### Features (x)\n", "For this notebook, we build a monthly dataset with features lagged by one month. We take the last recorded value of each month for daily z-scores:\n", "\n", "- `XGDP_NEG`: negative of growth trend. \n", "- `XCPI_NEG`: negative of excess inflation measure. \n", "- `XPCG_NEG`: negative of excess private credit growth. \n", "- `RYLDIRS05Y_NSA`: real IRS yield, 5-year maturity (expectations-based).\n", "\n", "\n", "#### Target (y)\n", "The target is a monthly aggregated return, created by summing daily returns for each month. \n", "Here, we focus on the return of a fixed receiver position in 5Y IRS (`DU05YXR_VT10`), scaled to a 10% annualized volatility target.\n", "\n" ] }, { "cell_type": "markdown", "id": "25ce8088", "metadata": {}, "source": [ "The first step is converting a quantamental dataframe into a wide format: \n", "- Columns = indicators or factors. \n", "- Rows = identified by `cid` (cross-section) and `real_date`. \n", "- Implemented via `categories_df` from `macrosynergy.management`.\n", "\n", "This function also supports:\n", "- Downsampling \n", "- Feature lagging\n", "- Dropping rows with nulls \n", "\n", "Both `SignalOptimizer` and `BetaEstimator` classes use this conversion internally." ] }, { "cell_type": "markdown", "id": "8b28826d", "metadata": {}, "source": [ "Here, we prepare the macroeconomic dataset. We set up the currency universe, select the required JPMaQS categories (including the target series and inputs for an FX blacklist), and construct the `FXBLACK` series to filter out untradeable currencies. We then build several derived macro factors (growth, inflation, credit, and real rate measures), merge them back into the panel, and standardize them across countries with cross-sectional z-scores. These normalized macro signals form the inputs for the later learning and signaling analysis.\n", "\n", "Further details on how the raw JPMaQS data is accessed and structured are provided in [this notebook](https://macrosynergy.com/academy/notebooks/check-out-jpmaqs/)." ] }, { "cell_type": "code", "execution_count": 32, "id": "180091b6", "metadata": {}, "outputs": [], "source": [ "import os\n", "import warnings\n", "warnings.filterwarnings('ignore')\n", "\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "\n", "import macrosynergy.management as msm \n", "import macrosynergy.panel as msp\n", "import macrosynergy.signal as mss\n", "import macrosynergy.pnl as msn\n", "import macrosynergy.visuals as msv\n", "import macrosynergy.learning as msl\n", "\n", "from macrosynergy.download import JPMaQSDownload\n", "\n", "from sklearn.pipeline import Pipeline\n", "from sklearn.linear_model import LinearRegression, Ridge\n", "from sklearn.metrics import make_scorer, r2_score" ] }, { "cell_type": "code", "execution_count": 33, "id": "6b059850", "metadata": {}, "outputs": [], "source": [ "# Cross-sections (cids) used throughout\n", "cids_dm = [\"AUD\", \"CAD\", \"CHF\", \"EUR\", \"GBP\", \"JPY\", \"NOK\", \"NZD\", \"SEK\", \"USD\"]\n", "cids_em = [\"CLP\", \"COP\", \"CZK\", \"HUF\", \"IDR\", \"ILS\", \"INR\", \"KRW\", \"MXN\", \"PLN\", \"THB\", \"TRY\", \"TWD\", \"ZAR\"]\n", "cids = cids_dm + cids_em\n", "\n", "cids_dux = list(set(cids) - set([\"IDR\", \"NZD\"]))" ] }, { "cell_type": "code", "execution_count": 34, "id": "5efe358d", "metadata": {}, "outputs": [], "source": [ "# Minimal set of JPMaQS categories required to recreate dfx, macro factors, and fxblack\n", "\n", "raw_xcats_for_calcs = [\n", " \"INTRGDPv5Y_NSA_P1M1ML12_3MMA\",\n", " \"CPIC_SJA_P6M6ML6AR\",\n", " \"CPIH_SA_P1M1ML12\",\n", " \"INFTEFF_NSA\",\n", " \"PCREDITBN_SJA_P1M1ML12\",\n", " \"RGDP_SA_P1Q1QL4_20QMA\",\n", " \"RYLDIRS05Y_NSA\",\n", " \"INTRGDP_NSA_P1M1ML12_3MMA\",\n", "]\n", "\n", "# The target category used in the learning_to_before_signaling notebook\n", "targets_needed = [\n", " \"DU05YXR_VT10\"\n", "]\n", "\n", "# Categories needed to build the FX blacklist\n", "fx_blacklist_inputs = [\n", " \"FXTARGETED_NSA\", \n", " \"FXUNTRADABLE_NSA\"\n", "]" ] }, { "cell_type": "code", "execution_count": 35, "id": "e2fe8a1e", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Downloading data from JPMaQS.\n", "Timestamp UTC: 2025-09-04 12:08:03\n", "Connection successful!\n", "Some expressions are missing from the downloaded data. Check logger output for complete list.\n", "11 out of 312 expressions are missing. To download the catalogue of all available expressions and filter the unavailable expressions, set `get_catalogue=True` in the call to `JPMaQSDownload.download()`.\n" ] } ], "source": [ "xcats_to_download = sorted(set(raw_xcats_for_calcs + targets_needed + fx_blacklist_inputs)) + [\"FXXR_NSA\", \"EQXR_NSA\"]\n", "\n", "\n", "dwn = JPMaQSDownload(\n", " client_id=os.environ.get(\"JPM_CLIENT_ID\", \"\"),\n", " client_secret=os.environ.get(\"JPM_CLIENT_SECRET\", \"\"),\n", " oauth=True,\n", ")\n", "\n", "df = dwn.download(xcats=xcats_to_download, cids=cids)" ] }, { "cell_type": "code", "execution_count": 36, "id": "b4ad9bb9", "metadata": {}, "outputs": [], "source": [ "\n", "# Build fxblack (FX blacklist) \n", "\n", "dfb = df[df[\"xcat\"].isin([\"FXTARGETED_NSA\", \"FXUNTRADABLE_NSA\"])][[\"cid\", \"xcat\", \"real_date\", \"value\"]]\n", "dfba = (\n", " dfb.groupby([\"cid\", \"real_date\"])\n", " .aggregate(value=pd.NamedAgg(column=\"value\", aggfunc=\"max\"))\n", " .reset_index()\n", ")\n", "dfba[\"xcat\"] = \"FXBLACK\"\n", "fxblack = msp.make_blacklist(dfba, \"FXBLACK\")\n", "\n", "\n", "# Recreate dfx and macro factors from the intro notebook\n", "dfx = df.copy()\n", "\n", "\n", "calcs = [\n", " # intuitive growth trend\n", " \"XGDP_NEG = - INTRGDPv5Y_NSA_P1M1ML12_3MMA\",\n", " # excess inflation measure\n", " \"XCPI_NEG = - ( CPIC_SJA_P6M6ML6AR + CPIH_SA_P1M1ML12 ) / 2 + INFTEFF_NSA\",\n", " # excess private credit growth\n", " \"XPCG_NEG = - PCREDITBN_SJA_P1M1ML12 + INFTEFF_NSA + RGDP_SA_P1Q1QL4_20QMA\",\n", " # excess real interest rate\n", " \"XRYLD = RYLDIRS05Y_NSA - INTRGDP_NSA_P1M1ML12_3MMA\",\n", " # combined real rate + inflation gap\n", " \"XXRYLD = XRYLD + XCPI_NEG\",\n", "]\n", "\n", "dfa = msp.panel_calculator(dfx, calcs=calcs, cids=cids)\n", "dfx = msm.update_df(df=dfx, df_add=dfa)\n", "\n", "# Create cross-sectional z-scores for the macro panels (ZN4), as used later\n", "macros = [\"XGDP_NEG\", \"XCPI_NEG\", \"XPCG_NEG\", \"RYLDIRS05Y_NSA\"]\n", "for xc in macros:\n", " dzn = msp.make_zn_scores(\n", " dfx,\n", " xcat=xc,\n", " cids=cids,\n", " neutral=\"zero\",\n", " thresh=3,\n", " est_freq=\"M\",\n", " pan_weight=1,\n", " postfix=\"_ZN4\",\n", " )\n", " dfx = msm.update_df(dfx, dzn)\n", "\n", "# the list of normalized macro factors referenced downstream\n", "macroz = [m + \"_ZN4\" for m in macros]\n", "xcatx=macros" ] }, { "cell_type": "code", "execution_count": 37, "id": "ba1a6812", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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XGDP_NEGXCPI_NEGXPCG_NEG
cidreal_date
AUD2000-02-29-0.127516-0.162771-2.316805
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" ], "text/plain": [ " XGDP_NEG XCPI_NEG XPCG_NEG\n", "cid real_date \n", "AUD 2000-02-29 -0.127516 -0.162771 -2.316805\n", " 2000-03-31 0.188010 -0.162771 -2.316805\n", " 2000-04-28 0.033589 -0.162771 -3.137645\n", " 2000-05-31 0.175323 -0.676674 -2.763879\n", " 2000-06-30 0.205179 -0.676674 -2.422330\n", "... ... ... ...\n", "ZAR 2025-05-30 -0.426351 1.882825 1.799903\n", " 2025-06-30 -0.030835 1.777107 0.718399\n", " 2025-07-31 0.399231 1.732005 0.136673\n", " 2025-08-29 0.213512 1.568641 0.322765\n", " 2025-09-30 -0.369677 1.279459 -0.600531\n", "\n", "[5444 rows x 3 columns]" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Downsample from daily to monthly frequency (features as last and target as sum)\n", "dfw = msm.categories_df(\n", " df=dfx,\n", " xcats=xcatx,\n", " cids=cids_dux,\n", " freq=\"M\",\n", " lag=1,\n", " blacklist=fxblack,\n", " xcat_aggs=[\"last\", \"sum\"],\n", ")\n", "\n", "# Drop rows with missing values and assign features and target\n", "dfw.dropna(inplace=True)\n", "X = dfw.iloc[:, :-1]\n", "y = dfw.iloc[:, -1]\n", "\n", "X" ] }, { "cell_type": "markdown", "id": "e475829d", "metadata": {}, "source": [ "## Cross-validation splitters" ] }, { "cell_type": "markdown", "id": "d697bc79", "metadata": {}, "source": [ "Cross-validation is a resampling technique used to evaluate how well a machine learning model generalizes to unseen data. Instead of training and testing on one fixed train-test split, cross-validation systematically splits the dataset into multiple parts (called folds) and rotates through them. Each division that results from the splitting is known as a \"fold\". \n", "\n", "The `macrosynergy` package specializes on cross-validation splits for panel data and supports the splitting of panel data into folds through five classes:\n", "\n", "- `ExpandingIncrementPanelSplit()`\n", "- `ExpandingFrequencyPanelSplit()`\n", "- `ExpandingKFoldPanelSplit()`\n", "- `RollingKFoldPanelSplit()`\n", "- `RecencyKFoldPanelSplit()`" ] }, { "cell_type": "markdown", "id": "33705318", "metadata": {}, "source": [ "### `ExpandingIncrementPanelSplit()` " ] }, { "cell_type": "markdown", "id": "631f029c", "metadata": {}, "source": [ "The [`ExpandingIncrementPanelSplit()`]((https://docs.macrosynergy.com/stable/macrosynergy.learning.splitters.html#macrosynergy.learning.splitters.ExpandingIncrementPanelSplit)) class facilitates the generation of expanding windows for cross-validation, essential for modeling scenarios where data is incrementally available over time. \n", "\n", "This class divides the dataset into training and testing sets, systematically increasing the size of the training set by one observation with each iteration. \n", "\n", "This approach effectively simulates environments where new information is gradually incorporated at set intervals.\n", "\n", "Important parameters are:\n", "\n", "* `train_intervals` specifies the length of the training interval in time periods. This parameter controls how much the training set expands with each new split. \n", "* `min_cids` sets the minimum number of cross-sections required for the initial training set, with the default being four. This is crucial in scenarios where panel data is unbalanced, ensuring there are enough cross-sections to begin the training process.\n", "* `min_periods` sets the smallest number of time periods required for the initial training set, with the default being 500 native frequency units. This is particularly important in an unbalanced panel context and should be used in conjunction with `min_cids`.\n", "* `test_size` determines the length of the test set for each training interval. By default, this is set to 21 periods, which follows the training phase.\n", "* `max_periods` defines the maximum duration that any training set can reach during the expanding process. If this cap is reached, the earliest data periods are excluded to maintain this constraint. By setting this value, rolling training is effectively performed." ] }, { "cell_type": "code", "execution_count": 38, "id": "d6d68a96", "metadata": {}, "outputs": [], "source": [ "split_xi = msl.ExpandingIncrementPanelSplit(train_intervals=12, min_periods=12, test_size=24, min_cids=2)" ] }, { "cell_type": "markdown", "id": "22305c55", "metadata": {}, "source": [ "#### `visualise_splits()`" ] }, { "cell_type": "markdown", "id": "3366cba2", "metadata": {}, "source": [ "The [`visualise_splits`](https://docs.macrosynergy.com/stable/macrosynergy.learning.splitters.base_splitters.html#macrosynergy.learning.splitters.base_splitters.BasePanelSplit.visualise_splits) method an be applied to a splitter and is a convenient method for visualizing the splits produced by each splitter based on the full data sets of features and targets. " ] }, { "cell_type": "code", "execution_count": 39, "id": "faae97e1", "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "split_xi.visualise_splits(X,y)" ] }, { "cell_type": "markdown", "id": "4943c6e9", "metadata": {}, "source": [ "### `ExpandingFrequencyPanelSplit()` " ] }, { "cell_type": "markdown", "id": "ed6bafae", "metadata": {}, "source": [ "As with `ExpandingIncrementPanelSplit()`, the [`ExpandingFrequencyPanelSplit()`]((https://docs.macrosynergy.com/stable/macrosynergy.learning.splitters.html#macrosynergy.learning.splitters.ExpandingFrequencyPanelSplit)) class generates expanding windows for cross-validation. \n", "\n", "However, the user specifies the frequencies at which the training sets expand and at which the validation sets span. \n", "\n", "The important parameters are:\n", "* `expansion_freq` specifies the frequency at which training sets expand. \n", "* `test_freq` specifies the frequency forward of each training set that each validation set spans.\n", "* `min_cids` sets the minimum number of cross-sections required for the initial training set, with the default being four. This is crucial in scenarios where panel data is unbalanced, ensuring there are enough cross-sections to begin the training process.\n", "* `min_periods` sets the smallest number of time periods required for the initial training set, with the default being 500 native frequency units. This is particularly important in an unbalanced panel context and should be used in conjunction with `min_cids`.\n", "* `max_periods` defines the maximum span that any training set can cover during the expanding process. If this cap is reached, the earliest data periods are excluded to maintain this constraint. By setting this value, rolling training is effectively performed." ] }, { "cell_type": "code", "execution_count": 40, "id": "7601d928", "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "split_xf = msl.ExpandingFrequencyPanelSplit(\n", " expansion_freq=\"M\",\n", " test_freq=\"Y\",\n", " min_cids=2,\n", " min_periods=12,\n", ")\n", "split_xf.visualise_splits(X, y)" ] }, { "cell_type": "markdown", "id": "9469523f", "metadata": {}, "source": [ "### `ExpandingKFoldPanelSplit()` " ] }, { "cell_type": "markdown", "id": "95af9bd8", "metadata": {}, "source": [ "The [`ExpandingKFoldPanelSplit()`](https://docs.macrosynergy.com/stable/macrosynergy.learning.splitters.html#macrosynergy.learning.splitters.ExpandingKFoldPanelSplit) class produces sequential learning scenarios, where information sets grow at fixed intervals.\n", "\n", "The key parameter here is `n_splits`, which determines the number of desired splits (minimum 2). \n", "\n", "As above, `visualise_splits()` method is used to visualise if the split has been performed as intended. " ] }, { "cell_type": "code", "execution_count": 41, "id": "37b0129b", "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "split_xkf = msl.ExpandingKFoldPanelSplit(n_splits=5)\n", "split_xkf.visualise_splits(X, y)" ] }, { "cell_type": "markdown", "id": "14e6dd38", "metadata": {}, "source": [ "### `RollingKFoldPanelSplit()`" ] }, { "cell_type": "markdown", "id": "625d2636", "metadata": {}, "source": [ "The [`RollingKFoldPanelSplit`](https://docs.macrosynergy.com/stable/macrosynergy.learning.splitters.html#macrosynergy.learning.splitters.RollingKFoldPanelSplit) class produces various adjacent paired training and test splits, where all splits use the full data panel. \n", "\n", "The training set and test set have to sit right next to each other in time. But the test sets can be both after and before training data. This means the method can use the past to validate the a model built on future data." ] }, { "cell_type": "code", "execution_count": 42, "id": "99f86caf", "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "split_rkf = msl.RollingKFoldPanelSplit(n_splits=5)\n", "split_rkf.visualise_splits(X, y)" ] }, { "cell_type": "markdown", "id": "50a55c71", "metadata": {}, "source": [ "### `RecencyKFoldPanelSplit()`" ] }, { "cell_type": "markdown", "id": "f2cdefb6", "metadata": {}, "source": [ "The [`RecencyKFoldPanelSplit`](https://docs.macrosynergy.com/stable/macrosynergy.learning.splitters.html#macrosynergy.learning.splitters.RecencyKFoldPanelSplit) class produces expanding training sets and constant-period test sets focusing on recent combinations of the two. \n", "\n", "It is similar to the `ExpandingKFoldPanelSplit`, being an expanding splitter where the number of folds is specified. However, the size of each test set, in terms of the number of periods at native dataset frequency, is also specified.\n", "\n", "Given parameters `n_splits` and `n_periods`, the last `n_splits` $\\times$ `n_periods` time periods in the panel are divided into `n_splits` test sets, each containing `n_periods` time periods.\n", "\n", "The respective training set for each test set comprises all dates in the panel prior to the test set." ] }, { "cell_type": "code", "execution_count": 43, "id": "a4f30048", "metadata": {}, "outputs": [ { "data": { "image/png": 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NKqpHjx6yZMkSM9y1Zs2aAfe/8MILcv/998uiRYukQ4cO8vbbb8uoUaMkMTEx4LG7du2STz75xMxFWp4GiczMTKmug2Rqamq1bNup9ORInQRH3QBwg8r+CXLiPw9kEffhfBsadQPAC/nEaXmELOI+nG9Do24AeCGPJISZRQLP5GEaMmSI7NixQx555JGA+/Lz82XWrFmmx0Pbtm3lmmuuMUNoHnrooYDH6lBZHbK6Z88eGTZsWKTFAQAAHkMWAQAAViKLAADgTRGPVNFeFtrDQsPDt99+K/369ZP69evL+vXrZebMmaanhi9YnHjiiTJmzBh54IEHzHyd5513nhx66KHyww8/yIIFC8xtEydOlNatW8fyvQEAABcjiwAAACuRRQAA8KaIG1XUVVddZRZvmj9/vkyaNEl27twpTZo0kZ49e8qVV15pfvYZPHiwdOzYUebMmWOGv27fvl3S09PlhBNOMMGBeTwB+2jeuPIFFAHYW5OG+0/vSenpftduRBYB3IksAriPW/MJWQRwJ7II4J1sEomE0lAr1rrYypUrzcIzKSkpVhfFFrQudPE8XaTOaXPYVjfqBoBXFBYWmuNcp06drC6KJ5BF/HG+DY26AeAl5JH4IYv443wbGnUDwEsKw8wiUY1UcSpOAoH1QZAKjroB4KXjHefH+KGu/XG+DY26AeAl5JH4oZ79cb4NjboB4CUJYWYRT45UAQAAAAAAAAAAqCpPjlRZtWqVGb6oQxcBAICYIf3aG0Pn+Ub1I4sAABCIPBI/ZBEAACLPIoniQRocGKDzJ60LnS+OOgk9dyh1E4i6CY3PVGjsN/atG86N8UV9++O4ad9jg51RN6HxmQqN/cbedcP5MX6oa38cN+19bLAr6iY0PlOhsd/Yu27CPT96cqSKrydGZmam1UWxhYKCAsnJyZGMjAxJTU21ujiA4/GZghOtWbPG6iJ4ClnEH8dNILb4TMGpyCPxQxbxx3ETiC0+U3B7FqkR6xedO3euLFu2TLZv3y6HHnqodOvWTYYPHy7NmjUzj+nVq5ds2bIl5DbOPfdcue+++2TMmDGydOlS+eCDD/zu19aqkSNHyocffii33367XHzxxbF8C3ChibO3yaa8IquLgTho3jhZbh3cULyM/R0qq20tGdq3vhRkZ0txfr4kpadLav/+4gVkEdgVx2dvIIuwryM0r+QTsgjsiuOzN5BF2NcReTZRpe3bS0JaWvwaVebPny+TJk2S448/Xm644QYTHL7//nuZOXOmvPPOOzJnzhxp3bq1TJ061Qz/qmjWrFny9ttvS+fOnUO+hgaH6667Tv7zn//I3XffLeeff36sig8X0wPp+s0cTOEN7O9QzRrtP71rKCjJyxOvIIvAzjg+wyvY1+HlfEIWgZ1xfIZXsK8jqmwS5gjOmDSqrFixQiZOnGh6R9x6661lt2uQ6N27t/Tr10/GjRsn2dnZ0qZNm4Dna7jQS9++fUMGgn379sno0aNNT4x7773XbBMAAECRRQAAgJXIIgAAeEdMGlW010WdOnXMyb2iBg0amCGrubm5Zj69ivPo6e16/5FHHinjx4+vNDjokNcHHnhAzjrrrFgUGwAAuARZBAAAWIksAgCAd0TdqFJaWiqLFy82c4LWrl076GPOOOOMoLfv3r3bzAOq25gyZUrQhYs0OOiwWe2x8eijj0qfPn2iLXJZuTXMYP/fofy1WyQkJITcJ+Fuui/rZ9zK1y9/HQ/s74j2s6G3637kRGQR53NrFlEcn72JLAJE9nlxah4hizgfWQRuQxYBbN6o8uuvv8revXuladOmVX7uXXfdJevWrZOHHnrI9MioqLi4WG688UYzp6geCPS1YkXnIc3JyYnZ9txg48aN4iZ6IA02rBrupz297BCG4/mZYn9HLD4bKSkp4kRkEfdwWxZRHJ+9iSwCRP55cWIeIYu4B1kEbkEWAWzeqJKUlFR2oq+K559/Xl555RUz3+iZZ54Z9DF5eXlmrtCnnnrKXHTO0C5dukhGRka0xZbk5OSYbMcN9CCrB7kWLVq4qkXXiT2cEBstW7a0vEdGvD9T7O+I9rOxYcMGcSqyiPO5NYsojs/eRBYBIvu8ODWPkEWcjywCtyGLADZvVKlbt66kpaXJ1q1bQz5Gh5NqDwh9rPrqq6/MAm7HHnusmTe0shP8k08+Kd26dTM9NnTBtuuvv14WLVokNWvWjPqDFmxYrZfpQY46gRvYJQTzmYKTPhtODqBkEffguAm3IIsAkX1enJpHyCLuwXETbkEWAapXYiw20qNHD1myZIkZ7hrMCy+8IF27dpW1a9fKb7/9ZuYL1Q+VzgVa2dDeQw891AQHpcNob731VjMsVntmAAAA+JBFAACAlcgiAAB4R0waVYYMGSI7duyQRx55JOC+/Px8mTVrlhlSqvPaaQ+MH374QR544AE57LDDqvQ65513nvztb3+TBQsWyLvvvhuLogMAABcgiwAAACuRRQAA8I6op/9SHTp0kFGjRpnw8O2330q/fv2kfv36sn79epk5c6bpqaH3Pfvss/L+++/LaaedZoa8rl69OmBbBx10UKVzek6YMME877bbbpN27dpJkyZNYvEWAACAg5FFAACAlcgiAAB4R0waVdRVV11lelzMnz9fJk2aJDt37jQn9p49e8qVV15pfn766afNY9966y1zCSYrK0vmzZsX8nUaNGhg5h0dPny43HjjjTJ37tyyReGAYJo3Tra6CIgT/tbUAfZr0nD/6T0pPd3v2u3IIrAzjs/ewN+ZOoC38wlZBHbG8dkb+DtTB4g8mxiJ4U3slVBaWloqHrNy5UrRt13ZvKVeonWhC+bpAnhOXRgQsBM+U3CiwsJCs7926tTJ6qJ4AlnEH8dNILb4TMGpyCPxQxbxx3ETiC0+U3B7FonZSBUn4cMcWB8EKSB2+EzBqfst58f4oa79cdwEYovPFJyKPBI/1LM/jptAbPGZgtuziCdHqgAAAAAAAAAAAFSVJ0eqrFq1ygxD0yFoAABAzNBs7Y3RsWNHq4viCWQRAAACkUfihywCAEDkWcSTjSoaHBigAwDAnzgvxhdZBACAQJwb44csAgBAoHDPjVE1qowZM0ZefvnlkPc3bNhQPvnkE/O4pUuXygcffBD0cYMGDTLX8+bNq3S7qamp0rRpU+nfv78MHjw44nL7emJkZmaKXUycvU025RVZXQwAgAtkta0lQ/vWl4LsbCnOz5ek9HRJ7d+/0uesWbNGnIgsEjtkEQCA1fnEiXmELBI7ZBEAgJXZROW2by8JaWkHfG7UI1XS09Nl6tSpQe+LZhhpxe1qK9G2bdtk4cKFct9990nNmjVl4MCB4hYaHNZvJjwAAKLXrNH+07uGgpK8PHE7skhskEUAANXJzfmELBIbZBEAgOXZJMzOBlE3qqSkpEiHDh2i3UzY2+3Zs6f07t1bsrOzXRUeAABAZMgiAADASmQRAAC8JVEcRnt51K5d2ywYAwAAEG9kEQAAYCWyCAAA1orJQvX79u0LentSUlJUJ/ny2y0pKZGff/7ZzC+am5srd9xxR8TbBQAA7kIWAQAAViKLAADgHVE3qmzZskXatm0b9L6bb75Zhg4dGtPttmjRQu6880656KKLJBo6F2lBQYFYTcOV9jABAKC67d6925z/gtHbndrbkSwSHbIIAMAu+cSpeYQsEh2yCADAaWKyUP0TTzwR9L4mTZqY60hCUfnt/vbbbzJt2jTZtGmTWYytY8eOUZZapKioSHJycsRqGhzatGljdTEAAB6gPRr1i4vK5u12IrJIdMgiAAA75RMn5hGySHTIIgAAp4nJQvWZmZkHPEEWFhaGvF/vq1evXqXb7dSpk5x33nlyxRVXyIsvvigtW7aMeg7SjIwMsZoTe+EAAJxJz52hRqps2LBBnIosEh2yCADALvnEqXmELBIdsggAwJNrqhxIw4YNZceOHSYkBOt1kpeXd8ATuQYQ7Y0xYMAAGTt2rCxYsCCqE68+NzU1NeLnAwDgNJVNq+D2f2bJIgAA2D+fuDmPkEUAAHCPxHi8SFZWlhlW+u677wbc9/nnn5vw0LVr1wNu59hjj5ULLrhAVq1aJa+88ko1lRYAALgNWQQAAFiJLAIAgHtEPVJFe1msXr065P1HH320dOnSRXr16iXjxo2T7777zvyemJgoX331lcyYMUM6d+4sZ5xxRlivd91118mbb74pkydPlr/97W9y0EEHRfsWAACAg5FFAACAlcgiAAB4S9SNKvn5+WboaSjac+KYY46RKVOmyOzZs+X111831yUlJXL44YfLwIEDZdiwYZKUlBTW69WvX19GjRolEyZMkMcff1xuueWWaN8CAABwMLIIAACwElkEAABvSSgNtWKti61cudIshBdsHlOr7Pi9RPYVe+5PAQCoBjVTEqROaqKU/vGHSEmJSGKiJKSlHbCHpc6rrQugovqRRQAAXhNOPiGPxA9ZBADgdTUrZhMRKUpJkYTExANmkbgsVG83dlz8rl6duCxvAwDwkAM1pPg9NiHBludHt7JjXZNFAABW5xPySPzYsZ7JIgAAq7NJQlFRWOdIT45UAQAAAAAAAAAAqCpPjlRZtWqVGeaanJxsdVEAALCFov/vjdGxY0eri+IJZBEAAAKRR+KHLAIAQORZxJNjKzU4MEDnT1oXusNQJ4Gom9Com9Com9CoG/vWDefG+KK+7bX/2xl1Exp1Exp1Exp1Y++64fwYP9S1/fZ/u6JuQqNuQqNuQqNu7F034Z4fPTlSxdcTIzMz0+qiAABgC2vWrLG6CJ5CFgEAIBB5JH7IIgAARJ5FYtKo8u2338pzzz0nixcvlry8PKlRo4a0atVK+vbtKxdccIH5XfXq1Uu2bNlS9jwdSlO3bl0znOa6666T1q1bl903aNAgWbp0acBJv2HDhnLyySebx+tz7Wji7G2yKa/I6mIAADwqq20tGdq3vhRkZ0txfr4kpadLav/+4mZkEX9kEQCA3bg9n5BF/JFFAABOyyaqtH17v4Xrq61R5Y033pCxY8fKkUceKYMHD5aWLVvKnj175KOPPpJJkybJxx9/LNOmTTNBQZ100kkyYsQI8/O+ffvk559/llmzZslll11mtnXIIYeUbbtNmzZy5513lv2uw3/Wrl0rDz30kOTk5MiCBQvKtmsnGhzWbyY8AACs0azR/tO7hoKSvDxxO7JIILIIAMBu3JxPyCKByCIAAEdmkzBHcNaItieGBocTTzxRHnnkkbKeF76QcPzxx8vIkSPlzTfflDPOOMPc3qBBA+nQoYPfdnS4ae/eveWtt96Siy++uOz2gw46KOCxxx13nPzxxx8yZcoU+fzzzwPuBwAA3kEWAQAAViKLAADgPVEtVD9jxgxJTEyU8ePH+wUHnz59+ki/fv0OuJ2qDldt166dud66dWuVngcAANyFLAIAAKxEFgEAwHuiGqny/vvvS9euXf2GplZ0//33+/1eWlpqhreqkpIS2bZtmzz66KNmTtDTTz89rNfNzc01182aNYum+AAAwOHIIgAAwEpkEQAAvCfiRpWdO3eaS4sWLQLu84UDH53fMykpyfz8yiuvmEvF+x988EEzBDZU0PC9pi7S9sQTT5hF3Hw9MyKh2y4oKJBY0vdRu3btmG4TAIBY2b17tzn/BaO323E+7sqQRQKRRQAATs4nTssjZJFAZBEAgBdE3KiivSmC+f777+XUU0/1u+3www+XDz74wPx88skny9VXX112At++fbuZW/TGG280YeqCCy4oe96yZcukbdu2ftvSYbXdu3eXCRMmRBW2dHE3XdQtljQ46CJyAADYkfZo1HNtKCkpKeIkZJFAZBEAgNPziZPyCFkkEFkEAOAFETeq1K9fX1JTU2XLli1+tzdp0kQWLVpU9vvjjz8u69atK/u9Xr16ZgG28nr27Ck///yz6ZVx3nnnlfXe0OCg85IqDQo1a9Y029eF2qKVnJwsGRkZEktO6lEDAPCeli1bhhypsmHDBnEaskggsggAwMn5xGl5hCwSiCwCAPCCqNZU6dWrl3z44Yeya9eushO69iopHw40LIRDh6x++umn8uuvv5p5RFVaWlpA0IjliV7DDwAAXlHZVAxO/QeYLAIAgHvyiRPzCFkEAADvSYzmycOHDzdze952221SWFgYcP+ePXtk8+bNYW1rzZo1UrduXdPTAwAAIBxkEQAAYCWyCAAA3hPVSJWjjz7aDE0dO3as9O/fX/7+97+b2zRQrFq1ygx33bZtmwwbNqzsOTpX6OrVq8t+1/lCdYG2zz77TEaPHl02xBUAAOBAyCIAAMBKZBEAALwnqkYV1adPHzNEdcGCBSYs6FyiOh9qs2bN5IwzzpALL7xQWrRoUfb4jz76yFx8dKipzqF65513ysCBA8UNmjdOtroIAAAPa9Jw/+k9KT3d79qtyCKByCIAALtxcz4hiwQiiwAAnJZNjMTwJvZKKA21Yq2LrVy50gQcnecUAACIma5C59Xu1KmT1UXxBLIIAACByCPxQxYBACDyLBL1SBUncuLidwAAVPe5kfNj/FDXAAAEIo/ED/UMAEDkWcSTI1UAAAAAAAAAAACqypMjVXSxOG1LSk5mjk8AAFRRUZHpjdGxY0eri+IJZBEAAAKRR+KHLAIAQORZJLyVV1xGgwMDdP6kdaHzxVEngbRO9MNE3QSibkKjbkKjbuxbN5wb44v69kcWse+xwc6om9Com9CoG3vXDefH+KGu/ZFF7H1ssCvqJjTqJjTqxt51E+75MeqRKmPGjJGlS5fKBx98EPT+Xr16SVZWltx3330yaNAgc9u8efMO+Fjftl9++eWQr/3oo4/KaaedVuUy+3piZGZmVvm5blRQUCA5OTmSkZEhqampVhcHAGCBNWvWiFORRZyPLAIAcHIeIYs4H1kEAFCVLGL76b/S09Nl6tSpQe9r0aJF3MuD8E2cvU025RVZXQwA8JystrVkaN/6UpCdLcX5+ZKUni6p/ftbXSzHIos4F1kEAOyDfBI5sohzkUUAwDnZRJW2by8JaWnOb1RJSUmRDh06WF0MRECDw/rNhAcAiLdmjfaf3jUUlOTlWV0cxyOLOBdZBADsg3wSObKIc5FFAMBh2STMEZyeXFMFAAAAAAAAAACgqmI2UmXfvn2x2lRY205KSpKEhIRqe00AAOAsZBEAAGAlsggAAN4Qk0aVLVu2SNu2bWOxqbC3fcMNN8jw4cMj3m5paalZiAwiu3fv9ruOloa62rVrx2RbAIDY0eO8nv+C0dud/E85WcTZyCIA4F3l84mT8whZxNnIIgCAuDeq6KJpTzzxRND7rrrqqiptq2KACrXtxo0bSzSKiookJycnqm24zcaNG2OyHQ0Obdq0icm2AACxk5ubW+k/ijpft1ORRdyBLAIA3lMxnzg1j5BF3IEsAgCIW6OKhp7MEIu4lA9EqampsmPHjpDbKSwsDGjJr2zb0UhOTpaMjIyYb9eJNMBqcGjRokVMelI4tWcRALhdy5YtQ45U2bBhgzgZWcTZyCIA4F3l84mT8whZxNnIIgAAS9ZUCUfDhg1l3bp1IYPD9u3bzWPiQU9wGmbwJw0O1AkAuFdl/yB65R8/soi9kUUAwNv5xAt5hCxib2QRAEA4EiWOsrKyZOvWrbJ69eqA+9577z0pLi6Wrl27xrNIAADAQ8giAADASmQRAACcL64jVc444wyZM2eO/OMf/zAXXWitpKREVq5cKTNmzJCzzjpLOnXqFM8iAQAADyGLAAAAK5FFAABwvrg2quh8nc8++6w8+eST8uKLL8qUKVMkMTFRjjjiCLn++uvlkksuiWdxAACAx5BFAACAlcgiAAA4X0JpqBVrXWzNmjXmujoWenOigoICycnJkWOOOSamc4dOnL1NNuUVxWx7AIDwZLWtJUP71peC7Gwpzs+XpPR0Se3fv9LncG6ML+rbH1kEANwvnHzC+TF+qGt/ZBEA8J6sCtlE5bZvLwlpaQc8P3qyUUWH1erbTklJsbootqB1UVRUZHrMeGFhQABA8IVR9RzAdBPxQRbxRxYBACjySPyQRfyRRQAAVckicZ3+yy44QQbWB0EKALxNzwWcH+OHuvZHFgEAKPJI/FDP/sgiAICqZBFPjlQBAAAAAAAAAACoKk+OVFm1apUZ2qnDOgEAgJjpDrQ3RseOHa0uiieQRQAACEQeiR+yCAAAkWeRRPEgDQ4M0PmT1oXOF0edAIB3cW6ML+rbH1kEAKA4P8YPde2PLAIAqMr5MS4jVR577DGZOnWqfPPNN7JkyRK59NJLZe7cuXL88cdX+rwPPvhA5syZI2vXrpW9e/dK48aNpWfPnnLllVfKIYccEnF5fD0xMjMzI96GmxQUFEhOTo5kZGRIamqq2M3E2dtkU16R1cUAAMfIaltLhvatLwXZ2VKcny9J6emS2r9/pc9Zs2aNuBlZxN7IIgDgfuHkEzfnEbKIvZFFAMB7sipkE5Xbvr0kpKU5d/qvl19+WcaOHSsXXnihXH755VK7dm3ZsGGDTJ8+XT788EN56aWXpG7dulYXE3GgwWH9ZsIDAISrWaP9p3cNBSV5eVYXx7HIIvAhiwBA9MgnVUcWgQ9ZBADilE3C7Gxg20aVxx9/XM4880y56667ym7r2rWrdOnSRc455xx58cUXZdiwYZaWEQAAuBdZBAAAWIksAgCAPdm2UWXbtm1B5y9r3bq16amh1wAAANWFLAIAAKxEFgEAwJ5s26iic4S+/vrrZs7Q008/XY477jhp1KiRuU+HvQIAAFQnsggAALASWQQAAHuybaPK3XffLSUlJfLOO+/Ie++9Z25r3ry5nHLKKTJ48OCyIBEp7e2hC5FBZPfu3X7XdpGQkGDmjAUAxIYe54P1dlR6ux538SeySPyQRQDAu8rnE/KIP7JI/JBFAACuaFSpU6eOTJkyRX744Qf56KOPZMmSJeYye/Zsef7552XWrFnSsWPHiLdfVFQkOTk5MS2z023cuFHsRINDmzZtrC4GALhGbm5upf8opqSkxLU8dkcWiT+yCAB4T8V8Qh75E1kk/sgiAABHN6r4NG3aVC6++GJz0R4a2jtjzJgxpsdGdnZ2xNtNTk6WjIyMmJbVqTTAanBo0aKFrXpA0EMJAGKrZcuWIUeqbNiwIe7lcQqySPUjiwCAd5XPJ+SR4Mgi1Y8sAgBwfKPK22+/LXfeeacsWLDABCyfxMREOfXUU2XZsmXywgsvRH1iSk1NjUFp3UODA3UCAO5V2T+I/MPmjyxiDbIIAHg7n5BH/kQWsQZZBAAQjkSxoVatWsmOHTtkzpw5Qe/X3gNHHXVU3MsFAAC8gSwCAACsRBYBAMC+aljZ6yLY3J3nn3++/OUvf5Hhw4fLU089JVu3bpW+fftK48aN5ZdffpF//etf8tlnn5k5RAEAACJFFgEAAFYiiwAA4EyWNarMnz8/6O2nnXaapKWlyejRo+WYY46RF198Ue655x7ZtWuXHHzwwdKlSxdZtGiRtG7dOu5lBgAA7kEWAQAAViKLAADgTAmloVasdbGVK1eahfBSUlKsLootaF0UFRWZRersOIftjt9LZF+x53ZTAIhYzZQEqZOaKKV//CFSUqKTb0tCWlqlzyksLDTngE6dOsWtnF5GFvFHFgEA9wsnn5BH4ocs4o8sAgDeU7NiNhGRopQUSUhMPGAWseVC9dXNjidIq+vDzkGqXh1bLv0DALZ3oIYUv8cmJHB+jCPq2h9ZBAC8o7J8Qh6JH+rZH1kEALwroVw2SSgqCusc6cmRKgAAAAAAAAAAAFXlyZEqq1atMkM7dVgnAAAQM92B9sbo2LGj1UXxBLIIAACByCPxQxYBACDyLOLJ8YMaHBig8yetC527ljoJRN2ERt0ceD5e6iYQdWPfuuHcGF/Utz/OKaFRN6FRN/Y9p9gZdWPvuuH8GD/UtT/OKaFRN6FRN/Y+p9gVdWPvugn3/OjJkSq+nhiZmZlWF8UWCgoKJCcnRzIyMiQ1NdXq4tgKdRMadQO4y5o1a6wugqeQRfxxTgmNugmNugHchzwSP2QRf5xTQqNuQqNuAO9mkWprVHnsscdk6tSp8s0338iSJUvk0ksvlVNOOUWmTZsW8Njs7GwZO3asvP/++9K0adOyxwc76Tdo0EC6du0qN910k6Snp1dX8QG4xMTZ22RTXpHVxQDiKqttLRnat74UZGdLcX6+JKWnS2r//uI1ZBEAdkAWAbybT8giAOyALAKEl01Uafv2fgvX22KkioaDV199Vfr27RvW4++44w5p27Zt2e9//PGHrFixQqZPny65ubny4osvVmNpAbiBBof1mwkP8JZmjfaf3jUUlOTlWV0cWyGLAIg3sgiwH/lkP7IIgHgjiwBVyCZhjuCMa6PKwQcfLBMnTpTu3btLw4YND/h4HT7XoUMHv9tOOOEEM1/h008/LRs2bDCPAQAACAdZBAAAWIksAgCA88V1ofrrr7/ezDd41113RR1CVEJCQoxKBgAAvIAsAgAArEQWAQDA+eI6UuXII4+Ua6+9ViZPniyvvfaanHXWWZU+vqSkRPbt21f2+65du2Tp0qUyc+ZMOfbYY6Vly5YRl6W0tNQEGYjs3r3b7xp/om6cWzf6z0Xt2rWtLgZgK/p51fNfMHq7F/4pJ4vYk93PKVaibpxbN2QRoOr5xAt5hCxiT3Y/p1iJunFu3ZBFAJc0qqihQ4fKu+++K3fffbdZWK2y4a6XX355wG1169Y1C7vpgmyJiZEPtCkqKpKcnJyIn+9GGzdutLoItkXdOK9uNDi0adPG6mIAtqLzblcW+FNSUsQLyCL2Zddzih1QN86rG7IIEFk+8UIeIYvYl13PKXZA3TivbsgigIsaVZKSkuTee++Vc889V8aPHy+PPfZYyMfq/bogm/bM0MXcZsyYIYMGDTK9OqKVnJzMvKP/TwOsngBatGhBC3YF1I1z68btPdyASGhPxlAjVXQ+bq8gi9iP3c8pVqJunFs3ZBGg6vnEK3mELGI/dj+nWIm6cW7dkEUAFzWqKD1pX3PNNfLQQw/J66+/Xmm4yszMND+3b9/enPCnTp0qNWvWlOHDh0d9YElNTY1qG26jJwDqJDjqJjTqBnCOyoK+1wI3WcSeOKeERt2ERt0A7sknXsojZBF74pwSGnUTGnUDeE9cF6ovb9iwYdKuXTsz3PWXX34J6zlXXXWVtG7dWqZMmSLr1q2r9jICAAD3IosAAAArkUUAAHCmRCuHu953331mkbWnnnoqrOfUqFFD7rrrLrNI2z333FPtZQQAAO5FFgEAAFYiiwAA4EyWNaqoVq1aydVXXy2///572M/p2LGj9O3bV5YsWSJvvvlmtZYPAAC4G1kEAABYiSwCAIDzWLKmSnlXXHGFvPvuu7J27dqwn3PjjTfKe++9Jw888ID07NnTlotBAbCH5o2TrS4CEHdNGu4/vSelp/tdIziyCIDqRBYB9iOfhEYWAVCdyCJAeNnESAxvDEpCaWlpqXjMypUrRd92SkqK1UWxBa2LoqIis+CdlxYGDAd1Exp1A7hLYWGh+Sx36tTJ6qJ4AlnEH+eU0Kib0KgbwH3II/FDFvHHOSU06iY06gbwbhaxfKSKFTjQBdYHQSo46iY06gZw32ea82P8UNf+OKeERt2ERt0A7kMeiR/q2R/nlNCom9CoG8C7WcSTI1UAAAAAAAAAAACqypMjVVatWmWG6OnwPAAAIGbYuvbG0IVPUf3IIgAABCKPxA9ZBACAyLNIeCuvuIwGBwbo/EnrQueLo04AwLs4N8YX9e2PLAIAUJwf44e69kcWAQBU5fxY7SNVHnvsMZk6dap88803ZT+Xl5SUJHXq1JEuXbrIddddJ61atZL//e9/cvnll8uZZ54pkydPDtjm7t27pW/fvubnf/3rX5KamlqlMvl6YmRmZkb13tyioKBAcnJyJCMjo8p1GY6Js7fJpryimG8XABBcVttaMrRvfSnIzpbi/HxJSk+X1P79K33OmjVrxK3IIvZHFgEA9wsnn7g1j5BF7I8sAgDek1Uhm6jc9u0lIS3NntN/Pf/882U/FxcXy9atW+Xhhx+Wiy++WF5//XXp2rWrXHjhhbJgwQI5++yzpWfPnn7Pf+SRR+SHH36QZ599tlpOdogtDQ7rNxMeACBemjXaf3rXUFCSl2d1cWyJLOItZBEAsB75xB9ZxFvIIgDgkGwSZmcDS6b/6tChQ9mlc+fOJiA88MADsnPnTnn55ZfNY2666SY5/PDD5a677pJdu3aVPffzzz+XuXPnytChQ81zAQAAqoosAgAArEQWAQDAuWyzpkq7du3M9ZYtW8x1Wlqa3H333fLjjz/KQw89VLZQzK233mqGY44cOdLS8gIAAHchiwAAACuRRQAAcAZLpv8KJjc311w3b9687LYTTjhBLrjgAlm4cKG5/u9//ysbN26URYsWSUpKioWlBQAAbkMWAQAAViKLAADgDJY0quzbt6/s5z179sjXX38tkyZNMguz+RZa87nllltMaBg3bpwJGNdee620bt066jKUlpaahciwf4G78texkpCQILVr147pNgEAkdPjvJ7/gtHb9bjtFWQReyGLAIB3lc8nXsojZBF7IYsAAGzfqNK2bduA21q1aiVTp06V9PR0v9sPOuggM9z1iiuuMHONDhs2LCZl0CGzOTk5MdmWW2hvl1jS4NCmTZuYbhMAEDn9J7yyfxS91NuRLGJPZBEA8J6K+cQreYQsYk9kEQCAbRtVdJiqT3JysgkMhxxySMjH//WvfzXXPXr0kKSkpJiUQV9X5yDF/p4YGhxatGgR0x4UXulhBABO0bJly5AjVTZs2CBeQhaxF7IIAHhX+XzipTxCFrEXsggAwLJGlS+++ELeeustGT16tNSosX/TxcXFUrNmTb/HZWZmitX0xJaammp1MWxFgwN1AgDuVdk/iG75h48s4mxkEQDwdj5xQx4hizgbWQQAEI5EiSHtVTJz5kz54Ycfym778ccfpXHjxrF8GQAAgKDIIgAAwEpkEQAA3C+mjSonnHCCadHXuT4//fRTmT9/vrzxxhty+umnx/JlAAAAgiKLAAAAK5FFAABwv5g2qjRq1EimTZsmO3fulBEjRsgTTzwhl19+uVxzzTWxfBkAAICgyCIAAMBKZBEAANwvoTTUirUutnLlSrMQXkpKitVFsQWti6KiIrNIXXXMYbvj9xLZV+y53QwALFMzJUHqpCZK6R9/iJSUiCQmSkJaWqXPKSwsNOeATp06xa2cXkYW8UcWAQD3CyefkEfihyzijywCAN5Ts2I2EZGilBRJSEw8YBaJ6UL1TuGGxe9iXR/VGaTq1YnpgCgAQJgO1JDi99iEBM6PcURd+yOLAIB3VJZPyCPxQz37I4sAgHcllMsmCUVFYZ0jPTlSBQAAAAAAAAAAoKo8OVJl1apVZminDusEAABipjvQ3hgdO3a0uiieQBYBACAQeSR+yCIAAESeRTw5/lCDAwN0/qR1oXPXUieh51WlbgJRN6FRN6FRN/atG86N8UV9+yOL2PfYYGfUTWjUTWjUjb3rhvNj/FDX/sgi9j422BV1Exp1Exp1Y++6Cff86MmRKr6eGJmZmVYXxRYKCgokJydHMjIyJDU11eriAAAssGbNGquL4ClkEX9kEQCAIo/ED1nEH1kEAFCVLFKlRpVBgwaZ63nz5vndvmvXLhkyZIh89dVX8sgjj8icOXNk6dKlfo/RYTN6YmrRooVcdtllcs4555jnZWVlyaWXXipjxozxe/yECRNk/vz5cuGFF8r48eP97rv77rvlpZdeMq9RnQuJwb4mzt4mm/KKrC4GANhSVttaMrRvfSnIzpbi/HxJSk+X1P79xQ3IIrALsggAeDOfkEVgF2QRAIhtNlGl7dv7LVxfbSNVNAAMGzZMvv76a3n88cflpJNOMuGhTZs2cuedd5Y9rri4WPLy8uSZZ56Rm2++WerVq2ceq70idC7Pij7++GPzmMWLFwfct2zZMunSpQvBwcM0OKzfTHgAgGCaNdp/etdQUJKXJ25HFoEVyCIAUDVuzidkEViBLAIA1ZBNwhzBmRiL4KBDJJ944gkTBnwOOugg6dChQ9mlc+fOcuaZZ8qsWbPMMNPs7GzzuK5du8ratWtl7969Zc/dtGmTuQwdOlR++OEH+e6778ru++2332T9+vVywgknRFN0AADgAmQRAABgJbIIAADeE3Gjyh9//CFXXHGFfPPNNzJ9+vSwT+Y1a9Y0PSl02Kvq3r27WYCm/Hxl2hvj4IMPlksuuURq167t1ytjxYoVUlJSYp4HAAC8iywCAACsRBYBAMCbEiNdwGv48OFmrtCnn35ajj/++IDHlJaWyr59+8ou2uNCe1aMHTvWBA+dO1R17NhRatWqJStXrvQLD926dTNzjR533HHm9/JDXNPT0+Xoo4+O7B0DAADHI4sAAAArkUUAAPCuGpEGB+0Z4fs9GD3Jt23b1u827YVx1FFHyaOPPionn3yyuU17Z+gQWF94KCwslCVLlpiQobSnhy7yprfrY5cvXx6T3hgabkKV3Wt2797td21nug9pLx0AQNXpcV7Pf8Ho7b7eknZHFnEfsggAeFf5fOKUPEIWcR+yCACgWhtVvvzyS7NQ2vz582XcuHEyZswY+de//mV6SZSnwWH8+PHm559//tkEAB3Oqtd/+ctf/B6rvS9mzJhhTugaIvSk3qNHD3OfXt97770mrOgcpNoLZODAgRItLYvOeYo/bdy4UexOg4Mu9gcAqLrc3NxK/1F0ykKnZBH3IosAgPdUzCdOyCNkEfciiwAAqqVRpW7dujJnzhxp3bq1PPDAA+ZEfsstt8jMmTP9epSkpaVJZmZm2e/t27eXvn37ypAhQ8xibA0aNPALD//85z/NMFgd0qrh4rDDDjP3ZWRkSOPGjU0vDd9JPxaLsemicLpt7O+JocGhRYsWtu/t4IReSwBgVy1btgw5UmXDhg3iFGQR9yGLAIB3lc8nTskjZBH3IYsAAKq1UUXn7NTg4AsE//jHP+Txxx83PSp0gbZQGjZsKHfccYeMGjVKJk6cKJMnTy67T1vYtZfH6tWr5bPPPivrjeGjw1q1R0ZiYqIZJlux90ekJyGdmxR/0uBAnQCAe1X2D6KT/jkji7gXWQQAvJ1PnJJHyCLuRRYBAFTbQvXljRgxwvS80PlAv/jii0ofe9ppp8mJJ54or732mixduvTPQiQmmkXdNDh8/fXXAeFBf9fbdQhsLHpjAAAA9yCLAAAAK5FFAADwlqgbVWrUqCEPPviguR49erTs2rWr0sfrfKM6xPSee+6R4uListu7du0qb731liQlJUlWVpbfc3QYrG5XF2MjPAAAgPLIIgAAwEpkEQAAvKXK03+FmgP15ptvNguw3XnnnZU+VucFHTRokMyaNUsWLFggl1xySVlA0HlBdUhrxelJdJ5RHQq7bt06Oe6442JRZDhc88bJVhcBAGyrScP9p/ek/58WwnftZmQRxBtZBACqxu35hCyCeCOLAEBss4mRGN4YlITSUCvWupgOl9W3nZKSYnVRbEHrQoOb9pRxyhy2AIDYKiwsNOeATp06WV0UTyCL+COLAAAUeSR+yCL+yCIAgKpkkZiMVHEaTpCB9UGQAgBv03MB58f4oa79kUUAAIo8Ej/Usz+yCACgKlnEkyNVAAAAAAAAAAAAqsqTI1VWrVplhnbqsE4AACBmugPtjdGxY0eri+IJZBEAAAKRR+KHLAIAQORZJLyVV1xGgwMDdP6kdaHzxVEnoedVpW4CUTehUTehUTf2rRvOjfFFffsji9j32GBn1E1o1E1o1I2964bzY/xQ1/7IIvY+NtgVdRMadRMadWPvugn3/OjJkSq+nhiZmZlWF8UWCgoKJCcnRzIyMiQ1NdXq4gAALLBmzRqri+ApZBF/ZBEAgCKPxA9ZxB9ZBABQlSwSUaPKoEGDZOnSpWYYzMKFC4M+5vrrr5c33nhDzj33XBk8eLD8/e9/l6ysLJkxY0bAYi/33XefzJ8/X55//nlp06aN2f6KFSvM78FO8L169TLb0ufB3SbO3iab8oqsLgYAOEpW21oytG99KcjOluL8fElKT5fU/v3FTcgiiBeyCADEhtvyCVkE8UIWAYD4ZBNV2r69JKSlVd9IlcTERFm9erXk5eVJ48aNA1r4P/zww7Lfjz76aBk9enRZSLjkkkvK7nvvvfdk9uzZcvvtt5vg4FNcXCxjx46V7OxsSUlJibSYcDgNDus3Ex4AoCqaNdp/etdQUJKXJ25FFkE8kEUAIDbcmE/IIogHsggAxDGbhDmCM+I1VfREX7NmTXnrrbcC7tPgULt2bWnUqFHZbZdffrl069ZNHnzwQfn222/NbT/88IOMGzdO/va3v/kFClWnTh1Zv369PP7445EWEQAAuBhZBAAAWIksAgCAN0XcqKJzTJ500klBw4MOb+3Tp4/UqPHnQBgd2nr//febwHHTTTfJ3r175YYbbpC0tDSZOHFiwDaOOeYY6devnxkW++WXX0ZaTAAA4FJkEQAAYCWyCAAA3hTVQvVnnHGGXHfddX5DXXft2iX//e9/zdBVvS5Pe2jcddddZl7RCy+8UNatWyfz5s2TunXrBt2+9tb45JNPzHDXl156KabDXUtLS81wXIjs3r3b79oONGxqrx4AQOzocV7Pf8Ho7RXn9nYCsog7kEUAwLvK5xMn5hGyiDuQRQAAcWtU6dmzpznAa68MHcaq3n33XTnkkEOkc+fOIQPHm2++Ke+8845cdtll0qlTp5Db11AxYcIEueqqq8xwVw0dsVJUVCQ5OTkx254bbNy4UexC96vyc8kCAKKXm5tb6T+KTpyrmyziLmQRAPCeivnEaXmELOIuZBEAQLU3qtSqVUt69erlFx5ef/11Of3000P2LsnPz5cVK1aY+99++225+uqrQ/bIULr9vn37muGup556qrRt21ZiITk5WTIyMmKyLafTAKvBoUWLFrbpBeG03kkA4AQtW7YMOVJlw4YN4kRkEXcgiwCAd5XPJ07MI2QRdyCLAADi1qiiNChcc801Zqirzgv62WefmaGvwZSUlMjNN98s+/btk0cffdQ87o477jA/V+a2224z2/UNd43VyUnnP8WfNDhQJwDgXpX9g+jkf9rIIu5BFgEAb+cTp+YRsoh7kEUAANW6UL3PX//6V7OomvbK0CGuTZs2lXbt2gV97PTp0+XTTz8184fqgm1Dhgwxz8vOzq70NbTHhj7nm2++kWnTpkVbZAAA4CJkEQAAYCWyCAAA3hL1SBWd77R3795myKoOez3zzDODPm7lypXy2GOPSf/+/c38oWrUqFGyePFiueeee+S4446TZs2ahXwdfY2zzjrLBBAdogoAAKDIIgAAwEpkEQAAvCXqkSpKw8CqVatkyZIlQcPDb7/9JjfeeKMcdthhZshq+eDxz3/+0yyOdtNNN0lxcXGlr3P77bdLvXr1Kl1kFwAAeA9ZBAAAWIksAgCAd0Q9UkV1795dDj74YGnSpIkceeSRAfffeuut8tNPP8lzzz1nhsSW16pVK7nhhhvk3nvvlccff1xGjhwZ8nU0OOhwV52rFN7QvDG9bwCgqpo03H96T0pP97t2M7IIqgtZBABiw+35hCyC6kIWAYD4ZBMjMbwxKAmlpaWl4jE65FbftvYIgZi60F4xOnzYqQsDAgCiU1hYaM4BnTp1sroonkAW8UcWAQAo8kj8kEX8kUUAAFXJIjEZqeI0nCAD64MgBQDepucCzo/xQ137I4sAABR5JH6oZ39kEQBAVbKIJ0eqAAAAAAAAAAAAVJUnR6ro4nHalqTDOgEAgJjpDrQ3RseOHa0uiieQRQAACEQeiR+yCAAAkWcRTzaqaHBggA4AAH/ivBhfZBEAAAJxbowfsggAAIHCPTdG3KgyaNAgWbFihTz//POSmZkZcH+vXr0kKytL7rvvvrLb9PHPPPOMWRDtt99+k0MPPVS6desmgwcPliOPPPKAz1c//fSTee3t27fLjBkzpEOHDlUuu68nxqvLm8imvKIqPx8AADvLaltLhvatLwXZ2VKcny9J6emS2r9/pc9Zs2aNOA1ZBAAAd+UTp+URsggAAO7JJiq3fXtJSEs74HOjGqlSXFwsY8eOlezs7AMu6DV9+nR56KGHpEePHjJu3DhJT0+X77//XhYsWCDnnnuu3HvvvXLmmWdWug0NDpdeeqns3LnThJB27dpFU3wTHNZvJjwAANylWaP9p3cNBSV5eeJmZBEAAJzBrfmELAIAgIuySZBOEsEkRvPCderUkfXr18vjjz9e6eM+/PBDmTx5slxzzTWmF4WGBO1tcf7555seHT179pQxY8aYbYXy888/lwWHOXPmRB0cAACA85FFAACAlcgiAAB4T1SNKsccc4z069fPBIIvv/wy5OOmTp0qf/nLX+Tqq68OOuR0woQJkpSUJE8//XTQ5+fn55vgsGvXLpk3b560bt06mmIDAACXIIsAAAArkUUAAPCeqBpVlA5ZrV+/vhnuWlhYGHC/zvGpweLkk0+WhISEoNuoV6+edO/eXd5///2A+7Zt2yaXXXaZ6Ykxd+5cadWqVbRFBgAALkIWAQAAViKLAADgLVE3qtStW9f0qFi3bl3Q4a5btmwx14cffnil2zniiCNMjwsNCT6//PKLCQ7fffedua+oiHk+AQCI1O7du6WgoCDopbS0VJyKLAIAgDvyiVPzCFkEAABvibpRRfXq1Uv69u1rhruuXbvW7z5fKNLhrJXRYa7lH6/++9//yp49e2ThwoWSmpoqo0ePNoELAABUXW5uruTk5AS9OP0fdLIIAADOzydOziNkEQAAvCMmjSrqtttuKxvuWj4I+Xpi+HpmhLJ582ZJS0szQ17LP/fZZ5+VDh06mF4f3377rdxzzz2xKjIAAJ7SsmVLM+93sMuB/sl3ArIIAADOzidOzyNkEQAAvCExlsNd77rrLvnmm29k2rRpZbcfcsgh5uT/9ttvS0lJSdDn6hDWTz75xPTsKC8rK0uaNGlifu7Tp49Z/G3RokXyxhtvxKrYAAB4Ru3atU0Px2CXUPN7OwlZBAAAZ+cTp+cRsggAAN4Qs0YV1bt3bznrrLNk+vTpZiE2n2uuucYM6X3ooYcCnlNcXCx33nmnGc46bNiwSrd/++23m14ad9xxh/zwww+xLDoAAHABsggAALASWQQAAPeLaaOK7wSvQ1XLz/F54oknypgxY2TmzJkydOhQ06Ni+fLl8sorr8jAgQPl3XfflYkTJ0rr1q0r3fZBBx0k999/v/zxxx9y4403yr59+2JdfAAA4HBkEQAAYCWyCAAA7hbzRhUNDjrctaLBgwfLggUL5OCDDzYBQH+fMmWKHHXUUZKdnS3nnHNOWNs/7rjjZMiQIbJq1SrzfAAAgPLIIgAAwEpkEQAA3C2htLS0VDxm5cqVom+7YG8N2VfsubcPAHC5mikJUic1UUr/+ENE5+1OTJSEtLRKn1NYWGjmMe/UqVPcyullZBEAgNeEk0/II/FDFgEAeF3NitlERIpSUiQhMfGAWaSGeJBv8bt6dWI+UAcAANs4UEOK32MTEhy/OKyTkEUAAF5VWT4hj8QPWQQAgMBsklBUFFYW8eRIFQAAAAAAAAAAgKry5EgVnXdU25KSk5OtLgoAALZQ9P+9MTp27Gh1UTyBLAIAQCDySPyQRQAAiDyLeHKcpwYHBuj8SetC566lTgJpneiHiboJRN2ERt2ERt3Yt244N8YX9e2PLGLfY4OdUTehUTehUTf2rhvOj/FDXfsji9j72GBX1E1o1E1o1I296ybc86MnR6r4emJkZmZaXRRbKCgokJycHMnIyJDU1FSriwMAsMCaNWusLoKnkEX8kUUAAIo8Ej9kEX9kEQBAVbJITBpVBg0aJEuXLg04QTds2FBOPvlkue6666Ru3boyZswY87gPPvgg5Lays7Nl7NixZpu33XZbwP2PPfaYTJ06Vb755ptYFB0hTJy9TTblFVldDABABLLa1pKhfet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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "split_rekf = msl.RecencyKFoldPanelSplit(n_splits=3, n_periods = 3)\n", "split_rekf.visualise_splits(X, y)" ] }, { "cell_type": "markdown", "id": "94de38a7", "metadata": {}, "source": [ "## Model evaluation" ] }, { "cell_type": "markdown", "id": "1e7e6677", "metadata": {}, "source": [ "To check if a model is actually useful, we need performance metrics.\n", "\n", "In `scikit-learn` terms:\n", "\n", "- A metric is just a function that takes the model's predictions and the true labels and returns a number (e.g. accuracy, mean squared error).\n", "- A scorer is a wrapper that takes three inputs: a fitted `scikit-learn` model, the input features, the true labels; and then calculates the score.\n", "\n", "You can use a metric or a scorer by itself to judge how well a model fits. Or, you can plug it into cross-validation, which will tell you how the model is expected to perform on new, unseen data." ] }, { "cell_type": "markdown", "id": "77ad6fc0", "metadata": {}, "source": [ "### Metrics" ] }, { "cell_type": "markdown", "id": "06444dd2", "metadata": {}, "source": [ "In scikit-learn a metric is a function that measures the quality of predictions. The `macrosynergy.learning` subpackage provides a set of custom evaluation metrics designed to work with `scikit-learn`. Each metric is implemented as a function that takes two inputs:\n", "\n", "- `y_true`: Observed values\n", "- `y_pred`: Values predicted by the model\n", "\n", "The available metrics include:\n", "* [`regression_accuracy()`](https://docs.macrosynergy.com/stable/macrosynergy.learning.model_evaluation.metrics.metrics.html#macrosynergy.learning.model_evaluation.metrics.metrics.regression_accuracy): Accuracy between the signs of predictions and targets\n", "* [`regression_balanced_accuracy()`](https://docs.macrosynergy.com/stable/macrosynergy.learning.model_evaluation.metrics.metrics.html#macrosynergy.learning.model_evaluation.metrics.metrics.regression_balanced_accuracy): Balanced accuracy between the signs of predictions and targets\n", "* [`panel_significance_probability()`](https://docs.macrosynergy.com/stable/macrosynergy.learning.model_evaluation.metrics.metrics.html#macrosynergy.learning.model_evaluation.metrics.metrics.panel_significance_probability): Significance probability of correlation after fitting a linear mixed effects model between predictions and true targets, accounting for cross-sectional correlations present in the panel. See the research piece '[Testing macro trading factors](https://research.macrosynergy.com/testing-macro-trading-factors/)' for more information\n", "* [`sharpe_ratio()`](https://docs.macrosynergy.com/stable/macrosynergy.learning.model_evaluation.metrics.metrics.html#macrosynergy.learning.model_evaluation.metrics.metrics.sharpe_ratio): Naive Sharpe ratio based on the model predictions\n", "* [`sortino_ratio()`](https://docs.macrosynergy.com/stable/macrosynergy.learning.model_evaluation.metrics.metrics.html#macrosynergy.learning.model_evaluation.metrics.metrics.sortino_ratio): Naive Sortino ratio based on the model predictions\n", "* [`correlation_coefficient()`](https://docs.macrosynergy.com/stable/macrosynergy.learning.model_evaluation.metrics.metrics.html#macrosynergy.learning.model_evaluation.metrics.metrics.correlation_coefficient): Specified correlation coefficient between model predictions and \"ground truth\" labels. Available correlation coefficients are \"pearson\", \"spearman\" and \"kendall\"\n", "\n", "With the exception of `panel_significance_probability`, all the above metrics can be computed along different panel dimensions: either across cross-sections or across time periods.\n", "\n", "For example, accuracy can be measured over all samples at once, or it can be calculated separately for each cross-section and then averaged, or separately for each time period and averaged.\n", "\n", "These approaches give estimates of the 'expected' accuracy for a typical cross-section or a typical time period. This is controlled by the type argument in the metric definition, where either:\n", "- `type='cross_section'`\n", "- `type='time_periods'`.\n", "\n", "Sometimes, you may want to take an existing `scikit-learn` metric and adapt it for panel data evaluation along cross-sections or time periods. For this, the subpackage provides the `create_panel_metric()` function. It takes:\n", "- `y_true`\n", "- `y_pred`: a standard scikit-learn metric\n", "- `type` \n", "\n", "and then evaluates the metric along the specified panel axis.\n", "\n", "An example is shown in the code cell below:" ] }, { "cell_type": "code", "execution_count": 44, "id": "0fca541d", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "np.float64(-0.779903424092632)" ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Fit a linear regression model and make predictions\n", "lr = LinearRegression().fit(X, y)\n", "y_pred = lr.predict(X)\n", "\n", "# Calculate expected in-sample R2 metric for a given cross-section\n", "msl.create_panel_metric(\n", " y_true = y,\n", " y_pred = y_pred,\n", " sklearn_metric = r2_score,\n", " type = \"cross_section\"\n", ")" ] }, { "cell_type": "markdown", "id": "8d7cc2a9", "metadata": {}, "source": [ "### Scorers" ] }, { "cell_type": "markdown", "id": "47216b30", "metadata": {}, "source": [ "While metrics are general-purpose evaluation functions in sklearn.metrics scores are what estimators return from their .score() method Currently, `macrosynergy.learning` provides a single scorer: [`neg_mean_abs_corr()`](https://docs.macrosynergy.com/stable/macrosynergy.learning.model_evaluation.html#macrosynergy.learning.model_evaluation.neg_mean_abs_corr).\n", "\n", "Important parameters:\n", "* `estimator`: a fitted custom linear regression model (subclassing BaseRegressionSystem in macrosynergy.learning) that fits a separate linear model per cross-section, storing one beta for each.\n", "* `X_test`: a multi-indexed panel of benchmark returns. It is named as such because it is generally expected for this to be out-of-sample, although in-sample statistics can also be computed.\n", "* `y_test`: a multi-indexed panel of returns, paired with `X_test`.\n", "\n", "Given a collection of estimated betas stored in estimator, hedged returns can be computed for each cross-section in `X_test`. To assess hedge quality, the absolute correlation between the hedged returns and the corresponding benchmark returns is calculated for each cross-section.\n", "\n", "As an overall panel measure, these absolute correlations are then averaged across all cross-sections. Finally, the result is multiplied by -1, since scorers in scikit-learn are defined to be maximized, and lower correlations indicate better hedge performance." ] }, { "cell_type": "markdown", "id": "a47d1d2d", "metadata": {}, "source": [ "## Preprocessing" ] }, { "cell_type": "markdown", "id": "e63aaa4e", "metadata": {}, "source": [ "The `macrosynergy.learning.preprocessing` folder comprises various methods to manipulate the input panel of indicators in a statistical machine learning pipeline, preprocessing them in a number of possible manners before passing the transformed indicators into a predictive model. \n", "\n", "We categorize the possible preprocessing methods into:\n", "- `selectors`\n", "- `scalers`\n", "- `transformers`" ] }, { "cell_type": "markdown", "id": "8b39fc77", "metadata": {}, "source": [ "### Feature selectors " ] }, { "cell_type": "markdown", "id": "15975fc9", "metadata": {}, "source": [ "A `scikit-learn` pipeline can incorporate a layer of feature selection. We provide some custom selectors in the `macrosynergy.learning` subpackage for use over a panel. \n", "\n", "* [`LarsSelector()`](https://docs.macrosynergy.com/stable/macrosynergy.learning.preprocessing.html#macrosynergy.learning.preprocessing.LarsSelector): selects features through the LARS algorithm. \n", " - `n_factors`: Number of factors to be selected\n", " - `fit_intercept`: If `True` includes an intercept in the LARS model\n", "* [`LassoSelector()`](https://docs.macrosynergy.com/stable/macrosynergy.learning.preprocessing.html#macrosynergy.learning.preprocessing.LassoSelector): selects features through a LASSO regression.\n", " - `n_factors`: Number of factors to be selected\n", " - `positive`: When `True`, enforces a positive restriction.\n", "* [`MapSelector()`](https://docs.macrosynergy.com/stable/macrosynergy.learning.preprocessing.html#macrosynergy.learning.preprocessing.MapSelector): selects features based on significance from the Macrosynergy panel test.\n", " - `n_factors`: Number of factors to be selected\n", " - `significance_level`: P-value significance threshold\n", " - `positive`: When `True`, enforces a positive restriction.\n", "\n", "For more information on the panel test, see the research piece '[Testing macro trading factors](https://research.macrosynergy.com/testing-macro-trading-factors/)'." ] }, { "cell_type": "code", "execution_count": 45, "id": "71920eff", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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XGDP_NEGXCPI_NEGXPCG_NEG
cidreal_date
AUD2000-02-29-0.127516-0.162771-2.316805
2000-03-310.188010-0.162771-2.316805
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5444 rows × 3 columns

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" ], "text/plain": [ " XGDP_NEG XCPI_NEG XPCG_NEG\n", "cid real_date \n", "AUD 2000-02-29 -0.127516 -0.162771 -2.316805\n", " 2000-03-31 0.188010 -0.162771 -2.316805\n", " 2000-04-28 0.033589 -0.162771 -3.137645\n", " 2000-05-31 0.175323 -0.676674 -2.763879\n", " 2000-06-30 0.205179 -0.676674 -2.422330\n", "... ... ... ...\n", "ZAR 2025-05-30 -0.426351 1.882825 1.799903\n", " 2025-06-30 -0.030835 1.777107 0.718399\n", " 2025-07-31 0.399231 1.732005 0.136673\n", " 2025-08-29 0.213512 1.568641 0.322765\n", " 2025-09-30 -0.369677 1.279459 -0.600531\n", "\n", "[5444 rows x 3 columns]" ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Keep only factors that a significant at the 5% level based on the MAP test. \n", "map_test = msl.MapSelector(significance_level=0.05).fit(X, y)\n", "map_test.transform(X)" ] }, { "cell_type": "markdown", "id": "8a250581", "metadata": {}, "source": [ "### Feature scalers" ] }, { "cell_type": "markdown", "id": "504a7463", "metadata": {}, "source": [ "Some learning algorithms work best when the input data is scaled before training. Without scaling, models may converge more slowly or even produce misleading results.\n", "\n", "To address this, the package provides the following scaling transformers:\n", "* [`PanelStandardScaler()`](https://docs.macrosynergy.com/stable/macrosynergy.learning.preprocessing.html#macrosynergy.learning.preprocessing.PanelStandardScaler): transforms features by subtracting historical mean and dividing by historical standard deviation\n", "* [`PanelMinMaxScaler()`](https://docs.macrosynergy.com/stable/macrosynergy.learning.preprocessing.html#macrosynergy.learning.preprocessing.PanelMinMaxScaler): transforms features by normalizing them between zero and one\n", "\n", "Both classes admit a `type` parameter:\n", "- `type='panel'`(default) to calculate the mean/std, or min/max over the panel for scaling\n", "- `type='cross_section'` to scale within each cross-section and concatenate each of the scaled cross-sectional features to reconstruct the panel" ] }, { "cell_type": "code", "execution_count": 46, "id": "f635d158", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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XGDP_NEGXCPI_NEGXPCG_NEG
cidreal_date
AUD2000-02-29-0.2174600.047128-0.058952
2000-03-31-0.1055180.047128-0.058952
2000-04-28-0.1603030.047128-0.198993
2000-05-31-0.110019-0.201634-0.135226
2000-06-30-0.099427-0.201634-0.076956
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5444 rows × 3 columns

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" ], "text/plain": [ " XGDP_NEG XCPI_NEG XPCG_NEG\n", "cid real_date \n", "AUD 2000-02-29 -0.217460 0.047128 -0.058952\n", " 2000-03-31 -0.105518 0.047128 -0.058952\n", " 2000-04-28 -0.160303 0.047128 -0.198993\n", " 2000-05-31 -0.110019 -0.201634 -0.135226\n", " 2000-06-30 -0.099427 -0.201634 -0.076956\n", "... ... ... ...\n", "ZAR 2025-05-30 -0.323480 1.037329 0.643387\n", " 2025-06-30 -0.183160 0.986155 0.458875\n", " 2025-07-31 -0.030581 0.964323 0.359628\n", " 2025-08-29 -0.096470 0.885244 0.391377\n", " 2025-09-30 -0.303374 0.745261 0.233856\n", "\n", "[5444 rows x 3 columns]" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Scale by training mean and standard deviation\n", "msl.PanelStandardScaler().fit_transform(X)" ] }, { "cell_type": "markdown", "id": "bb9c3d0b", "metadata": {}, "source": [ "### Feature transformers" ] }, { "cell_type": "markdown", "id": "67cf916b", "metadata": {}, "source": [ "All other preprocessing classes are placed under the general tag of \"transformers\". We provide two such classes:\n", "\n", "* [`PanelPCA`](https://docs.macrosynergy.com/stable/macrosynergy.learning.preprocessing.html#macrosynergy.learning.preprocessing.PanelPCA): transforms features through principal component analysis and returns a multi-indexed dataframe\n", " * `n_components`: If an integer, that many principal components are kept. If it's a float between 0 and 1, enough components are kept to explain up to that proportion of total variance\n", " * `kaiser_criterion`: If `True`, this parameter overrides `n_components` and keeps only the components with associated eigenvalues greater than one\n", " * `adjust_signs`: If `True`, each eigenvector is multiplied by either one or minus one to ensure its projected component is positively correlated with a target vector, if provided.\n", "* [`ZnScoreAverager`](https://docs.macrosynergy.com/stable/macrosynergy.learning.preprocessing.html#macrosynergy.learning.preprocessing.ZnScoreAverager) (**deprecated and to be replaced in a future release**) performs point-in-time zn-scoring (see section on `make_zn_scores` in the [Introduction to Macrosynergy package](https://macrosynergy.com/academy/notebooks/introduction-to-macrosynergy-package/#normalize-panels-with-make-zn-scores) for each feature and averages the result to form a composite signal" ] }, { "cell_type": "code", "execution_count": 47, "id": "1d43d9b7", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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PCA 1PCA 2
cidreal_date
AUD2000-02-290.070187-0.219199
2000-03-310.039984-0.117288
2000-04-280.154591-0.171389
2000-05-310.256607-0.021035
2000-06-300.212212-0.009634
............
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5444 rows × 2 columns

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" ], "text/plain": [ " PCA 1 PCA 2\n", "cid real_date \n", "AUD 2000-02-29 0.070187 -0.219199\n", " 2000-03-31 0.039984 -0.117288\n", " 2000-04-28 0.154591 -0.171389\n", " 2000-05-31 0.256607 -0.021035\n", " 2000-06-30 0.212212 -0.009634\n", "... ... ...\n", "ZAR 2025-05-30 -1.042873 -0.703141\n", " 2025-06-30 -0.916078 -0.559843\n", " 2025-07-31 -0.872366 -0.414922\n", " 2025-08-29 -0.826028 -0.441317\n", " 2025-09-30 -0.567299 -0.576667\n", "\n", "[5444 rows x 2 columns]" ] }, "execution_count": 47, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Scale dataframe before applying PCA\n", "pipe = Pipeline([\n", " (\"scaler\", msl.PanelStandardScaler()),\n", " (\"pca\", msl.PanelPCA(n_components=2)),\n", "]).fit(X,y)\n", "\n", "pipe.transform(X)" ] }, { "cell_type": "markdown", "id": "bc970de3", "metadata": {}, "source": [ "In a `scikit-learn` pipeline, it is often helpful to transform features into new forms. e.g. by scaling or averaging them. \n", "\n", "`The macrosynergy.learning` subpackage extends this functionality with a set of custom transformers:\n", "\n", "* [`PanelStandardScaler()`](https://docs.macrosynergy.com/stable/macrosynergy.learning.preprocessing.html#macrosynergy.learning.preprocessing.PanelStandardScaler): transforms features by subtracting historical mean and dividing by historical standard deviation\n", "* [`PanelMinMaxScaler()`](https://docs.macrosynergy.com/stable/macrosynergy.learning.preprocessing.html#macrosynergy.learning.preprocessing.PanelMinMaxScaler): transforms features by normalizing them between zero and one\n", "* [`FeatureAverager()`](https://docs.macrosynergy.com/stable/macrosynergy.learning.preprocessing.html#macrosynergy.learning.preprocessing.FeatureAverager): condenses features into a single feature through averaging" ] }, { "cell_type": "markdown", "id": "5b785e0e", "metadata": {}, "source": [ "## Forecasting" ] }, { "cell_type": "markdown", "id": "1fef2c16", "metadata": {}, "source": [ "The `macrosynergy.learning.forecasting` submodule comprises a collection of `scikit-learn`-compatible predictor classes that convert a collection of preprocessed features into predictions. \n", "\n", "The following conventional predictor classes are provided in the package:\n", "\n", "* [`NaiveRegressor()`](https://docs.macrosynergy.com/stable/macrosynergy.learning.forecasting.naive_predictors.html#macrosynergy.learning.forecasting.naive_predictors.NaiveRegressor): a naive predictor class that simply returns the average of the input features, for each cross-section and timestamp\n", "* [`LADRegressor()`](https://docs.macrosynergy.com/stable/macrosynergy.learning.forecasting.lad_regressor.html#macrosynergy.learning.forecasting.lad_regressor.LADRegressor): a linear model that estimates parameters by minimising the mean absolute deviations between predictions and provided targets\n", "- Weighted LAD regression models:\n", " * [`SignWeightedLADRegressor()`](https://docs.macrosynergy.com/stable/macrosynergy.learning.forecasting.linear_model.lad_regressors.html#macrosynergy.learning.forecasting.linear_model.lad_regressors.SignWeightedLADRegressor): equalizes the importance of negative return with positive return historical samples, removing a possible sign bias learnt by the model\n", " * [`TimeWeightedLADRegressor()`](https://docs.macrosynergy.com/stable/macrosynergy.learning.forecasting.linear_model.lad_regressors.html#macrosynergy.learning.forecasting.linear_model.lad_regressors.TimeWeightedLADRegressor): increases the importance of more recent samples, by specifying a `half-life` of exponentially decaying weights with time for each historical sample\n", "- Weighted least squares linear regression models:\n", " * [`SignWeightedLinearRegression()`](https://docs.macrosynergy.com/stable/macrosynergy.learning.forecasting.linear_model.ls_regressors.html#macrosynergy.learning.forecasting.linear_model.ls_regressors.SignWeightedLinearRegression): equalizes the importance of negative return with positive return historical samples, removing a possible sign bias learnt by the model\n", " * [`TimeWeightedLinearRegression()`](https://docs.macrosynergy.com/stable/macrosynergy.learning.forecasting.linear_model.ls_regressors.html#macrosynergy.learning.forecasting.linear_model.ls_regressors.TimeWeightedLinearRegression): increases the importance of more recent samples, by specifying a `half-life` of exponentially decaying weights with time for each historical sample" ] }, { "cell_type": "markdown", "id": "64fe5304", "metadata": {}, "source": [ "### Modified regressors" ] }, { "cell_type": "markdown", "id": "bdcabd7a", "metadata": {}, "source": [ "Linear model coefficients tend to be more volatile when only limited data is available. To address this, it can be useful to adjust the coefficients based on their statistical precision, effectively creating an auxiliary factor model. This adjustment is made by estimating the coefficients' standard errors and dividing the coefficients by these values (with a small offset added to avoid issues with very small errors).\n", "\n", "The effect is that imprecise coefficients are shrunk, while precise coefficients are amplified, leading to more reliable estimates overall.\n", "\n", "A key point is that the output of the auxiliary factor model is not an appropriate prediction, but it is a valid signal. \n", "\n", "To distinguish between these two concepts in the classes, we leave the [`predict()`](https://docs.macrosynergy.com/stable/macrosynergy.learning.forecasting.html#macrosynergy.learning.forecasting.CountryByCountryRegression.predict) function to make predictions using the unadjusted factor model, whilst we introduce a [`create_signal()`](https://docs.macrosynergy.com/stable/macrosynergy.learning.forecasting.bootstrap.html#macrosynergy.learning.forecasting.bootstrap.BaseModifiedRegressor.create_signal) function to output signals based on the adjusted factor model. \n", "\n", "All such regressors have a `method` parameter:\n", "- `method='analytic'`\n", "- `method='bootstrap'`\n", "\n", "Below is a list of modified regressors in `macrosynergy.learning.forecasting`:\n", "\n", "* [`ModifiedLinearRegression()`](https://docs.macrosynergy.com/stable/macrosynergy.learning.forecasting.linear_model.ls_regressors.modified_ls_regressors.html#macrosynergy.learning.forecasting.linear_model.ls_regressors.modified_ls_regressors.ModifiedLinearRegression): coefficient-adjusted OLS linear regression model\n", "* [`ModifiedSignWeightedLinearRegression()`](https://docs.macrosynergy.com/stable/macrosynergy.learning.forecasting.linear_model.ls_regressors.modified_ls_regressors.html#macrosynergy.learning.forecasting.linear_model.ls_regressors.modified_ls_regressors.ModifiedSignWeightedLinearRegression): coefficient-adjusted SWLS linear regression model\n", "* [`ModifiedTimeWeightedLinearRegression()`](https://docs.macrosynergy.com/stable/macrosynergy.learning.forecasting.linear_model.ls_regressors.modified_ls_regressors.html#macrosynergy.learning.forecasting.linear_model.ls_regressors.modified_ls_regressors.ModifiedTimeWeightedLinearRegression): coefficient-adjusted TWLS linear regression model" ] }, { "cell_type": "markdown", "id": "a9a0a10b", "metadata": {}, "source": [ "### Regressor systems" ] }, { "cell_type": "markdown", "id": "f84b83ed", "metadata": {}, "source": [ "The regressor systems in `macrosynergy.learning.forecasting` fit a regression model on each cross-section of a panel, inheriting from `msl.BaseRegressionSystem`. \n", "\n", "The following are the currently implemented systems of regressions:\n", "\n", "* [`LinearRegressionSystem()`](https://docs.macrosynergy.com/stable/macrosynergy.learning.forecasting.model_systems.html#macrosynergy.learning.forecasting.model_systems.LinearRegressionSystem): fits a linear regression model on each cross-section of a panel. Stores coefficients and intercepts for each cross-section when only a single feature is in the model \n", "* [`LADRegressionSystem()`](https://docs.macrosynergy.com/stable/macrosynergy.learning.forecasting.html#macrosynergy.learning.forecasting.LADRegressionSystem): fits a LAD regression model on each cross-section of a panel. Stores coefficients and intercepts for each cross-section when only a single feature is in the model \n", "* [`RidgeRegressionSystem()`](https://docs.macrosynergy.com/stable/macrosynergy.learning.forecasting.html#macrosynergy.learning.forecasting.RidgeRegressionSystem): fits a Ridge regression model on each cross-section of a panel. Stores coefficients and intercepts for each cross-section when only a single feature is in the model \n", "* [`CorrelationVolatilitySystem()`](https://docs.macrosynergy.com/stable/macrosynergy.learning.forecasting.html#macrosynergy.learning.forecasting.CorrelationVolatilitySystem): estimates betas through fitting moving average correlation and volatility estimators. This is used solely for the purpose of beta estimation" ] }, { "cell_type": "markdown", "id": "5fe9d974", "metadata": {}, "source": [ "## Sequential signal generation" ] }, { "cell_type": "markdown", "id": "d9543362", "metadata": {}, "source": [ "The `macrosynergy.learning.sequential` submodule contains classes that simulate the experience of a trader using statistical machine learning to create trading signals over time and point-in-time, thus producing data for a valid backtest. " ] }, { "cell_type": "markdown", "id": "5f295f67", "metadata": {}, "source": [ "### Signal optimizer" ] }, { "cell_type": "markdown", "id": "42e45332", "metadata": {}, "source": [ "The [`SignalOptimizer`](https://docs.macrosynergy.com/stable/macrosynergy.learning.sequential.signal_optimizer.html#macrosynergy.learning.sequential.signal_optimizer.SignalOptimizer) class supports sequential model selection, fitting, optimization and forecasting based on quantamental panel data. \n", "\n", "Three use cases are discussed in detail in the notebook [Signal optimization basics](https://academy.macrosynergy.com/academy/Data%20science/Signal%20optimization%20basics/_build/html/Signal%20optimization%20basics.php):\n", "\n", "- Feature selection chooses from candidate features to combine them into an equally weighted score\n", "- Return prediction estimates the predictive relation of features and combines them in accordance with their coefficient into a single prediction\n", "- Classification estimates the relation between features and the sign of subsequent returns and combines their effect into a binary variable of positive or negative returns\n", "\n", "Below, we showcase the second case, focusing on the principals of generation of an optimized regression-based signal:\n", "\n", "The SignalOptimizer constructor builds a wide-format DataFrame that makes panel data suitable for supervised learning. Internally, it relies on the same `categories_df` function introduced earlier to create the required DataFrames. \n", "\n", "As a result, all key arguments of `categories_df` can also be passed directly to `SignalOptimizer` when initializing an object.\n", "\n", "The only additional argument is `generate_labels`, a function applied to the target vector created by `categories_df`. If provided, the transformed target vector is used as the supervised learning labels.\n", "\n", "For example, in directional return classification, you might label positive returns as `1` and negative returns as `-1` using:\n", "\n", "`generate_labels = lambda x: 1 if x >= 0 else -1 `" ] }, { "cell_type": "code", "execution_count": 48, "id": "08d56271", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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XGDP_NEGXCPI_NEGXPCG_NEG
cidreal_date
AUD2000-02-29-0.127516-0.162771-2.316805
2000-03-310.188010-0.162771-2.316805
2000-04-280.033589-0.162771-3.137645
2000-05-310.175323-0.676674-2.763879
2000-06-300.205179-0.676674-2.422330
...............
ZAR2025-05-30-0.4263511.8828251.799903
2025-06-30-0.0308351.7771070.718399
2025-07-310.3992311.7320050.136673
2025-08-290.2135121.5686410.322765
2025-09-30-0.3696771.279459-0.600531
\n", "

5444 rows × 3 columns

\n", "
" ], "text/plain": [ " XGDP_NEG XCPI_NEG XPCG_NEG\n", "cid real_date \n", "AUD 2000-02-29 -0.127516 -0.162771 -2.316805\n", " 2000-03-31 0.188010 -0.162771 -2.316805\n", " 2000-04-28 0.033589 -0.162771 -3.137645\n", " 2000-05-31 0.175323 -0.676674 -2.763879\n", " 2000-06-30 0.205179 -0.676674 -2.422330\n", "... ... ... ...\n", "ZAR 2025-05-30 -0.426351 1.882825 1.799903\n", " 2025-06-30 -0.030835 1.777107 0.718399\n", " 2025-07-31 0.399231 1.732005 0.136673\n", " 2025-08-29 0.213512 1.568641 0.322765\n", " 2025-09-30 -0.369677 1.279459 -0.600531\n", "\n", "[5444 rows x 3 columns]" ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ "so_reg = msl.SignalOptimizer(\n", " df = dfx,\n", " xcats = xcatx,\n", " cids = cids_dux,\n", " freq = \"M\",\n", " lag = 1,\n", " blacklist = fxblack,\n", " xcat_aggs=[\"last\", \"sum\"],\n", ")\n", "\n", "so_reg.X" ] }, { "cell_type": "markdown", "id": "e9ee27cd", "metadata": {}, "source": [ "#### `calculate_predictions()`" ] }, { "cell_type": "markdown", "id": "6e7aa991", "metadata": {}, "source": [ "The [`calculate_predictions()`](https://docs.macrosynergy.com/stable/macrosynergy.learning.sequential.return_forecaster.html#macrosynergy.learning.sequential.return_forecaster.ReturnForecaster.calculate_predictions) function generates and stores predictions for sequentially optimized models, along with their chosen hyperparameters and parameters. Model and hyperparameter selection follow standard cross-validation principles.\n", "\n", "For explainability, the class instance also retains detailed information on model and hyperparameter choices, feature importances, model coefficients, feature selection, and correlations between transformed features.\n", "\n", "Important parameters: \n", "- `models`: a dictionary of scikit-learn predictors or pipelines that contains choices for the type of model to be deployed,\n", "- `hyperparameters`: a nested dictionary defining the hyperparameters to consider for each model type,\n", "- `scorers`: a dictionary of `scikit-learn`-compatible scorer functions used to evaluate a model in the model selection stage,\n", "- `inner_splitters`: a dictionary of cross-validation splitters provided to the cross-validation module. When multiple inner splitters are provided, all splits provided by the splitters are concatenated. \n", "- `search_type`: type of hyperparameter search to undertake. Choices are:\n", " - `grid` to perform a grid search\n", " - `prior` to perform a randomized search where priors can be placed\n", "- `normalize_fold_results`: if `True`, standardizes cross-validation fold scores for a given metric and CV fold. \n", "- `cv_summary`: how to aggregate cross-validation scores across folds, for different models. Options are:\n", " - `'mean'`(default)\n", " - `'median'`\n", " - `'mean-std'`\n", " - `'mean/std'`\n", "\n", " Alternatively, a callable can be passed into `cv_summary`, directly specifying the type of aggregation." ] }, { "cell_type": "markdown", "id": "219f3879", "metadata": {}, "source": [ "In order to showcase the different options that `SignalOptimizer` provides, we construct a pipeline that involves feature scaling, feature selection and predictor training. \n", "\n", "In the example below, we train a Ridge regression model sequentially over the realized trading history. At each retraining date (every three months):\n", "- The data is scaled,\n", "- One feature is removed from the training set,\n", "- Ridge regression is selected from 50 candidate alpha values.\n", "\n", "Model selection uses cross-validation that balances $R^{2}$ and balanced accuracy, combining splits from both `RollingKFoldPanelSplit` (5 folds) and `ExpandingKFoldPanelSplit` (3 folds initially).\n", "\n", "During the first three years of the backtest, the expanding splitter provides 3 folds, resulting in a total of 8 folds. After this period, it switches to 5 folds, giving a total of 10 folds for the remainder of the training history." ] }, { "cell_type": "code", "execution_count": 49, "id": "089f4b58", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "f885cc6f80f946028a96459590daaa4e", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/91 [00:00" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "so_reg.models_heatmap(\"MACRO_OPTREG\")" ] }, { "cell_type": "markdown", "id": "c70e194f", "metadata": {}, "source": [ "#### `feature_importance_timeplot()`" ] }, { "cell_type": "markdown", "id": "16791947", "metadata": {}, "source": [ "The [`feature_importance_timeplot`](https://docs.macrosynergy.com/stable/macrosynergy.learning.sequential.signal_optimizer.html#macrosynergy.learning.sequential.signal_optimizer.SignalOptimizer.feature_importance_timeplot) function creates a time plot of linear model regression coefficients for each feature. \n", "\n", "For these statistics to be recorded, the underlying `scikit-learn` predictor class (in this case, `LinearRegression`) must contain `coef_` and `intercept_` attributes. \n", "\n", "Gaps in the lines appear either when a model without the required attributes (e.g. a KNN or Random Forest) is selected or a feature selector (in this case, `LassoSelector`) doesn't select these features." ] }, { "cell_type": "code", "execution_count": 51, "id": "cad51344", "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "so_reg.feature_importance_timeplot(name=\"MACRO_OPTREG\", figsize=(16, 6))" ] }, { "cell_type": "markdown", "id": "7638c340", "metadata": {}, "source": [ "#### `coefs_stackedbarplot()`" ] }, { "cell_type": "markdown", "id": "e8b1d59d", "metadata": {}, "source": [ "The [`coefs_stackedbarplot()`](https://docs.macrosynergy.com/stable/macrosynergy.learning.sequential.signal_optimizer.html#macrosynergy.learning.sequential.signal_optimizer.SignalOptimizer.coefs_stackedbarplot) method is an alternative to `coefs_timeplot()` and displays a stacked bar plot of average annual model coefficients over time. " ] }, { "cell_type": "code", "execution_count": 52, "id": "e7998ff3", "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "so_reg.coefs_stackedbarplot(name=\"MACRO_OPTREG\", figsize=(16, 6))" ] }, { "cell_type": "markdown", "id": "e38caea8", "metadata": {}, "source": [ "#### `intercepts_timeplot()`" ] }, { "cell_type": "markdown", "id": "58a2d9dc", "metadata": {}, "source": [ "Similarly to model coefficients, changing model intercepts can be visualised over time through a timeplot using the [`intercepts_timeplot()`](https://docs.macrosynergy.com/stable/macrosynergy.learning.sequential.signal_optimizer.html#macrosynergy.learning.sequential.signal_optimizer.SignalOptimizer.intercepts_timeplot) function." ] }, { "cell_type": "code", "execution_count": 53, "id": "87963b1a", "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "so_reg.intercepts_timeplot(name=\"MACRO_OPTREG\", figsize=(16, 6))" ] }, { "cell_type": "markdown", "id": "2932f62f", "metadata": {}, "source": [ "#### `nsplits_timeplot()`" ] }, { "cell_type": "markdown", "id": "61dd13ba", "metadata": {}, "source": [ "The [`nsplits_timeplot()`](https://docs.macrosynergy.com/stable/macrosynergy.learning.sequential.signal_optimizer.html#macrosynergy.learning.sequential.signal_optimizer.SignalOptimizer.nsplits_timeplot) displays number of cross-validation splits that are applied over time. \n", "\n", "This is useful if `split_functions` was specified when running a pipeline. " ] }, { "cell_type": "code", "execution_count": 54, "id": "33901db6", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "so_reg.nsplits_timeplot(name=\"MACRO_OPTREG\")" ] }, { "cell_type": "code", "execution_count": 55, "id": "fc4f2481", "metadata": {}, "outputs": [], "source": [ "dfx = msm.update_df(dfx, so_reg.get_optimized_signals(\"MACRO_OPTREG\"))" ] }, { "cell_type": "markdown", "id": "e1d1a08c", "metadata": {}, "source": [ "### Beta estimator" ] }, { "cell_type": "markdown", "id": "e069bdf1", "metadata": {}, "source": [ "The [`BetaEstimator`](https://docs.macrosynergy.com/stable/macrosynergy.learning.sequential.beta_estimator.html#macrosynergy.learning.sequential.beta_estimator.BetaEstimator) class is used to calculate sequential betas for each cross-section of a financial market return panel, with respect to a common benchmark returns. Out-of-sample hedged returns are calculated between model refreshing dates. The same model selection process that `SignalOptimizer` uses is used to select between candidate models from which coefficients are extracted. \n", "\n", "The constructor of `BetaEstimator` constructs a wide format dataframe where the benchmark ticker, the sole predictor of concurrent market returns, is replicated across all return cross-sections in the panel. In the below example, we compute betas for each cross-section of our FX forward returns with respect to S&P 500 futures. " ] }, { "cell_type": "code", "execution_count": 56, "id": "233cfff1", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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EQXR_NSA
cidreal_date
AUDvUSD2000-01-03-1.172349
2000-01-04-3.749659
2000-01-050.120414
2000-01-06-0.672091
2000-01-074.024217
.........
ZARvUSD2025-08-280.330973
2025-08-29-0.686613
2025-09-010.000000
2025-09-02-0.729983
2025-09-030.494125
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153417 rows × 1 columns

\n", "
" ], "text/plain": [ " EQXR_NSA\n", "cid real_date \n", "AUDvUSD 2000-01-03 -1.172349\n", " 2000-01-04 -3.749659\n", " 2000-01-05 0.120414\n", " 2000-01-06 -0.672091\n", " 2000-01-07 4.024217\n", "... ...\n", "ZARvUSD 2025-08-28 0.330973\n", " 2025-08-29 -0.686613\n", " 2025-09-01 0.000000\n", " 2025-09-02 -0.729983\n", " 2025-09-03 0.494125\n", "\n", "[153417 rows x 1 columns]" ] }, "execution_count": 56, "metadata": {}, "output_type": "execute_result" } ], "source": [ "be = msl.BetaEstimator(\n", " df = dfx,\n", " xcats = [\"FXXR_NSA\"],\n", " benchmark_return = \"USD_EQXR_NSA\",\n", " cids = cids,\n", ")\n", "\n", "be.X" ] }, { "cell_type": "markdown", "id": "5c2b7889", "metadata": {}, "source": [ "#### `estimate_beta()`" ] }, { "cell_type": "markdown", "id": "1d6c59b6", "metadata": {}, "source": [ "The [`estimate_beta()`](https://docs.macrosynergy.com/stable/macrosynergy.learning.sequential.beta_estimator.html#macrosynergy.learning.sequential.beta_estimator.BetaEstimator.estimate_beta) method in the Macrosynergy package is used to work out how much an asset (or return series) moves in response to a chosen factor, while also estimating out-of-sample hedged returns at a set retraining frequency. Instead of just fitting a simple regression, it uses the same learning pipeline as SignalOptimizer, meaning it applies cross-validation to pick the best model type and hyperparameters, and then updates them as specified. The function doesn't just output betas, it also gives you a way to test how those betas would have worked in practice by producing hedged return series.\n", "\n", "Many parameters for `estimate_beta()` are also used for the `calculate_predictions()` method within `SignalOptimizer`. \n", "\n", "The only parameters that differ are:\n", "\n", "* `beta_xcat`: Category name for stored estimated betas\n", "* `hedged_return_xcat`: Category name for stored out-of-sample hedged returns\n", "\n", "Lastly, the `models` dictionary provided in `estimate_beta` expects models to inherit from the `BaseRegressionSystem` class, to ensure that different models are fit on different cross-sections, resulting in diverse betas amongst cross-sections." ] }, { "cell_type": "code", "execution_count": 57, "id": "696c8ce4", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "e6018591a60d4c8d89876fc1bceaae8b", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/26 [00:00" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "be.models_heatmap(\"BETA_NSA\", figsize=(12, 2))" ] }, { "cell_type": "markdown", "id": "9cdc0d7e", "metadata": {}, "source": [ "#### `get_betas()`" ] }, { "cell_type": "markdown", "id": "87d9ed29", "metadata": {}, "source": [ "The [`get_betas()`](https://docs.macrosynergy.com/stable/macrosynergy.learning.sequential.beta_estimator.html#macrosynergy.learning.sequential.beta_estimator.BetaEstimator.get_betas) function can be used to extract the calculated panel of betas." ] }, { "cell_type": "code", "execution_count": 59, "id": "bc1d96af", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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real_datecidxcatvalue
02000-03-24AUDBETA_NSA0.013376
12000-03-27AUDBETA_NSA0.013376
22000-03-28AUDBETA_NSA0.013376
32000-03-29AUDBETA_NSA0.013376
42000-03-30AUDBETA_NSA0.013376
...............
1519092025-08-28ZARBETA_NSA0.151301
1519102025-08-29ZARBETA_NSA0.151301
1519112025-09-01ZARBETA_NSA0.151301
1519122025-09-02ZARBETA_NSA0.151301
1519132025-09-03ZARBETA_NSA0.151301
\n", "

151914 rows × 4 columns

\n", "
" ], "text/plain": [ " real_date cid xcat value\n", "0 2000-03-24 AUD BETA_NSA 0.013376\n", "1 2000-03-27 AUD BETA_NSA 0.013376\n", "2 2000-03-28 AUD BETA_NSA 0.013376\n", "3 2000-03-29 AUD BETA_NSA 0.013376\n", "4 2000-03-30 AUD BETA_NSA 0.013376\n", "... ... ... ... ...\n", "151909 2025-08-28 ZAR BETA_NSA 0.151301\n", "151910 2025-08-29 ZAR BETA_NSA 0.151301\n", "151911 2025-09-01 ZAR BETA_NSA 0.151301\n", "151912 2025-09-02 ZAR BETA_NSA 0.151301\n", "151913 2025-09-03 ZAR BETA_NSA 0.151301\n", "\n", "[151914 rows x 4 columns]" ] }, "execution_count": 59, "metadata": {}, "output_type": "execute_result" } ], "source": [ "be.get_betas(\"BETA_NSA\")" ] }, { "cell_type": "markdown", "id": "6d9aae66", "metadata": {}, "source": [ "#### `get_hedged_returns()`" ] }, { "cell_type": "markdown", "id": "854d06fd", "metadata": {}, "source": [ "The [`get_hedged_returns()`](https://docs.macrosynergy.com/stable/macrosynergy.learning.sequential.beta_estimator.html#macrosynergy.learning.sequential.beta_estimator.BetaEstimator.get_hedged_returns) function can be used to extract the calculated panel of hedged returns." ] }, { "cell_type": "code", "execution_count": 60, "id": "e827d1fd", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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real_datecidxcatvalue
02000-03-27AUDHEDGED_RETURN_NSA0.980193
12000-03-28AUDHEDGED_RETURN_NSA0.119258
22000-03-29AUDHEDGED_RETURN_NSA-0.506133
32000-03-30AUDHEDGED_RETURN_NSA0.233666
42000-03-31AUDHEDGED_RETURN_NSA-0.827272
...............
1554052025-08-28ZARHEDGED_RETURN_NSA0.309650
1554062025-08-29ZARHEDGED_RETURN_NSA0.104120
1554072025-09-01ZARHEDGED_RETURN_NSA0.762983
1554082025-09-02ZARHEDGED_RETURN_NSA-0.629873
1554092025-09-03ZARHEDGED_RETURN_NSA0.097560
\n", "

155410 rows × 4 columns

\n", "
" ], "text/plain": [ " real_date cid xcat value\n", "0 2000-03-27 AUD HEDGED_RETURN_NSA 0.980193\n", "1 2000-03-28 AUD HEDGED_RETURN_NSA 0.119258\n", "2 2000-03-29 AUD HEDGED_RETURN_NSA -0.506133\n", "3 2000-03-30 AUD HEDGED_RETURN_NSA 0.233666\n", "4 2000-03-31 AUD HEDGED_RETURN_NSA -0.827272\n", "... ... ... ... ...\n", "155405 2025-08-28 ZAR HEDGED_RETURN_NSA 0.309650\n", "155406 2025-08-29 ZAR HEDGED_RETURN_NSA 0.104120\n", "155407 2025-09-01 ZAR HEDGED_RETURN_NSA 0.762983\n", "155408 2025-09-02 ZAR HEDGED_RETURN_NSA -0.629873\n", "155409 2025-09-03 ZAR HEDGED_RETURN_NSA 0.097560\n", "\n", "[155410 rows x 4 columns]" ] }, "execution_count": 60, "metadata": {}, "output_type": "execute_result" } ], "source": [ "be.get_hedged_returns(\"HEDGED_RETURN_NSA\")" ] }, { "cell_type": "markdown", "id": "ca3e6d52", "metadata": {}, "source": [ "#### `evaluate_hedged_returns()`" ] }, { "cell_type": "markdown", "id": "a347b66f", "metadata": {}, "source": [ "The [`evaluate_hedged_returns()`](https://docs.macrosynergy.com/stable/macrosynergy.learning.sequential.beta_estimator.html#macrosynergy.learning.sequential.beta_estimator.BetaEstimator.evaluate_hedged_returns) function can be used to determine the average correlation between the calculated hedged returns and the inputted panel of contract returns. This gives a measure of the quality of hedge. \n", "\n", "Important parameters:\n", "\n", "* `hedged_return_xcat`: Name of the hedged return category calculated in the `BetaEstimator` instance\n", "* `correlation_types`: Type of correlation to calculate. e.g. `'pearson'`(default), `'kendall'`, or `'spearman'`\n", "* `blacklist`: Blacklisted periods that should be excluded from correlation calculation\n", "* `freqs`: String or list of strings of frequencies at which correlations are to be calculated\n", "\n", "In the below example, we can see that hedging has produced lower absolute correlations with the benchmark on average than the unadjusted returns, meaning that hedge was successful." ] }, { "cell_type": "code", "execution_count": 61, "id": "8341f0d6", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
pearsonkendallspearman
benchmark returnreturn categoryfrequency
USD_EQXR_NSAHEDGED_RETURN_NSAM0.1887260.1021060.149935
Q0.2007740.1127120.165224
FXXR_NSAM0.3541680.2225690.321713
Q0.3758710.2321730.332494
\n", "
" ], "text/plain": [ " pearson kendall spearman\n", "benchmark return return category frequency \n", "USD_EQXR_NSA HEDGED_RETURN_NSA M 0.188726 0.102106 0.149935\n", " Q 0.200774 0.112712 0.165224\n", " FXXR_NSA M 0.354168 0.222569 0.321713\n", " Q 0.375871 0.232173 0.332494" ] }, "execution_count": 61, "metadata": {}, "output_type": "execute_result" } ], "source": [ "be.evaluate_hedged_returns(\n", " hedged_return_xcat=\"HEDGED_RETURN_NSA\",\n", " correlation_types=[\"pearson\", \"kendall\", \"spearman\"],\n", " blacklist = fxblack,\n", " freqs = [\"M\", \"Q\"],\n", ")" ] } ], "metadata": { "kernelspec": { "display_name": "base", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.13.5" } }, "nbformat": 4, "nbformat_minor": 5 }