diff --git a/.gitignore b/.gitignore index 613ebe6..ea6ac42 100644 --- a/.gitignore +++ b/.gitignore @@ -24,3 +24,7 @@ .idea/vcs.xml .idea/workspace.xml +.idea/other.xml +alphapy/examples/Trading System/.ipynb_checkpoints/A Trading System-checkpoint.ipynb +*.pkl +*.png diff --git a/alphapy/data.py b/alphapy/data.py index f92da49..d15f658 100644 --- a/alphapy/data.py +++ b/alphapy/data.py @@ -31,7 +31,9 @@ from alphapy.frame import read_frame from alphapy.globals import ModelType from alphapy.globals import Partition, datasets -from alphapy.globals import PSEP, SSEP +from alphapy.globals import PD_INTRADAY_OFFSETS +from alphapy.globals import PD_WEB_DATA_FEEDS +from alphapy.globals import PSEP, SSEP, USEP from alphapy.globals import SamplingMethod from alphapy.globals import WILDCARD @@ -282,6 +284,53 @@ def sample_data(model): return model +# +# Function enhance_intraday_data +# + +def enhance_intraday_data(df): + r"""Add columns to the intraday dataframe. + + Parameters + ---------- + df : pandas.DataFrame + The intraday dataframe. + + Returns + ------- + df : pandas.DataFrame + The dataframe with bar number and end-of-day columns. + + """ + + # Convert the columns to proper data types + + index_column = 'datetime' + dt_column = df['date'] + ' ' + df['time'] + df[index_column] = pd.to_datetime(dt_column) + cols_float = ['open', 'high', 'low', 'close', 'volume'] + df[cols_float] = df[cols_float].astype(float) + + # Number the intraday bars + + date_group = df.groupby('date') + df['bar_number'] = date_group.cumcount() + + # Mark the end of the trading day + + df['end_of_day'] = False + df.loc[date_group.tail(1).index, 'end_of_day'] = True + + # Set the data frame's index + df.set_index(pd.DatetimeIndex(df[index_column]), drop=True, inplace=True) + + # Return the enhanced frame + + del df['date'] + del df['time'] + return df + + # # Function get_google_data # @@ -345,25 +394,12 @@ def get_google_data(symbol, lookback_period, fractal): dt = datetime.fromtimestamp(day_item + (interval * offset)) dt = pd.to_datetime(dt) dt_date = dt.strftime('%Y-%m-%d') - record = (dt, dt_date, open_item, high_item, low_item, close_item, volume_item) + dt_time = dt.strftime('%H:%M:%S') + record = (dt_date, dt_time, open_item, high_item, low_item, close_item, volume_item) records.append(record) # create data frame - cols = ['datetime', 'date', 'open', 'high', 'low', 'close', 'volume'] + cols = ['date', 'time', 'open', 'high', 'low', 'close', 'volume'] df = pd.DataFrame.from_records(records, columns=cols) - # convert to proper data types - cols_float = ['open', 'high', 'low', 'close'] - df[cols_float] = df[cols_float].astype(float) - df['volume'] = df['volume'].astype(int) - # number the intraday bars - date_group = df.groupby('date') - df['bar_number'] = date_group.cumcount() - # mark the end of the trading day - df['end_of_day'] = False - del df['date'] - df.loc[date_group.tail(1).index, 'end_of_day'] = True - # set the index to datetime - df.index = df['datetime'] - del df['datetime'] # return the dataframe return df @@ -373,7 +409,7 @@ def get_google_data(symbol, lookback_period, fractal): # def get_pandas_data(schema, symbol, lookback_period): - r"""Get Yahoo Finance daily data. + r"""Get Pandas Web Reader data. Parameters ---------- @@ -391,6 +427,12 @@ def get_pandas_data(schema, symbol, lookback_period): """ + # Quandl is a special case. + + if 'quandl' in schema: + schema, symbol_prefix = schema.split(USEP) + symbol = SSEP.join([symbol_prefix, symbol]).upper() + # Calculate the start and end date for Yahoo. start = datetime.now() - timedelta(lookback_period) @@ -398,25 +440,26 @@ def get_pandas_data(schema, symbol, lookback_period): # Call the Pandas Web data reader. - df = None try: df = web.DataReader(symbol, schema, start, end) - df = df.rename(columns = lambda x: x.lower().replace(' ','')) except: + df = None logger.info("Could not retrieve data for: %s", symbol) return df # -# Function get_feed_data +# Function get_market_data # -def get_feed_data(group, lookback_period): +def get_market_data(model, group, lookback_period, resample_data): r"""Get data from an external feed. Parameters ---------- + model : alphapy.Model + The model object describing the data. group : alphapy.Group The group of symbols. lookback_period : int @@ -429,27 +472,71 @@ def get_feed_data(group, lookback_period): """ + # Unpack model specifications + + directory = model.specs['directory'] + extension = model.specs['extension'] + separator = model.specs['separator'] + + # Unpack group elements + gspace = group.space schema = gspace.schema fractal = gspace.fractal + # Determine the feed source - if 'd' in fractal: - # daily data (date only) - logger.info("Getting Daily Data") - daily_data = True - else: + + if any(substring in fractal for substring in PD_INTRADAY_OFFSETS): # intraday data (date and time) - logger.info("Getting Intraday Data (Google 50-day limit)") - daily_data = False + logger.info("Getting Intraday Data [%s] from %s", fractal, schema) + intraday_data = True + index_column = 'datetime' + else: + # daily data or higher (date only) + logger.info("Getting Daily Data [%s] from %s", fractal, schema) + intraday_data = False + index_column = 'date' + # Get the data from the relevant feed + + data_dir = SSEP.join([directory, 'data']) + pandas_data = any(substring in schema for substring in PD_WEB_DATA_FEEDS) n_periods = 0 + for item in group.members: logger.info("Getting %s data for last %d days", item, lookback_period) - if daily_data: + # Locate the data source + if schema == 'data': + fname = frame_name(item.lower(), gspace) + df = read_frame(data_dir, fname, extension, separator) + if not intraday_data: + df.set_index(pd.DatetimeIndex(df[index_column]), + drop=True, inplace=True) + elif schema == 'google' and intraday_data: + df = get_google_data(item, lookback_period, fractal) + elif pandas_data: df = get_pandas_data(schema, item, lookback_period) else: - df = get_google_data(item, lookback_period, fractal) + logger.error("Unsupported Data Source: %s", schema) + # Now that we have content, standardize the data if df is not None and not df.empty: + logger.info("Rows: %d", len(df)) + # standardize column names + df = df.rename(columns = lambda x: x.lower().replace(' ','')) + # add intraday columns if necessary + if intraday_data: + df = enhance_intraday_data(df) + # order by increasing date if necessary + df = df.sort_index() + # resample data + if resample_data: + df = df.resample(fractal).agg({'open' : 'first', + 'high' : 'max', + 'low' : 'min', + 'close' : 'last', + 'volume' : 'sum'}) + logger.info("Rows after Resampling at %s: %d", + fractal, len(df)) # allocate global Frame newf = Frame(item.lower(), gspace, df) if newf is None: @@ -460,5 +547,6 @@ def get_feed_data(group, lookback_period): n_periods = df_len else: logger.info("No DataFrame for %s", item) + # The number of periods actually retrieved return n_periods diff --git a/alphapy/examples/Kaggle/input/gender_submission.csv b/alphapy/examples/Kaggle/input/gender_submission.csv deleted file mode 100644 index 7594506..0000000 --- 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System/A Trading System.ipynb index 9d51134..67ea416 100644 --- a/alphapy/examples/Trading System/A Trading System.ipynb +++ b/alphapy/examples/Trading System/A Trading System.ipynb @@ -2,52 +2,466 @@ "cells": [ { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, + "execution_count": 1, + "metadata": {}, "outputs": [], "source": [ + "%matplotlib inline\n", "import pandas as pd\n", "import pyfolio as pf" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "'/Users/markconway/Projects/AlphaPy/alphapy/examples/Trading System'" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "pwd" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/Users/markconway/Projects/AlphaPy/alphapy/examples/Trading System/systems\n" + ] + } + ], "source": [ "cd systems" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "faang_closer_positions_1d.csv faang_closer_trades_1d.csv\r\n", + "faang_closer_returns_1d.csv faang_closer_transactions_1d.csv\r\n" + ] + } + ], "source": [ "ls" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "df = pd.read_csv('faang_closer_returns_1d.csv', index_col='date', squeeze=True)\n", - "df.index = pd.to_datetime(df.index, utc=True)\n", - "pf.create_returns_tear_sheet(df)" + "df.index = pd.to_datetime(df.index, utc=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pf.plot_monthly_returns_heatmap(df)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/markconway/anaconda/lib/python3.6/site-packages/numpy/core/fromnumeric.py:57: FutureWarning: 'argmin' is deprecated. Use 'idxmin' instead. The behavior of 'argmin' will be corrected to return the positional minimum in the future. Use 'series.values.argmin' to get the position of the minimum now.\n", + " return getattr(obj, method)(*args, **kwds)\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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9b9VQKqcmLvA3bcZsFVZ39bx+SGNCWLlVJ+HiV7tteQ98ueKDDcAJgnkXLDA1u3jfLNOz7vuc258eP/5u5/a7ceQJrWDHz/o8shpjMBfVcoQHmk4MD97vosvqNdiwGc66qP3447HEk0swxc1Gldsnp9JQ3kOPdhPaSWe7KKy5fgoPTW33kIZc16MAkUEwP5eOResUjgUKQqFcTYM4gikQvFYsiz9LOwEVnu1EY553P1Mzi/1EnVoPwpwwZEx4tCLkFqz2ZoALr4DHXpI6OiG1dTZM9uJMBsG+XluAyYqb9ELkldQbb9JApZp/s8b29CTHZIAPZ68JJqR2K8RyNdI8/GdWLRbDvmGTW+HvfTA6jz/Gg/fDzm3uuh3+kPz3l8rk+jxis0YwF9X2s4jEvSWwx8d3bHlY56tFKTWaNWrzbtxxdNrkrPPxHHS48yOF8/bblh58OMnfXgOXhcHenyH6rNSpxhM5r7ec6CKlNkft3cPisFZbvE5rde+GBZ7WgJLzfc3t976vHM2jE7OV1tPF1BAx4dGK2kJjlJNIo+AAH2VRSh+UxOdRdRE14fdwA4ZVh0gLzSNzPKXRbFWussi0NW5oQXt42UewqNe+gpAO16QVB21yq/MHd6RO3KB57HsQanVnQtp8XP77s4ELifCINI/J6ZwAh+QN/n9xq/9Nx7kw3E7Jah5hlX14NO5yGZ7/5kYtYxBRPFrPCI+wOIoWVIMgWbzRmcYTLlHwdTzt5Y2vJz6P7GfR1hrDxJS7XAf2wZ673fvrtfTaQWpN6PQ6Bc1jyD72MTWYD4pMiGwewe5cizUP3AQfwj0nppyQ2bsrdXSWys01j1KsVQSzVSw8QmbywuL3jwvZSacZieZBah5STXMkWr63DKc/yX/PpdSPoXXnB1i9Hi57c+pgzpI1W5HjMA/5FXmrxtjnoXU45Kj2Y84juzKdn3Pbjjh+8XinonsqONqlT4uMkCmf1TzKPkdmkGarmn8WwkKjMNFiL49kcZh51sJiphnVSaDkHNt7dqXji/0gS83zGBGLgwmPVhx+rHswZlsIj8qkn2TCzRVuxqCSluDJL4YTz0yje8Rvn8hxmJeCjT+jyZQzmod0+pCMGFlbeTMSn4emAhlS00w7TjkPfuv9cPK5qSM0OMpXtxFAeWYrkcbJslJ1P600jyCw8hYLRciucOs+VDkvtLnhfV7o9PU28dclcNypqcAftOYRAks6mVwTLaDJvRjyN/I+SzvNo+R9dUGgJaWKfGRmchzpSHYkQjse/xAw4dGKxz0bLvx12NiionXZm5lq0WQvkobqlkpukrrwCjj+Uf6G86vgiZzJJEyW2Qdgkc9jwA7JXpNdsTajXPErPL9/+H6LmK1ipmbS723/HnecvKTEhnPn+TykcbIUSSNrssSRdUqjVtAJWd9FsO8vVRj1imBSjfNJjjoJnvwSF+Y+yBVy0PKbTfTtaFZTqlJtIpDamK2qk74O3Xyjcz0RctF7g+ZRVBDElSpMeIwo1Qk48YzFmcsx2RIQcZ7HhVfAqU+Aw3zdo5DFGm7yPM0jmSyzmkc1s8+YO8zrRc1WUbTVxiPdtahOwpqCORKBs54Bhx7lvvO9u5z5Ky/KKqZUyYm2ksXjnl6Vlk25765o8tLGiSPvehchr1ikNAn1jgmTW7/mF/U5TJVMsuuqtT5kdoCaR23BT/QdLqqSnIsmU2HQ8rPPmlJA8yinBRTDeZKIy4zmEf/fdsz1MIChCg9zmHdLcH6HJzRxqlbd6vgpL0n3DTkcwcyRF24aJstaxgzW4PMIE+qYZpkHh18zO3NMuZpqHpse4mpY1euLs/PbMb0KnvQi+Mf3Ohv0k14EJ53V5tzZGmP+90XCw5dB2bXD1TPSuquPFVcTQLszWwnp8eq+nlS77yBoLP3yeRAtlGLKfrVeG6BPrl73tak6NZd5x3Oz4I3gX4xDsfPC57MkIdwLTpAkPo96al5LCJplUc3DC44kKnM4FYxNeHRLKescDA9UnlbhNY8Nm+HiV7lJZ9E+wbSyu+FwjWaraqOTftxIzB1FhEc5XeFVKu57WOqzUio7n9PsWjjzwgLnzik3kqd5hLDhUHIluxoME38rDbYViRCITB/lSvtotU5t6Z0SRxbGBA1gYUCacZjM43L1hd8L7rttpnm0CCdvpXls2ASPPAd+/F9OQ7zrZneyEK5fzvF5aCeaR/jdNI/xJUk8y2geeRNjEDSlFjb7cLPu2+PC/IKzLT5eJWgeYxptlWgeBbSHIEyzvoalsP5Q58cqWtIhLmUCkQkxR/OI824W9dPwfy9Z88isTOtNEkybvq9PBMdt1nxWqTafcHt6/rqLPKtOpIuReq1DjTxHs48JuUgNn0dTR3gzymV43LPcz7//kyvFHzSGbPRkXgWBlkPW9NoO0e9pwqNbsnblPB9FIInrbnXT+UtS9pnRYZLI+jykROGbbeTwN3wh4RH8RBTzkbRCxFUvLsoih3kTs9XMajfJJKYNjf4PJgbya5kVITFb+b/rteJmu2xl4F4Sl+KJmV6d7yfoNQ9sc8meG32Zl5Al3om5LJtzkaVcATKRjYk1sqDLuEF7yQlv7tQ3FdfHG6Lf0xzm3ZL1eYRM6LybsUh/hclpXya84hy7oSdA1mw1pmWcgWjSKTABhiStUHdokJQrbmUbd3KU0uJrO73al1k/4PbJJoyW/Pg79dMEsgEZWnc29ULv7eMio5nwmJx2pXwO7PWNz/p0/v173fXZ8wCJDy3O8wg5QS3xz2s7s5Vm3pPVHlpRie5hIKmuEOjUN1VPpNdQWzOY8OiWMDE0OMybTHRFNI+pWfiVK13hvz073cMXt1yF1nbYcSBk2hfxAUxMp8l93ZqtOiVMDnfc5P5vltg4s9oFSMROUYgiebymWETTyiUyP4XSFIWFRz/NVj7qJy+K7IQz3f/33eHu416wMNdY5DH4mAJVX4dqYc4tvO76Odx7R+tjhnm4lcO8me+o6HdbqaTHyDN9duKbWphLK1d0GlnWY0x4dEswKcR27jh7NLtv7PhsxkGHpeaqianFE+e4C49QqDCuTtyMEGcvMpj+FDFByAdzVLMS47PrfALjfH60T7heSx1/yd9j9Zoz09AkUq/ZZ+iX2WphHpD8UvonngkX/6ZbBPWqqu89tzpBHteRi/0RE1Pu8+7Z6dr2zh9wpWlaah8FzFa51ZUprnkkEYOkwr+So3kUuU733+MXlLWl57T0CBMe3ZJc+IzDPFfzKNjZbWrWrVZLvsNdVpOJs67Hkbl97jMc8dD2+wZBHBIvB0nQJEIEVa2Wry3NrPZFM58Jx56ShmPG90I34w/32IF9rjJvrba4xlrL9/aJhXk3Ka5v0nxp7cG+E1+v7lNvstt+m/N3QOPnm5x1fe3Pe64Pn667xUfTjn60XuxB6jDP+witHOYxiY8SV6QTXZzRXvRYtQVnJnz8pb6LqQmP8SWpeBposSoJJq56m4ep6kuelEqR5pHN8+ijI7TfLHifTrsM70CIUhu02Wphzv0ftIlWpeCPPAGecJkTHoJflQfh4cONi4QmNyOutPy0K10UT9H39etGqc27a7OmSZmXIPR7ZVoJ+SMH9sID230XSEkFdaXi8mtOfUIaPl0qO6Hbjmam5FIpZ6HWRlvJUvGJhiXfsVLraSh+cv6C16nuy9w8/Gw3T8zPFRtDH7Boq25JHGEZs1We5pGYtto8TEEVn1mTJhbGK5VQeXdcpUfo492sJESW4PMYtNkqTE6UfB8RbV8Kft0haU/2cF+E1Ws3wi9MblJypcM7MVv1Gq3DNl9ivFxp/p0E4ZHVfvbsdNc0L8+p9Yldsuelvw2fvsppH6UokTN8v5MzaXJfqdx6gm0XbSXihX5OtFVhs5WvGrHuELjy3XDTDTmliQouBoPfbdU6N665nFYAA8KER7ckN11Rs1XBldiL3+bsm//yN9EN7Ekm0nEVHvXOTDiSt/obALHmEX5vV1Nr3UY3ee3f4zOega0XwO6dxVeqeYQJuFQqLjjC+3r9tdUW3Oq/MgknbG1h8gk1m6Jt83Puvj6wD447pbPz1usu3Hn1Qb5KA+74IUAh3FOxn2xiktZfQMjFaRM+n6t5FBQe4f4N53jI6Yv3KWUtGE1QX11hYsrdZ7sfKDaGPmBmq25JVlbiEoH273Hb84THwUe4G6RQZrVXwZuF91Ym2pu/RhH1Wbad1nkSSUNmB0U4n5TSMNzVTcq3B9Yc7D6b1tMJZ/VBcO6l3Y0l7v0w2Ynw6Ifm4T/XiWfCRa9svl+YwGMO7Elb6HaaoxBW3YkPIRNtFTSPxE9WchNsq/O00zzCcfPyPAoLDx+63+ocRRZH2aTMNRsGWwImgwmPbomTf+bn3Ios76EBOP50uPQNcMHLOj3JYm2lOjnUML2lEx6ADhLmHvl4kharg2RhLhXeIWKoXSn46oQT7Aqpz6MHvppQRbk6WdzcB66aQa9VjzCJtctyD76q/bvh9p84c2XNVwTObbDU7rx+1R3ynBoSc9WFS8fnlpIT3K363ijNF2iBRSX3/ecvrElKo+aRu0sBtSOJ1PIa1obN7h4dUpkiM1t1S4PPI8T1N3m4Rei8BanXbLI3yMT0eAqP/XuLTTwxjzzHOaKLtJ7tJaH0Obi8AcVHv7VAxF2bej2d3DqZ7Jset+Q0zY5LnPQh2ircd0WEh4irLFtfcFp5LDBCJdxC5wxO8Ym0PA9EjuaMTykEnMysaa95tJu4F5mtonMUIdE8WuxfJKQ6yavxC6/jToHr/gXuvd19zokpX6plMBYJ0zy6JdtfWujtCjnpV54VHmOoeWgd7rvTmYM6nQRn1/Q37DSP0PnvjAvh0KNdX5eDW/R2CUxOpw86dBdlleCP1Ym/A/oTbVUvKDySHCjSBVDoZ1GZaKxU246w6g59MkJiXVyIMn7uzr8sXXDEJsTFH6a9BlGZoKs8j6BptZoXitzbwUwdEkQPO8ZVhj6wF+6/24Uw33Uz7Li72Li6xDSPbmkoPheia3pYInnz8fCLHy4WHtWpdDU2LgmD9bqfMDpIchsm5z7HPaDHnw7nP6/4+0JzqFbBE53SqmZaK/qRJKiZSawZoR2tG4g3W3kTUnUS5jsRHsE0NRlFG4YoxJzvedNxcOlvwVc/Fg4QjSU+Lu2FR3Uif6FWVKM89Ch3jFZ5TaHtQCuCEAxCW8S1fDj9SfDtL8D3v+YEzID8ICY8uqUhSkJ7H1L6mIvgoMNdh7aYUIoh29JylKnXcPbfMkyPgfCoTsAjzu78fVOzaaIg9GYxsVRB1E2EV9OxeEd1u/IysS8h3Ku1BW96qkKtgxyFMHkHZ3FYtJXLaSRc3ndT8c9JvQ5l/108sM2FuE7PktQra0VwmCcLtWBlKHgtpmbhJW9v7eMpEnof7oGs1n7wZqcVlytpm+sBYMKjW5IWksmG3iazlSvwsMcs3h6KrQ2xGUzHBHNHqTT4hL9BMjkdCfZeaaLBBNbhI9sPrbSo5lGKNQ9cqRARmPb5S52YreoZJ31iEiulk3pe+Hd1gkUBJ7sfcAJn3x6YmGgvPKqT7t7ds9MJgqX4ssrlNvsXdJij+e2Ms6H8A8g8H6rPQ0ReICI3+p//EJFT/PaNIvINEfmBiFwS7f8pEdk0vBHnkNx4sc9jABNjxT8U4xSuG27oUmnwYbeDJFQFiIVlt6gu7d7qR35MmIjbaR5JOR5PCHc+eFPG3NvBOcOqOwSSlMvp58sTrInmoelxtO56rFerXiNpIwQe9mgntO670xV67DTPowilAvk46jtI5n3vSQSaN88t9D/zfNgO858D56rqycDbgav99suADwJnAb8DICIXATeo6p3DGGhT4gzakLTUEwdpG0I/9HFymgfhISWXKbxcaRAemeoA3dKp2aqvmkc74ZETAnveZfDUK+jYthJ8HrHmEfyLrfxB1cnGlXjQYIJAry20vxcP2QJX/LHzP8btoXspPIrk47TS+ELrgqCFDWBxNlSzlar+R/TnfwIhlGUemAYmgbqIVIDXARcNdoQFiKtl0kJ97jXVSSAnhHeUCaGvF70Sjn/UsEfTP6peeOz1pch76XfodGHSjzI2wRyX7eOx6NyZyV1KrlhiudIYoVj0nMhizSMuT5KMEJuCAAAgAElEQVQnpIN5N6nEG5UyCZrQqia1uWJWrXXVrrffFgmrHguPdhpi3X/veVFuoe6XqtNiVoDmEfNrwOf9738LXAB8AXgb8BvAh1S1RXlMEJErReQ6Eblu+/bt/RxrfFKIq+rCYGowhRIMQ6yq2RHBWSriAgAGoZ0Ni6NOgqMe7hLUDtmy9PazDfiVeqcLk35kmNdz8iqaESa08JysWhf1wOnEbBVMU/7zB+ERfx/VPM1jwj8nXmOIc1SCz7BoheKGfI8eR1UWWWCEZ31mzeLXKhNRxFZpIAUTR8JhLiLn44TH4wBUdSfwNP/aeuCNwLNE5P3AeuC9qvrN7HFU9Wq86Wvr1q2DcQZkq+pKj80UzQg3S0f9mofIvXfAvt3u4SvayGhcWbUOnv36/hx7SWarPmkeRRYApaiVb0l8vS/pXPPIBgyE3Il4DHnjCc9J0NBDFNzkDJTud7+vz+lHkkccdQW9FcxFNMR6vbnJtzrhohiDb2wAwmPgmoeIvEpEvut/NonIycBfARer6n05b3kr8A6cH+R64GXAOwc34jbkaR6DKKNR8SuqcTFbLcy7h++MC9uX+DAWI/6fTjW2Uj/yPOqLV/1Nz19OfYFSSiPROj5njkm4VGps7Zvr8/DPycJcNPGrC9MNk//aosLD55T0MgQ7UChJ0Jt98xZf5aoLRQ7f03z/q+0OXHio6lWqeqqqnorTfD4BvEhVf5rdV0SOBzap6leBGSCk7XZQGGkANNh1ZUDCI5itxsRhrupWTE964fI2WfWNYLYaAZ9HbYHCgiyuBoyf+OJ+3kXJmq2e+EJXnTbUSAslQLJMzrjnZOe9Lr8j+E6mVqX7ry7g84C0PULcp6VXFNI8aqkAzhI0j5AM2avujS0Yts/jrcAG4C+9JnJd5vV3AG/xv/8dcDnOsf6egY2wCA2mgUEJD6+Oj5PPo5cP20pEcGXQO31PL6nXXK+S1esbV/2t9gdf20nSVrEdW9MyEVWHHgWXvLrRaZ+nCWzY5KoDrFrnTDnB5zE1kwqxogmrSRO2PpitimiIiebRJFS3JKkvaADCY9jRVlcAV7R4/TnR79uAxw5iXB1TKqUqcUlcT+t+E0LzxqU0iergq+IuJ5ZqtpJybxWPuf3OVFq0wGdtwY29VIn8HR06yyGdsLNO8epkOqnmCQ8ROPHR8J+fcdpHiFiaXpOGEhcNaCh5B3sIg+15qG6bCxVa0Oa2aKg2anQDiLayp7kXxGF2m46DU87t/znDzTI2fcx1MIEEyxY/KQzbYR7MpIceVXQA7r+Hnw2PvdhvChVxOwnV9f9nhedjnu4E2sQUzK5tMQw/OQfNY2aV21YqFQ/gKPloq9A/vddJgs2Y2w/bbnVjz4u0grRcUfgx4TEmxKuGw46F9Yf2/5yVidY33CgRtDLTPJZOUstpibWtelZAM4TpFgwZDhrT6oPSQIkwwXVstsoJDz54Mzzrte3fHsxCidnKRyxJqbg5tRxKzM+3b+7UMS3MVvt2O1NhiBLLY2IKNm7xrXkr7ZuW9QB7mntBQ5brgOz6DbWtRh11D4YJj+5YUnmSAgJj1w5YOODMSs1WtoGkTErRa+kFRawVZCddrbf3H4TzLnW1H44/d8C38p3JH0srSt5hngQB9FLziEKasyTPuDTPhi+V4Rm/4TSOuNJwHxm2w3x5UApONB1cJFGlOl7+jkF+N8uZThcnRZy6u+51/oB772hvBk2ingpey6BlxL3fE5+HOn/FHTfBjntanzvUbFrq4qxUcomC+3Y7n+SajW5S7kQABB9JcJr3w2yV9x2Eyr8irq9NM0LWf7k8kIWaCY9eEPseBmXXL1caNZ5Rppm92uicJfk8Wvg91PsBpma9Tb+dJtuijlSz84vAdNQFMk6srfkeHw/cAzvuan/epU7YpZLz19S9s39iko7bIYfvPgjkmR7WZ2sl5JNWBgwmGKcgZkfoBUlZgIIlG3pBWGWMi9kKzGzVNUspidFGOw0d+kKNqMKaRyfXUmAymqSTxFpI7o3qpNMKWp23W7NVXFhw7cHwuGd39vyEc4fs9qL5IUVZmHfJfcGBrwq77nMO85JAvdSowQ0Ze5p7gQzB5wED7VfcFdkEL2NpdORrCO+RVPHIkyPBpFgqucmp6GTaySIpr4hi7MgH1y62VUmNYLZasuYRFVAMYz/tCZ0fA3xYcI9D8oMJ+s6b4ehHuN9rC86kWPfmNa2NlOZhZqteUIpWNYNcXVenxiPDPERbdZrgZmRYQt20JPa/hdkK0sm9XX+YTu/zi18NZz9z8aSXDZ2VFg5jd2L3X1cOcy8o8wooFiFeGEq5WJJkUUKp93gxWFtwz/cjz3EpAKVS63DkAWPCoxfEN3QvnWjtmJhiEFEV3WOaR8/oNDy0XUxF0t51yu3crmJB4rguKDxm18DZlyzWyENv9Vjzaecw76b0T3L8LkzLSRST9L4+W17/jVAJeOOR7twiTkMbEUx49ILE2TVAnwe4B35hHvY9OLhzLoVkdWvCoyvixmOF31MgBFbVLUQKVWkOGkCXU0fJa0OJOapNhnW3xQhjn8dSWyYE/9D0LDz3jUs7RjMS4RFd35rPnF93CEm17hGqSG3CoxeEB0kYrM9j/SHuht52m4tfH1XC6tKER/d0Wk8p6XLZxmw1OZ1GJLVCaV4KpKNxZXwe1ck2/rseRFt1GxGZNJ0qu+TEXpKXER5MWas3uAZqj7nIHObLjngVNkjNY+sFbsX45Y/A/t0u/HAk8atLC9Xtno41jwLRVgCTvkR520KbnSYJNqEh4ztEDrY6bayhLOV8kdBZ6tiDYO1Hg62Fedzni65XfcGNe2a1i+w651m9P28XmObRC+IbcxBdBAMicOQJLoZ+AFU0l0yrHtNGcUKIaGdvai1AwrWZmi7WHyZM4t1OoInD3Auj6mT6d7Pz5vVE7+h80FWb6KTVdB+mzdp8WsolfAfBbNWux/qQMOHRC+LaVoOeIFf50tijnO/RaVaysZhnvApOeYLro90JyWTbzmw1U9Bs1SOfR57ZKj7H3l2NgkTr3Z2zFwu8RPPoQ2WH0CNFJH2Wg6Ae0UATEx69YFjRVuDMVtOrRztkN0TKZGP9jeKsWgdPeXF+I6BWJJN0k9eT5kizaS5BS7r0PTSMS9PjVSdJQor373GlUrbfHp22yxV/bA5a6gJv45FuDK1KhHRDVvMIAnNE++CYz6MXhC5p9fpwVtfTq0ZbeAxLKzPah+rWa26fydlik3O3IbOBkkShuviS4qRCbmHOaR/xebsRWL3weRx2NFzxrj4+4wIS5b7UR7uBmgmPXvCws+CW77kHcRhJPBUzWxlNSARCjuoxP+cbJNXS/hbt6FUXvZAUGDSfZHIPRQAz/o3g81gqSdXaLgM31m1c+nvbIRmBqjUoj1bH7RgTHr3g8GPcimRYVKqjXaakWRc4o/801JHKUF/wgRbizGGd2PK7zvOIkgQl5EdF1amznfVUuys6GnfZG+lFjKZWhLqOtLZuPo/lQKVdjPywCWar0XT8LWvatjf1Zc4rk+1NXNB9sl6glIm2qlT8+TUVKHHGu3ZpwhlWOH1RLniZD1qIanDpkMzgBTHhsRyo+LIJoypAzGw1XMKknEXV+R5KZb/aLSA9guO6Z9FW/ryJ5kFkGhMXhRTKxncTdRSbwXraAbBHbDgcnvoyFzjQ8JlHN8hkBL9Fo2MaVP4RJC8c0xgMsbkmSwgF3fwQlzEtBe6hIl3/ihAyvhc54KMILMTlOqi6Td0kwcZjHnREZFEqVTfOXfc5f5Qy0lUZTHgsB8qVxvjwkSOYrUb0oV3OJOVJcl4LguKkx/oim1CgkmJvVu5Sdv6WvQ/S0N8+9oOIuOS5kJzXzSq8FAnRQTVs65SNW2D9oa5f+c5tuJI+o7vgMuGxHEiExyhrHksoJ250T6skwST/Zqp49na3+RaB0LpZSnDUw71JKi6W6Mder0WJjF0UBQx9TWA0zVYAaw6CZ78eDj4y3WbCw+gryapthIVHN13gjKUTssa3/XJx2e+kHPtE+0z05D090jxCW9VDjoTn/j+Z5lBBU614s5UfZzcVZZN2vIyu8IC09HqIuOpGYPaZEf4WjcI02ItHEIWuitoZS+fYU+DwY91ktPv+dHu97jK5wUdatWkaFeiV8AiTYwiiKJVTzSAMoVzxzmPfHrcrzSN2mI+wBhw0wOAPMuFh9JVyhZF2mFM3zWNYrNkA518Gaw5uvD8e2AZ7drrJeWKi0azTim4zvZPj+CirEEEVBFLd+zfAn0d8d0N1JVSWSlwYcZTvw/CZE21rZqjDaYUJj+VA8Hm0Lac9JDrtPmf0FolWs4GF+dSMVZlIJ9W2648eCY9wrwbNI9Z8GnJJoiz0bsxWiU9nxDXgoIH1ws/TZ0biWxSRM0SkJiKX+r9PEJHrReR7InKW31YRkX8VkdEVxcNi1KOtwoQ0yg/tcqaUIzxiTaNhAm8hPdRnP/eiymsiPILmUV5cnqMc8pd6YP8PJquR1zxKjc9ydXTLkwz9aRaRMvAu4F+izS8H3gRcCrzBb3sl8GFV3TvYEY4B5Yp78Ee2OKI5zIdKKcefEbe0DRphO+EefA/dmI8CQXhUM2arEJYb6l2Fc4p07/MIJc9H+T6U6HPT5WfuM0MXHsCrgY8D26Jt88A0MAPMi8g64CLgQ4Mf3hhQrjZGaIwaIXt5lB/a5YyUSMp9J9u8CSepK+X3a+U3q9cA7U1zotB0KoSihmKIoU+4iEsKrGtvIo+S74DR14BDxr8w0sJjqEZoEdkMPBN4AnBG9NJVOEExidNC3gq8Q7W1R1hErgSuBNiyZUs/hjyalCs+Y3dEhUdSKbUPTXSM9gSTUIPmkeNAbifc614LmOlBP4vELDMZjRGSDHPB+TiC5gGu9tNSKUWfd9Tvw/g6TJjZqhl/CrxRtbEDjareqqrnqepZwF5gE/BjEfmwiPy9iDw072CqerWqblXVrRs39rF08qgx6marXiWWGUsjcUZnNQ9oCGQoldJGTHkEU9Oqdd2PqV5r1HpKXvOoB4e5wNSMTxKs01X7WPBaDeNxH8aVGEbY5zFwzUNEXgX8uv9zLfBRcTfywcCviMiCqv5T9JZ3AG8BXgNcC/wC+D3gBYMa88gz6ppHzxLLjCWR9903mK2C8PA9zHfd52pdZQmLk1500qtloq3ifh6JpjHrzlmvuUm/m2zroHmNg+k0XA+Rka5tNXDhoapX4cxSDYjINcBnYsEhIucCd6jqz3yUVR2o4XwhRiDRPBaGPZJ8tD7aiVnLnSRMNdMfAxqFR/ALNDMOB21hphcNzzKNmYI5Kc4wD/b+2oJ7rRtHfSjEOA7CIxljl9pWnxnZJ1qcOvIW4Dl+09U4zaOCi7wyAkmY4whrHlYUcXgEs1UsFELJmKzZym/KJWgeM6t7NC7SYoelcuqwD9FVE1MkZdlFuvN5wBgJj0jzqIzsFD06wkNVL8/8rcCTo79/BJw+4GGNB3EC1LCp1xc7JcfloV2uBGd0w+0RaR5JDkQwbzUr4e41j6keRFuF81SjSK+4LAc4YSHikhknJrtbgCzM4yrzjsyU15xyJRWgI6x5mCF6OZBE04wAd9202OGqdfN5DJO4HHkgBDEcvzXd1u4eCo7sXtnhRdLukuH+WJhLM98npt356gvdax0L8+5Y49DNMhaSI9xAzZ7o5UCobTUM4pViyECOC/CBW+Saz2N4hFX97gfSa6PqJqanXxnt6IVDM4Kpq5dlZsLkODVL0gjpwF5fFNCbreq17nNLFubc/900lBoU8fc7ii1zPSY8lgN5DtFBses+2H5rKkRyh2A+j6ESiu3VFmDbbX5jXD8qUFDz6KUJMtwXB2+Gi18Fj3x82op1Ysq9XqvBdJcRXjUfTDLCoa8J5SpJZNgIa+yjOzKjOKEHwDDY9yDs2ZX2Xc6uXkMvZvN5DI9SlF2ddBbMER5JDm6zCs15AmeJPOVyWHcIrD8s3bb5eLjgcli93o1z8/Gw5SRYfRAcd2p35wumsBFurpQQhP2IFxId7dEZxQg3W0ye47rX1BZcK9GQYxLlnS0eo91qQyOp6xRtCz6PvPuj2S2zqN94Fxy8Ga589+LtU7Nugi+VXCb7M1/T/bnAma1Exkh4MPILLtM8lgN5LUTzHNe9Zm6/F1IlZ5eu5+QRBFPWiK+iljWJ5hGFXOVFwMURWXmaR0O12z7ytFfAGb/S22Me8VD32Vav7+1x+0G4LiPs7wDTPJYH2dpFWk8d1xuP6N955/an2k2tDqWotATx/2MSIrmsiUqSJ7kUmbWjxibHPOHh8zz6vSI+dIv76SUnPho2HAFrN/T2uP0gKUE/2v6Zwk+0L51+aPweVb21H4MyOiTpPub/jusD9ZMD+9x5JqZ9DaIouamuUCZNLBsHc8FyJu5nEepFZQV6uFbZ3h+BIHBG2InbFBE4pI8LqV4yf6D7jPoBUEh4iMircfWk7sGVCAE3VZ3cp3EZnZCNtgorl17Ljrn9Lrpq/x449GhYOOBMGNUJ0AXQqNSE1oFyOpYRfxBWBl5LDNVxpUMNwpI9B8P8Aff/dI8y+ftEUc3jtcAJqnpfPwdjLBERF9KoWeHRY+mx4y7Yu8vJqB13+hDKWTcJ1WqNq9UwhjBRTY32g7DsCZ0DQ7mPPEEQ17vK1Twsam4gzO13/8/2ooZY/yiqf94G7OznQIwuiaOZwoTda9Uj1B8qlVPT2NSsE1z1elRbS1ITSOgMN9OrkhbG0pD0p+ZLdWRzbxoERhOzlQmP/jM/B0jvaoj1iaKaxy3AV0Tks8CBsFFV/6QvozI6J9TDgXTC7nXvguoESfx5uQzz6pK39u6E+ahpj5D2fghdBCfNbDVUYs2jXmuiedSjPJCcY5jwGAzzPhCl28TIPlN0drkV+BIwAayOfoxRYZHwyCSD9YqSuCiQYKZatY60lLemBRqD8AhjmTbhMXzE+cfCtVsUcltA87CQ6/5z8BHLx2EOfFhVb+nrSIzuKJdJHvgk36LHZitVF3t+0CbY9gt3vrUHu3wSpdFmHmseQg8rsRpLImgU5YrzW5HToKtdhrnWTXgMgvOe65Iojz9t2CNpSdE74Rrfb/zbwNeAr6vq9/s3LKNjShnNI5gptN55VE0zgmYxs9p3ghNYc7CfhDT1eZQraS0hi7YaEXykVaniysmUSouDGGI/WbNQXRMe/WdiCk5/0rBH0ZZCd4KqPl5EJoAzgPOAz4rIKlU9qJ+DMzpgkdkKoNR7sxW4TnJBkKzZ4ATDrvvSc1Um0lpCIQN9HKqZLmeCEhqc5I+5CM7MyeJulmEe6pZZgUvDUzTP43HAOf5nHfAZ4Ot9HJfRKbHwqEd5Hr0UHuFYRz4UfvgNJzxWr4fDjoE7fpb2XChXGn0e41JTaFnjfVGhGsExj3T+qpgQ3BD3EQ8kZWZGu2SGMTiK6qBfBa4D/ifwOVWd69+QjCWRp3loziTQDcH5/Yhz4PhHOe1i1Trn8xCBndtxpUiqcCArPMagCc9yJkRalXy/+6zgAFInuZDmAsev6Ug3JzIGS1HhsQE4G3g88BoRqQPfVNXf7dvIjM4IK8JYYPRc88BPQKXG5jzHn+40j7t/7iqh7t/tstDDeGCk22muDILD3PeIaJa9HBIE63lmK0zzMBKK+jweEJFbgCOBI4DHAnYXjRLlTHFEwEXN1Hp3jmbtZKdm4akvS//+xJ+mFX3z+kYYgycUsAyJnm0DGDKaR7IIsMfecBT1edwM/AT4BvA+4KVmuhoxwuScNGTCTRK99nkUSTycWeOjsUhNXSY8RgSByen2QiDWPOb2wX13Op+IaR6Gp6jZ6nhVzRpBjVEiFEeMW8GWSqnzvBvuu8tlvc7PFQu5nZgicbrGpi5jeFSq7jr86htgzwP5+5z/fPjI7y/WYOfnYO6AC9E+5hEDGa4x+hR9ojeJyCdFZJuI3CMiHxeRMalvvEJIugl6zSNoCd3KfFWXVLZ/r+vGVkQIlKskiWbNTF3GYHnma+HZv+Wi4w4/Nn+fw49xwmVqVabIpQ/Lfvor4ORzBzNeY+Qp+lR/APg0sAnYDPyz32aMCqVyJDuCn6GUhswuFfU1q2ZWR02n2hD7X4qauoz+Up2EY09uf/1Ca9pYYzVnuZFD0ad6o6p+QFUX/M81wMY+jsvolAafR7StW59HPS6yWLDcSTwWK6Y3XpQrOQImOMstu9xIKSo87hWRF4pI2f+8ELDeHqNEbLaKa0x1SzjWxGTxUllhAjKz1fgRtMvY3BnWH2ULtzZSij7VLwOeA9wN3AVc6rd1jYicJyLfFZEfishX/baNIvINEfmBiFwS7fspEdnUi/MuO8o5mkezpj6dEMxWU7MsSfNAG3uNGKPN5Iy7frXY3OlrXllpEiOiaJ7HrcAzen1yEVkH/CXwVFW9VUQO8S9dBnwQ+CjwBeCfROQi4AZVvbPX41gWJKahOKu8R8IDXD+OokmHoXheMFuVTfMYG6Zn/fXbn25TddfeNEgjoqXwEJH/TX5bGABU9TVdnv/5wCe8cEJVt/nt88A0MAnURaQCvA64qMvzLV9ihzk9NFsFx+nkVHHHd7lCgwnNNI/xYXLWX6+c3h7muzIi2s0G1wHXA1PA6cDP/M+pQC9Slx8KrBeRr4jI9SLyYr/9b4ELcFrH24DfAD6kqnt7cM7lSfB5JC1oA91qHv79kzPFhVEQZGEsVsZ7fCiXnfZRy0ZbiS0CjAZa3g2q+kEAEbkcOF9V5/3f7wO+2KPzPwp4Ik7T+KaI/Keq/hR4mj/XeuCNwLNE5P3AeuC9qvrN7MFE5ErgSoAtW7b0YHhjRCkbHiu983mIeOHh+3YUGkvIMbFoq7FjZk1jWZs49NswPIWTBGlsO7vKb+sYEXmVd5B/F7gT+IKq7lHVe3GNpk7JvOWtwDtwfpDrcY76d+YdW1WvVtWtqrp148YVFkkchEcSWutLcHdL4vPwmkcRWRTyPGo1X8bbVqxjxap1MLcf7r+nMXLPhIcRUfRu+CPgOyJyjYhcA9xAkwm8Hap6laqeqqqnAp8EzhGRiojMAI8GfhT2FZHjgU2q+lVgBletTXFmNCOm5PMwtO6DY0rFHdytCDWOZlZ35vOQqCijCY/x4thTXBXkndtdZYFE87DraKQUmg1U9QO4if2T/uesYNLqBlX9Ec6vcSPwX8BfqeoPol3eAbzF//53wOXAfwLv6fbcy444t6LXmocIrD6oeFOn0DMiaEEmPMaLY0+Gp7/Sma/q8yShuqZ5GBFFq+p+mLR3+Y97OQBVfTfw7iavPSf6fRuuFLyRR5zcFZcE6bao7sIcILDxSHjay4sJj3Ls88DKWowj1aq7pxYWnAlLMN+V0UDRJeEHgMcB/1tEjgW+C3xNVf+sbyMzOiM4qSEVHov6e3TI/AHY+6Crkjs1Cw99VMGxeC0o1NWyHhDjR1iM7NyeRsyZ8DAiiiYJ/h+f/X0GcD7wCuDhgAmPUaHBYU7qA+lGeNRr7ufU853PoyihW11t3v9twmPsiBcjtQUTHsYiipqtvgzMAt8Evg6cESX0GaNA0s8jmK0k7Vu9VIKjNG45W2gskc9D1ZlAjPEi9qEl/c/N52GkFL0bbgTmgEcAJwOPEJHpvo3K6JwktyLUkwrRV10cM/gsKgX8HDHljBZkmsf4EWse+OCLXlQsMJYNRc1WrwcQkVXAS3E+kMNw5UOMUSBxmIckwTYmhtoC7NsNs2ubTwpJRd0OI6OzZb2rVo117FjUu6WH7YyNZUFRs9VvAufgssF/CfwNznxljArlzMNeKrWO1L33Dtj3oHvP7Nr8fYL5q1PhERzmoauhOczHjxAxF8J0DSND0WiraeBPgOtVdaGP4zGWStLPA1+MsIVFUtWF4KrCgX0thIc3O00swWxFyYV4Ip2bvYzhU4q0x6KVBYwVRVGz1bsBROQQEZmKtt/ar4EZHRI/7Ek9qRYrxnrNJ++1mBWC2araoeYxuw42HQe7tkNZ4KDDOnu/MXziKs3m6zByKGq2ugineWwCtgFH4cqIPLx/QzM6Ihua26ptrGpawqRV+ZJEeCxB83j6y92PMZ4E7RG6zxcyliVFo63+EHgM8FNVPQZXBfff+zYqo3MShzlpMcKmC8awE63nhODzMIf3ymORw9y0D6ORosJjXlXvA0oiUlLVf8P19DBGhWBmSBr3hEvbbsXYSvOo43wWJjxWHMniwzQOI5+iDvMHfJju14BrRWQbYI7zUaLsfRxJnkcZd4lyVoxxfwatL3493k+wwoYrkViT7bZSgbEsKap5XAzsBV6Pq4J7M9YSdrTImhmymkiz97T1eYiF2q5EyhXc9NDDlsbGsqLtklJEysCnVPVJuH4aXZdiN/pAUk6inv7djETzKMP+PXDPL50WctAmr8GE/SxDfMUS5wkJMN1BbTNjRdBW81DVGrBXRJokAxgjQbkCE9NpJduWobohimrCRVwtzLnquQ/uyOym6bGNlUWsyR5yNFz6hqEOxxg9is4K+4Hvi8iXgD1ho6q+pi+jMpbG9GrX+hV83keT/YKlastJcN7z4MBe+MT/cgmDDfvV3SRiBfFWJkF4TK+Cw44a7liMkaOo8Pis/zFGmdk1UQ+NCqkDPYumPRrWHATzs840tfcemNuQliOp160M94rGCw+LtjNyKJph/kER2eh/397fIRlLZmZNWpI99JvOc3QGeRLMUdVJ93ttAe75ORz5ML9f3fwdK5nQytjyfIwcWtojxPE2EbkX+DHwUxHZLiJvHczwjI6YXpVGUIXQ3dxoq8hhHgjNfiTjMDfNY+USFh4mPIwc2hmzXwecjWv+tEFV1wOPBs4Wkdf3fXRGZ0zOOCEQTFLtwvNLkeL58LO90z2qY6XqzV/GysQ3FLPClkYO7YTHi4HLVPXnYYOq3gK80L9mjBIHb3YmKMEVJ2xGXhTVaU+ADYc3ahp1NbPVSlCHMp4AABB4SURBVOaU82BqlbuvDCNDu2VlVVXvzW5U1e0iYrPKqLHpOLjiXbBnJ6xaBzd/p4nmEfpsRIKiVHaCInawa93CdFcyp5wHJ5877FEYI0q7mWFuia8Zw2Jmtfup+7pUedIjbCplLn9coj10JLRIm5WNZZYbTWgnPE4RkV052wXosMmDMVBEktbTi/FlR7LO8OAvgbRGlpUmMQwjh5bCQ1Ut1GZcCWGWucFWOdFW0Gi2CqVJTPMwDCMHSx1ezrTLDC9nhEclq3nQeSMowzBWBCY8ljNNO8AV0Tyi+leGYRgZhio8RGStiPyziHxPRH4oIi/1208Qkev99rP8toqI/KuIzAxzzGOFlFrKjlyfR9ZhbpqHYRg5DFvzeBXw36p6CnAe8F4RmQBeDrwJuBQI5TxfCXxYVfcOY6BjSdNImSbVcisTUaiu93mY8DAMI4dhB/ErsFpEBFgF7MC1v5sHpoEZYF5E1uGaT10wrIGOJRI184kJvcmzmkelmjrKQ7TVxHS/R2kYxhgybOHxF8CngTuB1cBzVbUuIlcBHwImcVrIW4F3qLZqewciciVwJcCWLVv6Oe7xQDp1mFdTc1XoImg+D8Mwchi22eoC4LvAJuBU4C9EZI2q3qqq56nqWbj2t5uAH4vIh0Xk70XkoXkHU9WrVXWrqm7duHHjwD7EyFJqF6qbkyQYnOxqZivDMJozcOEhIq8Ske+KyHdxPo9PqOMm4OfAiZm3vAP4XeA1wLXA7/kfox3NzFaBrOZRjnqAWLSVYRgtGLjwUNWrVPVUVT0VV+b9iQAicihwAnBL2FdEzgXuUNWf4fwfdaDmfzfa0cxh3ixJMHQfDMJDpLHKrmEYhmfYPo+3A9eIyPdx09YbQyFG70R/C/Acv+/VOM2jgou8MtrRVPNoEm0VeoBoZLayDHPDMHIYqvBQ1TuBpzR5TYEnR3//CDh9QENbHjTL8wjk5nlA0qYWYNKirQzDWMywHeZGPym1MVtlNY/gQFf1VXmxUF3DMHIx4bGcaRequ0jz8H/X685sJSVzmBuGkYsJj+WMlBqbOwVCDke2cGK5kr5H685hbj4PwzByMOGxnCm1CNUVFmsmJZ/noXXXghYTHoZh5DPsaCujn2SFw/wB2PdgqnlkXy9HwkPrTsCY2cowjBxMeCxnsqG6O+52/c1nVru/83wepZLzeYQ2tqZ5GIaRg5mtljOSKU8SkgYP7HNaRdbnUQo+j5r7ScqVGIZhNGLCYzmT1TwSh7gPw80KhvB63YfqWv9ywzCaYMJjOZOX5xHKj9TrOT6PcuQwr7vOgoZhGDmYz2M5I+VGs5V6P8YhR8LUqsXZ46WK93moM1tNWEVdwzDyMeGxnCmXSUqNiEC95gTGFe/K92UEs9X+B9x7zFluGEYTTHgsZ0JpkSA8ajXnx2jmBC+XvfDY6zSQ2XWDG6thGGOFCY/lzMRk5CAvOc2jurr5/qWoGdQZF8JZzxjUSA3DGDPMYb6cqU45B3m9ljrJW3UGTEJzBY44AVaZ5mEYRj4mPJYzE1NAyZmrQoOnViXWQ7SVCEyvGtgwDcMYP0x4LGcmppxAqC9405XCZIsmjEHzEGm9n2EYKx4THsuZiSlXgqS2EPXnaNFWNmSYIzBp7WcNw2iOCY/lzMSUEwg1r3l0YrZq5RsxDGPFY8JjOVP1Zqvg8wCYnG2+f8n3MDfhYRhGGyxUdzkTfB477/WlShSmWvgygtYh4gSPYRhGE0zzWM6sPgiOfoT7fdcOnC+jSE9ySVvSGoZh5GDCYzlTLsNTLodjT06zyls5zCHNRjcMw2iBCY+VQOjbIQU0j0f/ChxyVP/HZBjGWGM+j5VA3DFwoo3wePjZ7scwDKMFpnmsBOK+HZb8ZxhGDzDhsRIol1NfRtXKrBuG0T0mPFYC4s1WItajwzCMnjAQ4SEiJ4rIN0XkgIi8IfPaU0XkJyJyk4i8Kdp+rYjcKCLvjLb9rohcPIgxLyuCwxyxvuSGYfSEQTnMdwCvAS6JN4pIGbgKeDJwO/BtEfl0GJeqniwiXxeRtcAMcKaqvn1AY14+JDkbpnkYhtEbBqJ5qOo2Vf02MJ956UzgJlW9RVXngI8CF/v9pkWkBEwANeAPgLcOYrzLjsRshaucaxiG0SXD9nlsBm6L/r4d2KyqPwJuBW4APgY8BBBV/U6rg4nIlSJynYhct3379n6NefyIQ3VLljluGEb3DHsZmpfKrACq+rpkJ5F/Bl4uIv8DOAX4kqq+f9EbVa8GrgbYunWr9mXE40gpWiOY8DAMowf0TfMQkVeJyHf9z6Ymu90OHBn9fQRwZ+Y4FwPXAbPAI1T1OcCLRMQSFooSZ5iXhq1sGoaxHOjbTKKqV6nqqf7nzia7fRs4XkSOEZEJ4HnAp8OLIlIFXgu8G+cwD9pE8IUYRZBS2pvcMAyjBwzEbCUih+G0hzVAXUReB5ykqrtE5DeBfwHKwN+o6g+jt74K+KCq7hWRG92h5PvA51T1gUGMfVkQMszNZGUYRo8YiPBQ1btxJqm81z4HfK7Ja38a/a7AZX0Z4HKn5FvLWrVcwzB6hBnAVwLBbGX+DsMweoTNJiuBxGFuZivDMHqDCY+VQKJ5mNnKMIzeYMJjJRB8HeYwNwyjR5jwWAmUguZhwsMwjN5gwmMlID7ayoSHYRg9woTHSiBoHmKX2zCM3mCzyUogCI2yaR6GYfQGEx4rBcvzMAyjh9hssmIQKA27iLJhGMsFEx4rAVVXE9Ec5oZh9AgTHisGi7YyDKN3mPBYEaj5PAzD6Ck2m6wEQhcU619uGEaPMOGxIlDLMDcMo6eY8FgJqJmtDMPoLTabrBhM8zAMo3eY8FgpiOV5GIbRO0x4rARUcZqHXW7DMHqDzSYrAp8kaNFWhmH0CBMeKwHFHOaGYfQUm01WBOoq65rPwzCMHmHCYyVQnXSaR6U67JEYhrFMsKXoSuCRj4f774GHnDrskRiGsUww4bESqE7AE18w7FEYhrGMMLOVYRiG0TEDER4icqKIfFNEDojIG6LtR4rIv4nIj0TkhyLy2ui1d4nIjSLyoWjbi+J9DMMwjOEwKLPVDuA1wCWZ7QvAb6vqDSKyGrheRL4E3AE8VlVPFpFrReSRwE3A5cBTBzRmwzAMowkD0TxUdZuqfhuYz2y/S1Vv8L8/CPwI2AzUgQkREWDav+93gD9X1YZjGIZhGINnZHweInI0cBrwLS9IPg58B/g5sBM4Q1U/NbQBGoZhGAkjEW0lIqtwwuJ1qroLQFX/GPhj//pfAW8VkSuApwA3quof5hznSuBKgC1btgxo9IZhGCuPvmkeIvIqEfmu/9nUYr8qTnBcq6qfyHn9NP/rT4EXq+pzgEeIyPHZfVX1alXdqqpbN27c2KNPYhiGYWTpm+ahqlcBV7Xax/s0/hr4kar+SZPd3o7TJqpAaEhRB2Z6NFTDMAyjQ0RV2+/V7UlEDgOuA9bgJv7dwEnAycDXge/77QBvVtXP+fddApyiqr/v/34PcAHObNUy601EHgR+Em1ai/Od9IJeHmsLcGsPjrPcPx/0Zlyj+Nns2g3nWKP4+YZ9/Q4GZlW1velGVZflD3Bd5u+re3jsXh5r+wiOaeQ+X6/GNYqfza7dcI41ip9v2NcvO2+2+hmZaKsB8M8jeqwHenSc5f75oDfjGsXPZtduOMcaxc83qtdvEQMxWw0DEblOVbcOexztGJdxLpXl/PmW82cD+3zjzlI+XyfvWc6ax9XDHkBBxmWcS2U5f77l/NnAPt+4s5TPV/g9y1bzMAzDMPrHctY8DMMwjD5hwsMwDMPoGBMefUZEjhCRT4nIz0TkZhH5MxGZaLH/60Rk7BIgRWT3sMfQD0TkmSKiInLisMfST9pdPxH5ioiMnXN5JTx/w3r2THj0EZ9B/wngn1T1eOChwCrgHS3e9jose36UuAz4BvC8Tt4kIuX2exn9xJ6//mLCo788Adivqh8AUNUa8HrgZSIyKyLvEZHv+6ZXrxaR1wCbgH8TkX8b4riXhIisEpEvi8gN/nNd7Lcf7Rt+vd83/fqiiEwPe7zt8AU7zwZ+DS88ROQ8EfmaiHxSRP5bRN4nIiX/2m4R+QMR+RZw1vBGvjT8Z/tM9PdfiMjlQxxSt6yY528Yz54Jj/7ycOD6eIO6qsG3AlcAxwCnqerJuMKQfw7cCZyvqucPerA9YD/wTFU9HTgfeK9f/QEcD1ylqg/HJS89e0hj7IRLgC+o6k+BHSJyut9+JvDbwCOB44Bn+e2zwA9U9dGq+o2Bj9bIspKev4E/eyY8+osAebHQAjweeJ+qLgCo6o5BDqxPCPBOEbkR+FdcY69D/Ws/V9Xv+t+vB44e/PA65jLgo/73j/q/Af5LVW/xK9m/Ax7nt9dwFaKN0WAlPX8Df/ZGop/HMuaHZKS8iKwBjgRuIf/GHmdeAGwEHqWq8yLyC2DKv3Yg2q+G6xA5sojIBpzZ4xEioriKzgp8jsXXLfy93wuUcWWBxgXlVLMdx4SV9PwN/NkzzaO/fBmYEZEXQ+JEfS9wDfBF4BUiUvGvHeTf8yCwevBD7QlrgW3+5j0fOGrYA+qCS4EPqepRqnq0qh6J62r5OOBMETnG+zqei3OoLwd+CZwkIpMishZ44rAH1CUr6fkb+LNnwqOPqEvffybwqyLyM1xDq/3Am4G/wtlebxSR7wHP92+7Gvj8ODns/AN4ALgW2Coi1+FWQj8e6sC64zLgk5ltH8ddp28CfwT8ACdQsvuNFeH6qeptwMeAG3HX8jtDHViXrITnb5jPnpUnMbpGRE4B3q+qZw57LP1GRM4D3qCqTx/2WHrFSrp+y41hXjvTPIyuEJFX4JzGbxn2WIzOses3vgz72pnmYRiGYXSMaR5GR4jIkSLybz7x6Ici8lq//SAR+ZIvA/ElEVnvt58oIt8UkQMi8oboOCeIyHejn10i8rphfS7DGHV69ez5117vj/EDEfk7Eek4ss40D6MjRORw4HBVvUFEVuPixi8BLgd2qOoficibgPWq+kYROQQX+XEJcL+qvifnmGXgDuDRqvrLQX0WwxgnevXsichmXITgSaq6T0Q+BnxOVa/pZDymeRgdoap3qeoN/vcHgR/hEpIuBj7od/sg7oZFVbep6reB+RaHfSJwswkOw2hOj5+9CjDto7VmcJn1HWHCw1gyInI0cBrwLeBQVb0L3E0OHNLBoZ6Hc/wZhlGAbp49Vb0DeA8uVPkuYKeqfrHTMZjwMJaEuKKBHwde5+sFLfU4E8AzgH/o1dgMYznT7bPnfSIX42p7bQJmReSFnR7HhIfRMSJSxd2816rqJ/zme7xNNthmtxU83IXADap6T+9HahjLix49e0/C1bvarqrzuLL1j+10LCY8jI7wlTr/GviRqv5J9NKngZf4318CfKrgIS/DTFaG0ZYePnu3Ao8RkRl/zCfi/CedjceirYxOEJHHAV8Hvg/U/eY342yvHwO24G7OX1XVHSJyGHAdsMbvvxsX5bFLXMe224BjVXXnYD+JYYwXPX72fh9Xl20BV4bmClWNCyi2H48JD8MwDKNTzGxlGIZhdIwJD8MwDKNjTHgYhmEYHWPCwzAMw+gYEx6GYRhGx5jwMAzDMDrGhIdhGIbRMSY8DMMwjI75v3Wr/IFDJQK5AAAAAElFTkSuQmCC\n", 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\n", 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" + ], + "text/plain": [ + "Worst drawdown periods Net drawdown in % Peak date Valley date Recovery date \\\n", + "0 11.61 2016-10-24 2016-11-14 2017-01-19 \n", + "1 7.42 2017-06-08 2017-07-03 2017-07-18 \n", + "2 5.04 2017-11-28 2017-12-04 NaT \n", + "3 4.64 2017-07-24 2017-09-25 2017-10-05 \n", + "4 3.20 2016-09-07 2016-09-09 2016-09-15 \n", + "\n", + "Worst drawdown periods Duration \n", + "0 64 \n", + "1 29 \n", + "2 NaN \n", + "3 54 \n", + "4 7 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pf.show_worst_drawdown_periods(df)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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T51167rnnGDNmDL169aK4uJhZs2Yxbtw4Jk2axJFHHsm2bdt44403uOeee5gwYQKtWrXi448/pqysjKFDhzJq1CjA5oKaM2cOPXr0AOCBBx6guLiYLVu2MGjQIE444QTKysq44YYbmDVrFkVFRRx66KHst99+APz2t7/lkksu4eCDD+b7779n9OjRfPnll5E/uyjkWlgY4DURMcB9xpjxvuOdgMXO/hKvLKw8ARE5B6uR0LVr1yw2W6nRpDJDqbBQ8sTEiRO5+OKLARg3bhwTJ07khhtu4KKLLqKsrIxXXnmFYcOG0bRpU1577TVmz57NU089BdjEgvPnz6dRo0YccMABOwUFwB133MGzzz4LwOLFi5k/fz4rVqxg+PDhO1OZn3jiicybZzMWTJkyhblz5+48f8OGDWzcuJGioqKsPWuuhcVQY8wyEdkFeF1EvjLGTHOOB60gbpKUJxZY4TMebG6obDRYqeFUlMdDYxs0gIaFdrtl23idaU/CuhVwwFHQvU/+26jUC9asWcObb77JnDlzEBG2b9+OiHDzzTczYsQIXn31VSZNmsRJJ50EgDGGO++8k9GjRydcZ+rUqQkpyqdOncqUKVP44IMPaNasGSNGjGDr1q0ky+O3Y8cOPvjgA5o2bZqbhyXHwsIYs8z7v1JEnsX6H1xhsQTo4ux3BpZ55SN85VNz2VallpAQCeWFzQLsfSC8MgG2brL7n78DX02HC+6Eth3y304l7/xyeK+UZqNkXHx030qmqWQ89dRTnHrqqTt9DwDDhw/n3XffZdy4cdx///3MmDFjZzry0aNHc88993DooYdSWFjIvHnzdqYsdykpKaFNmzY0a9aMr776ig8//BCwCyJdcsklrFu3jqKiIp5++umdPo5Ro0Zx1113cdlllwE2XXq/fv0y/SgCyZmDW0Sai0hRbBsYBczxVZsMnOpFRR0IlBhjlgOvAqNEpI3n2B7llSn1na3OmsONnVFUsyIYdXpi3fIyuP0cmD0NRck2EydO5Pjjj08oO+GEE3j88ccZNWoU06ZNY+TIkTRq1AiAX/3qV/Tu3Zv+/fuzzz77cO655+5cGMllzJgxVFRU0LdvX66++moOPPBAADp16sRVV13F4MGDGTlyJL1796ZVK2t+veOOO5gxYwZ9+/ald+/e3HvvvVl/3pylKBeR3YFnvd2GwOPGmJtE5DwAY8y9YpeDugvrvN4MnGGMmeGdfyZwlXf+TcaYB5PdT1OU1xO+mg6P3WC3u+8DZ/0l8fiSeXDf7xLLChvDNU/lp31K3qirKcqTUVpaSosWLaioqOD444/nzDPPrCSwklGVFOU5M0MZY74F9gsov9fZNsAFIec/ADyQq/YptRQ3q+xuPSof79wLDjwGPnRWTisvy3mzFCUfXHvttUyZMoWtW7cyatQojjvuuLzdu7pDZxUlPVxhsWu34DrtOyfuFzbKWXMUJZ/ccsst1XZvTfeh1C5WLIpv79o9uE7rXRL3gzQQRVHSQoWFUnvYvh3WLIvvh2kWnX+UuF9QmLs2KdVKXVkWOh9U9bNSYaHUDrZsgqdvhR1e7pyiNtCoSXDdZkUw7MT4/o6am29HyZzCwkJKS0tVYETAGENpaSmFhZkPnNRnodQOpr8InzshsK13TV5/r8F2ch6osKijFBcXs3btWjZu3FjdTakVFBYW7pz9nQkqLJTawZRHE/eLd0tev0FBfFuFRZ2koKCA9u3bp66oZAU1Qym5YcZrMP738MX7ubl+Ks1ChYWiZBUVFkpyjIEfvoMdO6Kfs20r/O9OWPw1PPGX1PWj4CYKBOuzSEaBIyxq8BoBilJbUDOUkpynb4PP3oKe/eHU66Kds3Fd4v62reHO6Khs962r7Q+P9aOahaJkFdUslOR89pb9P38WbI7oSNzkW3xo49qqtaF8G2x10pJ32Qt6Dkh+jisstlfOv6MoSnqosFDC8YckRu10/etJbKiisHBXvmtWBGffbNOTJ6PAUZpNGiY0RVECUWGhhOM330Q15/iXNS1dF1wvKu75rXeNpyVPhjivtpqhFKXKqM9CCcevSVSUB9fz4xcWVTFDzZ4GT/49vp/KsR2jQM1QipJNVLNQwvELh4pt0c7zaxKZahbl22xUlUuLiMKigTMOSieSS1GUQFRYKOFkLCx8msU7T8Ocd9O//4Y1NpIqRmEj6HdotHNVs1CUrKJmKCUcf7hq1E53c0nlskl/g0ZNoVeKKCYXN6qqoCFc9gg0bR5e30V9FoqSVVSzUMLxC4fyDDWLGLEw3Ki4vo6eA6ILCkiMhlJhoShVRoWFEk6mZqjNG+Lb7nyIeTPSMwm5IbdFaSZAcyOmjKkcBqwoSlqosFDC8ZuhokRDGQNbSuP7J/8xPtt66yZYsTD6/V0zVNQoqBgiOotbUbKICgslHL9w8AuPILZuio/imzSz5iB3mVP/hL1kuGaoqFFQLpofSlGyhgoLJRy/sIjis3BTgjQtsv+btAg+ngo3x1QmwkJTfihK1lBhoYTj72CjaBZbAoRFU0dYuCaqVLiaRcsMFm1xhYWm/FCUKqHCQgknE5+Fqzk0CxAWWzdFv7/rKG/WKrxeGOqzUJSsocJCCSeTaKggzcI1Q21JwwzlaiExwZMOaoZSlKyhwkIJp6o+i1gH73b0Uc1Q2yugvMxui2S2HkaBpvxQlGyhwkIJp5LPIsLoPFs+C9dc1bRFtEyzfpKZobZutku++hdqUhQlkJyn+xCRAmAGsNQYc7TvWGPgEWAAsAYYa4xZ5B27EjgL2A5cZIx5NddtVXxU8lmk0CyMgc+nxfebVcEM5QqLJmnM3HZx17zwC7rHb4SFn9vt4t1gt93huIvSmyWuKPWIfGgWvwW+DDl2FrDOGLMncBvwNwAR6Q2MA/oAY4C7PaGj5JN0fRZffwyrl8b3s6VZZCoswlJ+bN4YFxQAa1fA3PdhxiuVr/HJGzav1dL5mbVBUeoIORUWItIZOAq4P6TKscDD3vZTwGEiIl75E8aYMmPMQmABcEAu26oEkG401FcfJe7HJuMlRENlIixahNdLRpgZKmwW+fJvEve/+RSeud1mzL33UnhxfGIWXEWpR+Ras7gduBwI8y52AhYDGGMqgBKgrVvuscQrU/JJJc0ihbBY+X18e//DoFNPu52uZlG+Df53V3w/YzNUiLBY5giFwsbx7VXuKwdMvjtx/8Pn4dM3M2uLotRyciYsRORoYKUxZmayagFlJkm5/x7niMgMEZmxatWqDFuqhFJppbwkZihjYJUjLEaeGt8ubBw3CVWUw7ay5Pf9ZAqs+yG+n21hseLb+Pbwn8e3Vy2JpwXZsQPWrah8zalPZNYWRanl5FKzGAr8REQWAU8Ah4rIf3x1lgBdAESkIdAKWOuWe3QGlvlvYIwZb4wZaIwZ2L59++w/QX0nqmbxzafw6HU2wghs5+4m/hOBFq3j+5tCUpjHeGVC4n7TDM1QYbmhFn8V3+7RN57RdntFXEBs2RicqbZtx8zaoii1nJwJC2PMlcaYzsaY7lhn9ZvGmFN81SYDp3nbP/PqGK98nIg0FpEeQE9geq7aqoRQafGjAGHx7rPw0NUw31Egd+laOdTVze0UC1ddtxKmv5yYihwq+ygyFRZBmsX6VdahDVbj6bSnbW+M5Z7WEbYmR3GHzNqiKLWcvM+zEJHrReQn3u4EoK2ILAAuBa4AMMZ8AfwXmAu8AlxgjNF8Dfkm1aS8jevgTb+yCPQZWrnM1SxK19lR+yPXwPN3w5M3J9b1CwfJ8DUNEhYLZ8fLuu5tzWNd9oqXffOp/R+WHbdR08zaoii1nLwsq2qMmQpM9bavccq3AieGnHMTcFMemqeEkSqR4IxX4gKkqI31U7TZDbr3qXwtV7MoXW8FTSzMdtEXVnjEtBG/EzydfFIuQcJi0Rfxsh772v89+8d9Ed98Ytvi5qVyiZJMUVHqILoGtxKOv2P0axZrHDfSoSdD/5Hh10oQFusCnOflUNjI+hZKfbOq9xsRuckJBAmLZQviZV33tv879YTGTaFsizVTbVgTrlnouhhKPUXTfSjh+M1Q639IHPW7eaBSrTdR5BMW27YkHo/tb1qf6FgedyXs2i16m138wqJ8G6z8Ll7WYQ/7v6Bhoi+idF2isHCjsTQhoVJPUWGhhBPks/jkjfj+1jSywvod3P7JbWWesHDXsOiwO/Q5KHp7/bjRUB++YCfjxRIKtutkV/KL0dRp/+aNicLCXelPU50r9RQVFkowi79OjHCK8a3jIHa1jFSzrF3NYtP6yppFmRd2u96ZL1OUwYJHLq5msWgOfPC/+H7HPRPrusLuw+dh+ktOO9rGt1WzUOopKiyUyiyZB+N/H3zMHfknLKGaQli4mkXJ6rgmESO276biaNeZKtHAl07s83fi27HZ5TGatYxvz5uReMwVWqpZKPUUFRZKZb7+OPxYTFgYk2iGSiUsWrWLZ4ENciDHNA03P1PHPaK1Nwy/sHCppFm0DK7XpDl0dUJrVbNQ6ikqLJTKuDmSmhXB6TfG90vXWbt/2Za4/b9RE2hYmPyaBQ2h9S7x/R8WJR5fuwKmTkoUVB2qKixCXm+RyoIoyOeyW3f49R2JgkQ1C6WeoqGzSmXchICnXmdNNs2KrNnJGKsVuM7vqDOsizvEZ08v/zbx2Iv3Je4XNrJO6KqwKWSuRLvOlVfeaxogLEaeCm12ScwRpZqFUk9RYaEksr0icf5EzG/Qok3cR/H6w9DYjSRKQ1jwid0OSxMeY68DwzWDqPjna4DVKob8pHJ5kBmqlZdvzF0XQ4WFUk9RYaEksmZ53NTSur2drAbWyRvTONzwWQgelQfhJuFLti7ECZdC7yHRrpkMf36n3/wLmreyf36aRxQWaoZS6ikqLJREXMdzy3bx7WSdZFRh0Wa31HU67A79Dol2vVT4NQs3YaAf/zM0bhqfh6GahaKkdnCLSCsRuS22boSI/ENEAoZmSp1gh9MZuk7rzj8KPyfqehNR0nvvkuFs7SAOc5IcjxiXvK6b6BCsVhHLVeVGVamwUOopUYzCDwAbgJ97fxuAB3PZKKUacTvDAkdY7DvMjrT9jmGw5qootNm1cupyP8lG/+kycDQMPd7+DQvMVxmnURMYfFR8f49+8W01QylKJDPUHsaYE5z960Tk01w1SKlmEoSF83p06AG/e9A6nf97c2KIaxTzEtgIp5Zt7aS8MLIpLBo1gTFnRq9/9HkwYDSUrIKeA+LlqlkoSiTNYouIHBzbEZGhwJYk9ZXaTJiwgLhm0bJtYnlxRGEBqRcPyjRpYLbo0AP2OiAxr5RqFooSSbM4H3jY81MIdtnT03PZKKUacedP+IVFjCKfsGi9a/TrF3eAhZ+HH3cn7tUUVLNQlNTCwhjzKbCfiLT09kNmOil1gmSaRYzCxon7RSnSk7t06w0zXws/nsqnUR1oNJSihAsLETnFGPMfEbnUVw6AMebWHLdNqQ62h0RDufijn9Lp4PuOsCvfrVhoO+GFn8dXzPvRoLSamjfUDKUoSTWLWI8QFERvAsqUuoArLBqEvB77/hhef8jO6B4+Nr3rFxQkzqDeVALjL7Mpyg89Oe3m5gU1QylKuLAwxsSS9UwxxrznHvOc3EpdJIoZqnFTuOheWLMUuuwVXCcqzVvBxffZpIQFSbLEVieqWShKpGioOyOWKXWB7REc3GDTY3TdOzs+BpGaKyjAhgvHntOYeLZdRalHJPNZDAEOAtr7/BYtgRr8y1aqRBTNoj7SoCD+2WyvgAaNqrc9ipJnkvUGjYAWXh3Xb7EB+FkuG6VUI1Ec3PWRgoaJwqJQhYVSv0jms3gbeFtEHjLGfJfHNinViWoWwbhObvVbKPWQKL3BZhH5O9AH2JkYyBhzaM5apVQfKiyC0bkWSj0nioP7MeAroAdwHbAISLJIs1Kriergrm9oRJRSz4kiLNoaYyYA5caYt40xZwIH5rhdSnWx3ekIC9RnsROda6HUc6IIi9hQc7mIHCUi+wOdU50kIk1EZLqIfCYiX4jIdQF1GovIJBFZICIfiUh359iVXvnXIjI64vMoVUXNUMGoZqHUc6L0Bjd6SQR/h51f0RK4JMJ5ZcChxphSESkE3hWRl40xHzp1zgLWGWP2FJF8LTLEAAAgAElEQVRxwN+AsSLSGxiH9ZN0BKaISC9jjP5Kc42aoYJRzUKp5yTVLESkAOhpjCkxxswxxhxijBlgjJmc6sLGUurtFnp//jQhxwIPe9tPAYeJTT51LPCEMabMGLMQWAAcEP2xlIxRzSIY1SyUek5SYeGN5H+SrE4yRKTAWyhpJfC6MeYjX5VOwGLvXhVACdDWLfdY4pUpuUaFRTDuZ+GmcVeUekIUn8X7InKXiPxYRPrH/qJc3Biz3RjTD+vjOEBE9vFVCcoVYZKUJ54sck5sbfBVq1ZFaZKSCp2UF4xqFko9J8rQ8SDv//VOmQEiz7MwxqwXkanAGGCOc2gJ0AVYIiINgVbYxZVi5TE6A8sCrjseGA8wcOBAzYSbDaJkna2PqM9CqedEWfzokEwuLCLtseG260WkKTAS68B2mQycBnyATSHypjHGiMhk4HERuRXr4O4JTM+kHUqaqIM7GNUslHpOFDNUpnQA3hKR2dhJfK8bY14QketFJOYHmQC0FZEFwKXAFQDGmC+A/wJzgVeACzQSKk+ozyIYV7N4+BqYN6P62qIo1UDOegNjzGxg/4Dya5ztrcCJIeffBNyUq/YpIaiwCMa/lOzz98DvJlRPWxSlGsilZqHURtTBHUy/QxPX7ihZZde2UJR6QkphISLNRORqEfm3t99TRI7OfdOUaqFCfRaB7D0Yrngsvm+MhtDWVYyBNct1MOAjimbxIHY29hBvfwlwY85apFQvaoYKp1kRNGke3y8vq762KLnjuTvh9nPgjvNh5eLU9esJUYTFHsaYm/FyRBljthA8D0KpC6iwSE6jJvHtbVurrx1KbjAGPnvLbq9eCs/cVr3tqUFEERbbvNBXAyAie2A1DaUuskPnWSRFhUXdZktp4oBp6XyY8h8o31Z9baohRBEWf8KGr3YRkceAN4DLc9oqpfpQB3dy3KiochUWdY7S9ZXL3p4Es9/Of1tqGFEm5b0uIrOwa1gI8FtjzOqct0zJH9vKwOywo+aEGdwF4efUVxo3jW9vUwW7zrGpJLh82pMw4PD8tqWGEdXOMBw4GGuKKgSezVmLlPxhDDx9G8yeareLd4MdO+wxEWigkdWVKHTMUDVBs9iwBr78EBo1hT5DoVHj1Oco4WwK0CwAWrTObztqICmFhYjcDewJTPSKzhWRkcaYC3LaMiX3rPw+7swDWLsivl3QMHFegWJxzVDV7bPYsQMe+iOsWmL3v58Lx15YvW2q7QSZoSDRV1VPiaJZDAf2McbEHNwPA5/ntFVKflj+bfixho3y147ahNtpVHfobMnquKAAK/iP+JV2bFUhzAy1eUN+21EDiWJn+Bro6ux3AWbnpjlKXkkmLJq2yF87ahM1KRpq7fLE/fJtcM/F8OJ4WPZN9bSptuOaodw5NWFCpB4RRVi0Bb4UkalemvG5QHsRmexlh1VqKyuSCYui/LWjNlGTzFDrVlQuW70UPnweHr8x7n9SUlNRDk/dCh+/Ei874lfx7c0b6v2M7ihmqGtSV1FqJSsWhR9TzSKYGqVZBAiLGCWrYctGaN4qf+2pzXz+TqL/DqBtRxs+XlFutbbysnpt4ksqLLw1uK82xozMU3uUfLFjR9wOK2JHzG7n10w1i0Bqks/C1SyOuwiK2sCj18XLNquwiMwqX1qPLj+CLnvZz6/EmymwaUO9FhZR1uDeLCL6xtU1tm6KbzduBq3aJx5XM1QwNUmzWOMsHtmuE/QaaDu5GFs25r9NtZX1P8S3Dz0Zzv67DR1v1jJe/sxtiQEF9YwoZqitwOci8jqws4cxxlyUs1YpuWdLaXy7SXNo3T5xdKXCIhjXZ7Gm0kq/+eGL9+GD/8UDFESgXWe73cQxH7rfsZKc9Svj2933iYeNu8Ji0RyY9Fe48K78tq2GEEVYvOj9KXWJrU5H0rQFFHdIPK5mqGBczeLbz2D+LOjZPz/3Ll1vR7ZP/CWxvM9QaO51au73tlk1i8i4wqL1LvHtPfvDN5/G93/4zn4P9XCSXpR0Hw/noyFKnnHNUE1bQPsuicfVwR1Moc9m/dnUYGGxcA58Pd2mUem6t+3Qq8L0l+GFeypH5BQ0hGHOYpOuRqhmqGiUb4ON6+x2gwbQsm382EHHwm7d7VK6MVYshD0rLQJa54kyg7sn8BegN7Dzl2KM2T2H7VJyTYIZqkXcjBFDzVDBuLmhAL77onKdktXw8NXxPFvv/w/OuAl275v5fT99o7Kg2G8EDD0eOjg/xaZqhkqbklXx7ZbtoMDJidaggRUMg46Aj1+2Zcu/rZfCIuriR/cAFcAhwCPAo7lslJIHXM2iSXNo7xcWqlkE0nHPxH3/2twACz5JTMgIsLCKSQ/W+Cbgtd4FTrg0UVCAahaZ4AoL1wTl4n7OySaz1mGiCIumxpg3ADHGfGeMuRY4NLfNUnLOFp/Poqg48bimJw+msBH87oH4/gYnAfO2MnjnaXjujsrnra5iFI072t21G5z4++DcXa6QV59FNNY5/gr/7yCGKyyWfJ3b9tRQIkVDiUgDYL6IXAgsBULEr1Jr8EdDicD+h8Enb9jR1W5qZQylVTsrNMq3QdkW2LrZZn/992WJGpvL6qVVu+fWzfHtc24Jj/d3Hdxb1QyVkukvwfP3xPddf4VLh92tFlleBut+sKbGVu3y08YaQhTN4mKgGXARMAD4JXBaLhul5AG/gxvgmF/D6TfA+bcnjmSVRESgyOlUNqyBN/4TLigA1izNPF3E9u3xCYCxCZRhNNVoqLRwBQWEaxYFDe0kvRiL5uSuTTWUKNFQH3ubpcAZuW1O5qzZuJXRN0SL8D1i/y5cfHSis/H2F2bz8ifRFmc/ZVhPfjm8V0LZNU98zEfzV4ackchvj9qXI/t3TSi74N/vsGBFtMyW140dyIG9dk0oO+m2KawtjTaj+K5fHUzPrYmaReJnl3wU/PjFh9G2KD6yXbNxK7+4/Y1I9wZ49eqjEvbnLy/hwvvfjXRucYvGTLwkMaHAh/N+4E+TZkQ6f8/dWvKvs3+cUPbSrO/554vRfAqDe+7C9eMG2VGll8jv0fcW8p8FfaFRCgf2jS9l/u41Og+AU/iMX/rMT5XePa8uqwDfb6JGvHsdEuf4Rv3dQg7ePWnHhYU/ixe8CbwZ3J7iRoOZyGd2Z8k82G9E9bx7Do++PY//TJsf6fywdy8qUaKhegGXAd3c+sYY9VvUZippFklGxUplWjomiGQaRbZRX1L2SDfRojjadj3MQhvFZ/EkcC/wb2B7bpuj5A3XRNGkBfBDaFUlgLYd49ubSoA8hRoXqLDIGukKeVejq4f+IDEp7KgiMtMYMyBP7cmYgQMHmhkzoqmDCvCPs+KzVi8eD207JK+vJPLlh/D4TXa7697w/ZeJxy++zwqUx26Erz6yZb/4P9j7wPTv9d1cuP8PdrvLXnDO35PX/+ZTeOjq+P7pN8Ae/dK/b11nzXK4/ZzEsqufDA8e+P4rG8QA0KknnHdrbtuXJ7w+fmCqeqEObhEpFpFi4HkR+bWIdIiVeeWpGtBFRN4SkS9F5AsR+W1AHRGRO0RkgYjMFpH+zrHTRGS+96cO9WxiTOIiL/UwdUGV2bV7fNsvKM67La55uM7oTLPUljmRUO6CPGHsvh/0dMZ3X38cXrc+49cOmhUlzyrrRprVwwmPycxQMwEDxHSvy5xjBkgVW1kB/M4YM0tEioCZIvK6MWauU+cIoKf3Nxg7+W+wJ4z+BAz07jVTRCYbY9ZFfC4lGeVlNuwTbAhoPU67nDFtdrWfmz/zbPd9oJMzcS8biyW5YbP+GeRBiMCAUTB/pt1ftgBWLoa3Hrdp6ffsD/0OtSnN6zN+M9TPL09e353DUg/NUKHCwhjToyoXNsYsB5Z72xtF5EugE3alvRjHAo9463t/KCKtRaQDMAJ43RizFsDLeDsGmFiVNike7qL0zVsFT+5SkiNiZ3P7Qyi79Uncr8r6F1s22WSFsTQTYNPJR8HN9fXdXLjz1/H9b2fDe8/CpffX74GCqx30HpLaVOdqdZs32jkaBxyZm7bVQEKFhYgMAhYbY1Z4+6cCJwDfAdfGOvIoiEh3YH/gI9+hToAbM7jEKwsrV7KBG8nRXE1QGTP6DHj1QWvGa70LtNnNjthdMjVDGQMPXlU5tURUYZHKB7WpBJYugB772P2l8+1qcf0OtYnz6gMJKW8ipLcpaJioTT5/jxUwbrBDPtheYQcpu/XI6+JWycxQ9wEjAURkGPBX4DdAP2A88LPwU+OISAvgaeBiY4w/mDtoSGuSlPuvfQ5wDkDXrl0rnVBvqCiHxV/bWaZNInQmfs1CyYzOveCsvySvk6kZaun84BxEUTWBgoCfdtuOiWtwxDrLJfOsA317BXzzCVxwZ/R21jSmPApfvAejzoC9Byev6095E4WmLRK/x3kzYUgehMXK723gQqOmdgLoxrV2gHLx+LxNoE02g7vA0R7GAuONMU8bY64G9kxy3k5EpBArKB4zxjwTUGUJ4ObG7gwsS1KegDFmvDFmoDFmYPv27f2H6w/P3w0PXAn3/NbO9k1FgmahwiKnZKJZLF0A9/0u+Jg/QWEy3LToo8+0EVr7DouXlW22Gsyz/4xfd8WiuD+rtrHuB3j7vza1yuM3pq7vT3kTBf/n3yBKEowqsq3MRmG99G+bd2yj1y2vX5nXBbiSCgsRiQ1PDsPObYwRZTKfABOAL40xYTFmk4FTvaioA4ESz9fxKjBKRNqISBtglFem+NlWBrOm2O21K6IlrEuIhKrnTs5ck4nP4j3fuKqdY4FNZ6Gl4T+3fpX9RsCQY2yZ2ylu3WSz4a78PvG8DWui36MmsdEX/1JRnrx+UMqbVLhaOcQ77lyydnlikINLHh3tyTr9icDbIrIa2AK8AyAiewJRpi8OxeaR+lxEYktNXQV0BTDG3Au8BBwJLAA246UTMcasFZEbgFjM3/Xp+EjqBUvmWeenP6IjiqlDzVD5I8EMFVFYrF2RuH/2361ppVmRjbaKSofd4fzbEstcYVG2GWa+Vvm8DWtq57wb/6h/7QrYpUtwXai8WmQmZFtYLJwDb0+y66kP+YkNpNicJBXL4zfZuTejz0gcVOSAZNFQN4nIG0AH4DUTn73XAOu7SIox5l2CfQ9uHQNcEHLsAeCBoGP1ns0b4cH/CxYM27akPt8dOYYlTlOyQ6MMzFDu9/OLP1ohMWhMdtrj+rS2lNq1Nyrd30u7box1eq9ZBgceXfPXOPG/+2uWJRcWribirrWdjE49rT8pRra1sFfuh2XfWP/Ey/dbB/ru+8WP9z7Ihrt/NtXubyqxkz4XzoZz/pH8eatIUnOSMebDgLJ5OWtNbWbJPPjweasuttnVmgCCJrsZY1/SllXopBd8Eq5BRElhkLDecD329eQDdxnW8gha3/btUOp0Yr2ynDzB1Szeeza4TqwDnDoJ3nzMbpdthjFnZrct2abMZ6pZkyItfJRFj/wccz7ce2l8P9uaxbJvEve/+TRxDfDmLYNTvpRtgef+abXQHIXCR8kNpUThmdtglc9fcJQvlYAxNurk+y/tynS9DwJpAHsNTpzIlYp5SdKahNk2XdwfSSsVFjklXQf3ppJ4KvPmrYKjmqpClNDbdSvsgkAxQQE2SqqmU+bTLJKtaLdjR6JW0DLi2hSdesLvH4RbvATcP3xn1+c++rzshNA2KIAdSYJUmrW0dYI48fKczpnKgyu/HmBM5cVtViysXG/JvHhqiFVLbOTG1Cdgwh8qOxnDWP4tfP52fP/cf8CBx8T3/aMrP9vK4tFQDQrUDJVrXDPUoi8qd2h+NubYRBgW9dP/8Pj2x6/ArWf52lULkif4P9s579jOPIjSdXEfR7OWid9TKlq2TRTiCz6BJ2/JfL2SGMaAcTLh/mhQ5TrNWiauWRLjuIugTW7XpFNhkQ0qyiu/KEG2THcJTpfybZUXYQnj9UfiqZXbdrTRLu5oMZWwcNvQsm1+Qv/qM/6Fiv53V/L6GxyzRtiqbVUhSLM4/3YYfFTlcpdNJTU/pNb/7u/YYQVfEOtdE1Sa2rUIDPT5kJbOh7kfpHcdP+Xb4v1IYaPg2eHNWgb7jtIJfMgQ7SmyQZD/YOPaygLEH+XismgO3HYOjP+9TfMQxrIF8e2fXGA7e384ZDIS/BW6Om7OKfRNovt8WvL6+dYsfnwCdNzDRk4N+YnNFxVm+nLNlzWRoOCOrz6s/Dtc+X3iOumZmGKPPhcu+Tfs7iwmtPir9K/j4vq0ChsHL20cJixyMbDwocIiGwQJi/IymP12Ypm3sloCbhz+2uV2JvbsqYl1Nm+0USkfTI6bkAobxUcTbnK5VD4Ltw3qr8g9QeaNZIvuuGaTXKzx7NcsWnur3onAkWfD5Y8kmqRc1kdbCbLaCHr3S1Yn+i7Kt8GEK2GVk00o099BsS+9S1Ujo9zQ6kZNrOD2h7Y3DzBDFbWx/UGOUWGRDcIcl0/9w86IjeEXFs1bBY8IYimll863wuP+P8B/b7YzOGO07Rg3Iflj55Ox0FnCseMeyesqVSdoveywVdaMga+nx/dzYVrwaxZtdq1cJ0yjWVfDF8hyNQvXCTzxzzbKyBj7G/TPW3BDU9PF/f1WNTLKbX9hEyvA/YkpmxYlpkqHvFkIVFhkg2QhkZ96E9+DnOAn/j44vLZBgc05c++l1iy1KmB95mIn8iKKz2LFIuuEm+Osda0L4uSeIGFRGuIsXrHIjoTBmhq67p399vhTnAd1NGHrm0QNwqguXAe3G/SxfiXcczG881Tl38cp18BeB2R+zyJHWPg1i5Xfw2sPJ87LSIZroYhZHIb5UvA1b1XZDOWmcMkhKiyyQbJZ01u85Uu//zL+MhU2hqufsp11UNbXtcttJEcy3BemSQRhMflfiWaxomLYpR4nX8wXQaGMYZFFS50pTHv0y37YLFht1BUQQcIiTLOI2eSNsYOZd5+F5QFRf9WF++7vNbjyjOa5HySaqvbc30YcVSXc1P2sNq6J+0eMgUevswLq8Zuirfdd7jNDgQ3VHXq83R40xgr7Zi3jYbq7dc9bmnSdZ5ENkgmLmFbgRmXsNyJuyw4axa1bUTmTZIMGdgQRm7m574/jx1zNIshuu2NHomMc7OI4uo5Ffjj8NHj94fj+pvXB9VwN0l2JL9v8/HKY8Sr0HR5s6w5bFGnpfHjzcTs3KDYHo1ETuy5GTUgb45pxmjSDsVfYJImxd79sc2KdqOnek9GkWTxtefk2G+22+Cs74Ivlptqwxgae+M1HflzNyPVljjkTDj81PngQgbP+aifr9RqYm0FFACossoF/ROAKjx++s6MMt7Pef2R8O0izqChP9HU0K4KjzoM+B0GnXnbftbO6duggu2nJqsS8OefdakNulfww7GdWQLz/P7sfplm4kzrbd85de7r8yP6FkSy55Fu+9ce2bbWLK/Uekp22ZcrKxYm/mcbNrAP65D/C30+3ZWWbfasOZkFYiFi/RczEHJRrC6AiQthxkGYRwy8QitpAv0OitzMLqBkqG7jCofdBcPbNicfWrkgMiXRnekYZkV3xGPQdZl+YIcdYzcTVCpq2iO9v3QSfvJF4vpv/p3sfq9qqVpFfXNt2kM9iU0l8GVRIXOku3/i13bDoqBh+X1y++fKjxJUAIe6b8YeVJ6xnngVhAdFCnKPMUXH7EX/IdQ1AhUU28Dumuu6dmEp6waz4iKZhYaI66h9x+F+8Lnul7tgbNbEmhRhvPha3kX431/orYhTXwmyidQG3A57zrh0Jx+zbM16Dv54SPy5Svd9TQUP73oEd2Bx1Lvz04vD6UdLi5xI3ggzsZx1b+a6wcfz3U74t7kMEu5BQNujaO3E/aB5ElFQv20LMUDUEFRbZYJtvMg0khry5juWW7RI7/55OorhOPeHyh+HCu+Kjnqiq5jG/jguh9avi2kQsDDeGCovqwRX6G9fakfBDf7QCY4ZvlvGu3eygojoZd6VNmnfa9da/tv9hNjlmEN9+Fj39ei7wd8Rjr4j7/EQSzU3uxNhsmKEADhkHx15oHdCjz7S5o467KHkbg0hmhqoBqLDIBu6XHFN/3dFGLB8UVJ5X0b6zTUK27zD4mbc62q7d4Df3wK/viB7p0Lgp9Dssvh8bbfknUrXLoS1cCadtB9vhunw72zpC/dlPjzw3v20LomWxfffceRhhobwlq+Fvp8D8Wflpmx93kaNxV1hTq4trbnJn0GfLDFXQEAaOthkVDj7edvQDDreDvxhRhEWYg7uGoMIiGwTZGjv3Co40CZqEN/go+PlliaF+LYuhQ4/02tHNEVBhqRmCkpMp+WHMWbDPwYllG9bEJ+mJ2KVPe+Q+z09GdE7iFN+2Faa/lL+2uLgdccOA31xY8sRsaRZhuGtkVNXBXQNQYZEN3El5sZDYwkaJJqYYuUjhEHTtmLBwc0Wdck31mzfqM01bwNg/JArsFYtym5I8m6QK/QxLlJlr3I44SFiECYVcCwt3sFgHHNw1+M2s4RgDrz4I38+FTU76APdL7jO0cibKqHnzM8HNcRPLqpmNpSOV7OJGwLmp7POQDK7K+EPD3bUd0k13sXCOfT97Dao8rygdXGERpM2HCYVsmaHCSLaWyYa18OJ99vPc52D723UjtWqgZqHCIlO+eC94pTH3S957SOIyjK3aJU6myzaxken2CqtRbNtql86M0USFRY3ANU/8sCi+XVQLhMVPL4En/mK3Dz8tMcqrdL1d6S9Kx790Pjxwpd0+7iJr488Ud9QelF4lTFhkKxoqDFfL8QuLDyfD3PftdiwlkEsNHNipsEiXTSUw7cn4BCs/7sta2MguTrRhjQ1lbdUut+tHiNgRSixh4fpViWaoMNutkl9cYZGgWdSChah6D4Ejz7EhqIOPsoOT5q3iK/xtWh9NQ3rlgfj2c3dUTVikMkOZkFQbOTdDJdEs/Ktq+snlDP4MUWGRLlMetakSwvCrjyK59VP4adUuLizuuiAxl78Ki5qBa4ZyZ9bXBs1CxE4MdWnZNu6k37AmmrBItnRouqRycIet8ZJPM5TfwZ3MZNekeXjKlWpEHdzpsiDFWsTVvaCQ67dwBUVBw7zkvFci4GoWLrulGf1WU8gkTXeQuShT3NDZoHc8KLlmQcPcD57cZ/SHsLuf06X3Jx4rL6uRGRZUWKTDhjXJF4Bp3iq/WkQQfYcHv2hNmtfIF7BeEiQs+gytWqrs6sTNOvDaQ/Cviyov/OUnmw7cVJpFp16Vy7r1zv3vwRVcH78SXx5gx47ElC9FxYm53vKUcjxdVFikg7tsYvd97FKULh33rP4OuWd/G6HiX9SmBjrM6i3+fGD9D7chtdX97mSKKyxWL7V+mBfHV17O1MUfwu1qB+mSKhpqxLjKmQtGnJT5/aLi154m/c3+31QST8fTrMh+FsecbwcRzVvBiLG5b1sGqM8iHZY6mWO77JXonISak8m1ZVs4+Kfw/D3xMvVX1Bz8wmLMWbVXUEDwanubN9hIvLC5Gf60/qXroXUGy5tu3x7veEUSV8iL0bylney4YhFM+6/NrpCPiY9BWs727YkmqJgJr10n+MOj1hlfQ+fa1MxW1VTWOXll2nepnD206175bU8y/Kq3CouaQ5NmNo/QjFfhxz+DprX8u2kdICzATgwNExZuaguwHWgmwqLCFzYbJnRFbEaEsX9I/x6ZEuSX+WByooXC1coaNKAmG3tUWPj55lOY+gR02duqg65tdc2y+HbbDokppQG675ufNkah4x42l08sL9Uu3aq3PUoiP7kAjji7bgQdFO8WXL5+JSz/Fj583o74Dz8N9vBs89t8wqI0ZEGoVKQKm61OgoTFqw8k7kdJb15DyJkYE5EHRGSliMwJOS4icoeILBCR2SLS3zl2mojM9/5Oy1UbA5l8Nyz6wi6H+Nyd8XJjEjWLNrtVzvvfKIsRHlVFBM78s+2URowLzxiqVB91QVBAeIf32kN2pbrl39pJeM/fHT/m1ywyTRVSnsJfUZ2k6g9EoM/ByevUIHKpWTwE3AU8EnL8CKCn9zcYuAcYLCLFwJ+AgYABZorIZGNMyPJiWcAY+8WtWxmfowA2Q+WxF9qMrps3xtekaNTECorBR8HHL1vn3MlX56x5GVPQ0Jo7FCWXhE009S+KtGaZTXPRsriyZuH+7tKhJmsW/vZ038dGS26vsGbhgaMTM9PWcHImLIwx00Ske5IqxwKPGGMM8KGItBaRDsAI4HVjzFoAEXkdGANMDL1SVShZDU/+HX5yYTwth8vyb23KY/dlLt7NCpe2HeHSCfaFDXLyKUp9IZZmJhWLv7Rhwv614jNdbc8Nm61pmoU/4uvU62peG9OgOr0pnQBnhXqWeGVh5ZUQkXNEZIaIzFi1KiQldzLWrYQJV9jV5O66AJ65rXKd2NrZy7+JlxU7y6IWtVFBoSgnXhYcieTn+y9t9JI//UWmwiJhQl4NMgNDZeFZiwUFVK+wCApbMEnKKxcaM94YM9AYM7B9+wwiKTatj6cpCIsJX+YJCXdhl+41dL0BRaku+hxkQz+vfQ6GnWi179a72IyqbgTSknmV/RVgNffPpkZL5e3iCp2CGpZ+v1PPuF+zhk60S4fqjIZaArir0ncGlnnlI3zlU3PSgs694JQ/waN/SnxJd9/PLhUJdn3h7RXxfYBeAetUKEp9JxYme/ip9i/GRsfduPzb8FxNT/0D9v80fL1vY+xv0TXv+ENnaxIFDeGcf1jTW6/av+hYdQqLycCFIvIE1sFdYoxZLiKvAn8WkVgmrVHAlTlrRY994LzbYN5MOyGmXSfYbXe49Sx7fONaWLM8PomodXvrq1AUJRpFbezksw1rrCYw/cXwuu6gzMUYeOxGmPcxjDrdTjqFmh0NBdBmF/tXB8iZsBCRiVgNoZ2ILMFGOBUCGGPuBV4CjgQWAJuBM7xja0XkBuBj71LXx5zdOWOXrvYvhvsClq6HjWvi+9WdKFBRagFXEIoAAA91SURBVCMd97TCAuDdZ+LlrdvbdV8+mGz3g0xUAGtXxNeVf/VBG53YfZ/EcNyaFg1Vx8hlNFTS5CteFNQFIcceAB4IOpYXChvZXEpbSm0q5R++ix9rUfNSBytKjadbH/jqo8rlPfrC6DPiwmLblngou4s/W8I7T9k/FxUWOaXmzi2vblyh4OaAUmGhKOlzwJHBc36G/MTa9mP5kHbsCE4quHlD5TI/GpWYU1RYhOEuPuKGzfpnbSuKkppGjW02gesn2wgpgIOOjWdubuwsceqfsAfxqMUY/UcmLl40YJQVSErO0NxQYbgpDFYsim+rZqEomSMCP78cjr840SHdyMuSAHbCnj8zryssfnyCdXKPOsNOpN29b+UJcErWUWERRphQUM1CUaqGSOW8Sa6WEKRZuGao2OJRzVtqGHseUTNUGGHrCKtmoSjZp5Fjhpo3w2ZXcNnkCAu/1qHkBRUWYfQ+KNGOGkM1C0XJPu5vbcqjcMd5Nm9bjCDNQskrKizCaN0eLnsYTrkmXtaspQoLRckFjXwDs4py+MiZvOf6LFRYVAvqs0hG46bwo0Fw/u0w8zWb36WGLnmoKLWaIC1+8wYbSiuimkUNQHu+KHTcAzqeX92tUJS6i1+zADtAm/mazZqw3vFhqM+iWlAzlKIo1U+QZhHDFRQt2yZGTil5Q4WFoijVT5BmEUQtWlmurqHCQlGU6ieqtqDCotpQYaEoSvUTZUlWsGvQKNWCCgtFUaqfII2hRWvoNTC+36iJrlJZjWg0lKIo1U+XvWDkL+3yqj/+mV2ru+veNl/Ut5/ZCKhT/qSh69WIfvKKolQ/IjD85/H9dp3i2/83ya5xURNXwqtHqLBQFKVmoxllawTqs1AURVFSosJCURRFSYkKC0VRFCUlKiwURVGUlKiwUBRFUVKiwkJRFEVJiQoLRVEUJSUqLBRFUZSUqLBQFEVRUqLCQlEURUlJToWFiIwRka9FZIGIXBFwvLGITPKOfyQi3Z1jV3rlX4vI6Fy2U1EURUlOzoSFiBQA/wKOAHoDJ4lIb1+1s4B1xpg9gduAv3nn9gbGAX2AMcDd3vUURVGUaiCXiQQPABYYY74FEJEngGOBuU6dY4Frve2ngLtERLzyJ4wxZcBCEVngXe+DsJvNnDkTe6qiKIqSbXJphuoELHb2l3hlgXWMMRVACdA24rmIyDkiMkNEZmSx3YqiKIqPXGoWQcN8E7FOlHMxxowHxgOISKXjiqIoSnbIpbBYAnRx9jsDy0LqLBGRhkArYG3EcxMYMGAAM2aogqEoipIOUc33uTRDfQz0FJEeItII67Ce7KszGTjN2/4Z8KYxxnjl47xoqR5AT2B6DtuqKIqiJCFnmoUxpkJELgReBQqAB4wxX4jI9cAMY8xkYALwqOfAXosVKHj1/ot1hlcAFxhjtueqrYqiKEpyxA7kaz8DBw40aoZSFEVJDxGZaYwZmKqezuBWFEVRUqLCQlEURUmJCgtFURQlJSosFEVRlJTUGQe3iGwEvsbO1SjxHQ4qS7e8HVCeo2vHytoBq2vhtWPE7lGVa6Qq9z9HNq+dzmdV1c8v7H3KxrVjZYVU/qxq4/ee7meVyTNG/ayq8vll8jmlW57O7yNWvpsxpijgWCLGmDrxhw3HBRgfcKxSWbrlwIwcXnu8+wy17dpRvoMsfjczcnjtyJ9VVT+/sPcpm99N0GdVG7/3dD+rTJ4x6mdVlc8vk88pk88qG88e9FcXzVDPRyzLVnlNuUZtvXa61wijPjx7Xbp2TW9fLq+dy/aFUeVr1CUz1AwTIVa4pl4/1/eo7e3Xe9TMe+TjPnXls6qp94h6Tl3SLMbX8uvn+h61vf16j5p5j3zcp658VjX1HpHOqTOahaIoipI76pJmoSiKouQIFRaKoihKSlRY+BCR0hxee7uIfOr8dU9Sd4SIvJDGtY2IPOrsNxSRVelcI417He/db68sXzdvz+BdP2ffdTr3EZGpIpK24zNX30PAff5PRL4Qkdneezs4B/foLCL/E5H5IvKNiPzTW9ogrP7FItIsjesbEfmHs/97Ebm2is12rx/7bX8hIp+JyKUikrP+NV/vrosKi/yyxRjTz/lblMVrbwL2EZGm3v7hwNJ0LuAtQBWFk4B38VLKp3H9ghRVqvwM9YyMvod0EJEhwNFAf2NMX2AkiUseZ+MeAjwDPGeM6Qn0AloANyU57WIgsrAAyoCfiki7jBuanNhvuw/2vT0S+FOO7lUtqLAIQERaiMgbIjJLRD4XkWO98u4i8qWI/NsbQbzmdGyZ3qtARP4uIh97I7dzncMtReRZEZkrIvdGGKm8DBzlbZ8ETHTuc4CIvC8in3j/f+SVny4iT4rI88BrEdrbAhgKnIXXSXla0LSgtopIqYhcLyIfAUMifCSZPMM7ItLPqfeeiPSNcK9KGpyI3CUip3vbi0TkOuc9yHgEn+w+GV4v7HsIe5YjReQrEXlXRO5IQ1vrAKw2xpQBGGNWG2OWicgAEXlbRGaKyKsi0sG7z1QRud37fuaIyAER7nEosNUY86B3j+3AJcCZItJcRG7xPv/ZIvIbEbkI6Ai8JSJvRXyOCmzUzyX+AyLSzfu9z/b+dxWRVt73H3uPm4nIYhEpTHUjY8xK4BzgQrGE/sZF5HLv2T4Tkb9GfJbYuXnrp0CFRRhbgeONMf2BQ4B/eKMfsKv2/csbQawHTkjjuk0lboJ61is7CygxxgwCBgFni10dEOAA4HfAvsAewE9TXP8J7AqDTYC+wEfOsa+AYcaY/YFrgD87x4YApxljDo3wDMcBrxhj5gFrRaR/irY2B+YYYwYbY96NcP1MnuF+4HQAEekFNDbGzI5wryis9t6De4DfZ+ma2SDse6iE91neBxxhjDkYaJ/GfV4DuojIPBG5W0SGex3mncDPjDEDgAdI1AKaG2MOAn7tHUtFH2CmW2CM2QB8D/wK6AHs72k2jxlj7sAus3yIMeaQNJ7lX8DJItLKV34X8Ejs+sAdxpgS4DNguFfnGOBVY0x5lBsZY77F9q+7EPIbF5EjsN/jYGPMfsDNaTwL5K6fCkSFRTAC/FlEZgNTgE7Art6xhcaYT73tmUD3NK7rmqGO98pGAaeKyKfYjrEt9osGmG6M+dYbaU0EDk52ca+D7I4dkb/kO9wKeFJE5gC3YX+gMV43xqyN+AwnYTt0vP8npWjrduDpiNfO9BmeBI72OrEzgYei3i8Cz3j/0/2uc03Y9xDEXsC3xpiF3v7EJHUTMMaUAgOwI+VVwCTgXGAf4HXvvf0j0Nk5baJ37jSsdtw6xW0ECIrhF2AYcK8xpsK7ZtT3NOhZNgCPABf5Dg0BHve2HyX+7k4Cxnrb47z9dIh13GG/8ZHAg8aYzV770n22XPVTgeRsWdVazsnY0dcAY0y5iCwCmnjHypx624GqqncC/MYY82pCocgIKv+AokyKmQzcAozAvpQxbgDeMsYcL9axPtU5tilSQ0XaYk0G+4iIwS6Xa7Cdelhbt5r0l8RN6xmMMZtF5HXgWODnQDrO4goSB01NfMdj3/d2qvZ7SXWfyCT5HiaH3EOoAt73NxWYKiKfAxcAXxhjwsyK6b63X+Ab+YpIS6AL8G2E89PhdmAW8GCSOrH7TQb+IiLFWIH5ZtSbiMju2HdmJeG/8TFU7dny2U+pZhFCK2Cl9wUcAnTL4b1eBc6P2UJFpJeINPeOHeCpqw2wI5woZpwHgOuNMZ/7ylsRdxafnmFbf4ZV17sZY7obY7oAC7EjsUzaGkYmz3A/cAfwcZojtO+A3iLS2DNPHJZBe/N9n7DvgZB7fAXsLvHou7FERER+JCI9naJ+wJdAe7HOb0SkUERcTXWsV34w1vwSlO3U5Q2gmYic6p1XAPwDqyG+BpwnXvCF13EDbARSZ0r14b0b/8WahmK8TzxI4GS8d9fTqqYD/wReiDroEZH2wL3AXcbOeg77jb+G9cs08z1bVPLZT6lm4eK9kGVYu+XzIjID+BT7Y8sV92NVxFmevXEV1o4J8AHwV6wfYBrwbNAFXIwxS7Avt5+bgYdF5FLSGCH5OMlrj8vTwPmZtDWMTJ7BGDNTRDaQfMS4k9h3bYxZLCL/BWYD84FPMm13Hu8T9j38AtsRJtzDGLNFRH4NvCIiq7EdYFRaAHd6pqQKYAHWJDUeuMMTSg2xI/YvvHPWicj7QEusWTApxhgjIscDd4vI1dhB7EvAVdhRcS9gtoiUA//G+hjGAy+LyPI0/RZgBdGFzv5FwAMichn293eGc2wS1sw5IsU1m3pmpkLs5/Qo/H97dwxSVRTGAfz/J2mpBhsqlRpakpZoKqKtliDKJWgIXAraMhCKamlrCJsaIoKcAsGghggcChoiEpcSh2jIqIcFBuZShP+G70hPsO55entXvP/f9Hj3cTzXezyf99zvfA9D6diyf+OSnjISM8ZJ/mw653+qaJ5yuY9mJPcBuCspJ4PDkrRkNijpeIV96EYslfRKWsj4fFuu9VoZUyQ3S5pPk9VtAO8k3foPP+c5YiyMl922harGlJehEpLnEQ/mrlXdF2tNWr54BeBqZqBoy7VeY2PqXPrPdxKxfHGn4v7YClQ5pnxnYWZmhWp7Z0FyJ8lnjM0rkyQvpPe3khxjlB0YI9mZ3u8l+ZLkD5KDTe3s4dISHnMkB6o6LzNbP8qap9Kxi6mNtyQfMPbf5PelrncWjB2nXZImSG5B5CL3IbJsZiXdIHkZQKekSyS3IbIN+gB8k3RzmTY3ILJ1Dkj60K5zMbP1qax5imQPIstrb0p4GAHwRNL93L7U9s5CUkPSRHr9HZEO2IPI1R9OHxtGykyS9EXSa8QXx//NEQDvHSjMrAwlz1MdiKytDkRdrc+t9KW2waJZyj/fj3hIul1SA4gLhdiun+s0Wtgda2aWazXzlKRPiI2u0wAaiP0vhbXgmtU+WDAKso0CGEjlAFbazkYAJxA52WZmpVntPJWeaZxE1NnqBrCJ5JlW2qh1sEg7KkcRxckWawDN8E8FzS7Edv0cxwBMSJopv6dmVlclzVNHEfWivqZiiA8BHGqlH7UNFmlz0j0AU5KGmg49BtCfXvcDeJTZ5JJy2mZmq1XiPDUN4CCj1DoRz1enWupLjbOhDgN4AeANgMWNXFcQ64EjAHYhfsGnJM2S3AFgHFHCYAHAPCKzYC7VdvkIYHdGHRwzsywlz1PXEXW7fiFKwZxV+p6SrL7UNViYmVm+2i5DmZlZPgcLMzMr5GBhZmaFHCzMzKyQg4WZmRVysDAzs0IOFmZmVsjBwszMCv0GqbEAhYORBbYAAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pf.plot_rolling_sharpe(df)" ] } ], @@ -67,7 +481,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.0" + "version": "3.6.4" } }, "nbformat": 4, diff --git a/alphapy/examples/Trading System/config/market.yml b/alphapy/examples/Trading System/config/market.yml index 6991b1f..d01f3bd 100644 --- a/alphapy/examples/Trading System/config/market.yml +++ b/alphapy/examples/Trading System/config/market.yml @@ -1,10 +1,13 @@ market: - data_history : 1000 + data_history : 500 forecast_period : 1 fractal : 1d + lag_period : 1 leaders : [] predict_history : 50 - schema : google + resample_data : False + schema : quandl_wiki + subject : stock target_group : faang system: diff --git a/alphapy/examples/Trading System/systems/faang_closer_positions_1d.csv b/alphapy/examples/Trading System/systems/faang_closer_positions_1d.csv new file mode 100644 index 0000000..926344a --- /dev/null +++ b/alphapy/examples/Trading System/systems/faang_closer_positions_1d.csv @@ -0,0 +1,499 @@ +date,nflx,amzn,fb,aapl,googl,cash +2016-08-19,-19940.96,-19690.059999999998,-19893.16,19903.52,-19991.25,581.0500000000065 +2016-08-20,-19940.96,-19690.059999999998,-19893.16,19903.52,-19991.25,581.0500000000065 +2016-08-21,-19940.96,-19690.059999999998,-19893.16,19903.52,-19991.25,581.0500000000065 +2016-08-22,-19940.96,19746.48,19988.15,-19965.84,-19991.25,364.0300000000061 +2016-08-23,19955.52,19746.48,19988.15,19919.55,-19923.75,472.88000000000466 +2016-08-24,-19987.800000000003,-19688.5,-20003.760000000002,-19985.55,-19914.75,-57.0199999999968 +2016-08-25,19853.28,19739.72,19822.4,-19985.55,-19840.0,774.6800000000003 +2016-08-26,19853.28,19739.72,19822.4,-19900.449999999997,19830.5,774.6800000000003 +2016-08-27,19906.32,19994.0,19993.6,-19783.899999999998,19830.5,774.6800000000003 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members of the group are stored in - one file. If ``False``, then the data are stored in separate - files corresponding with each member. + separate files corresponding with each member. If ``False``, + then the data are stored in a single file. Returns ------- diff --git a/alphapy/globals.py b/alphapy/globals.py index bab9462..5b0a71b 100644 --- a/alphapy/globals.py +++ b/alphapy/globals.py @@ -65,7 +65,20 @@ # Dictionaries # -MULTIPLIERS = {'stock' : 1.0} +MULTIPLIERS = {'crypto' : 1.0, + 'stock' : 1.0} + +# +# Pandas Time Offset Aliases +# + +PD_INTRADAY_OFFSETS = ['H', 'T', 'min', 'S', 'L', 'ms', 'U', 'us', 'N'] + +# +# Pandas Web Reader Feeds +# + +PD_WEB_DATA_FEEDS = ['google', 'quandl', 'yahoo'] # diff --git a/alphapy/market_flow.py b/alphapy/market_flow.py index aec700c..58c8f49 100644 --- a/alphapy/market_flow.py +++ b/alphapy/market_flow.py @@ -29,7 +29,7 @@ from alphapy.alias import Alias from alphapy.analysis import Analysis from alphapy.analysis import run_analysis -from alphapy.data import get_feed_data +from alphapy.data import get_market_data from alphapy.globals import PSEP, SSEP from alphapy.group import Group from alphapy.market_variables import Variable @@ -90,17 +90,25 @@ def get_market_config(): # Section: market [this section must be first] specs['forecast_period'] = cfg['market']['forecast_period'] - specs['fractal'] = cfg['market']['fractal'] + fractal = cfg['market']['fractal'] + try: + test_interval = pd.to_timedelta(fractal) + except: + logger.info("Pandas offset alias [%s] is invalid for resampling", + fractal) + specs['fractal'] = fractal specs['lag_period'] = cfg['market']['lag_period'] specs['leaders'] = cfg['market']['leaders'] specs['data_history'] = cfg['market']['data_history'] specs['predict_history'] = cfg['market']['predict_history'] + specs['resample_data'] = cfg['market']['resample_data'] specs['schema'] = cfg['market']['schema'] + specs['subject'] = cfg['market']['subject'] specs['target_group'] = cfg['market']['target_group'] # Create the subject/schema/fractal namespace - sspecs = ['stock', specs['schema'], specs['fractal']] + sspecs = [specs['subject'], specs['schema'], specs['fractal']] space = Space(*sspecs) # Section: features @@ -168,7 +176,9 @@ def get_market_config(): logger.info('lag_period = %d', specs['lag_period']) logger.info('leaders = %s', specs['leaders']) logger.info('predict_history = %s', specs['predict_history']) + logger.info('resample_data = %r', specs['resample_data']) logger.info('schema = %s', specs['schema']) + logger.info('subject = %s', specs['subject']) logger.info('system = %s', specs['system']) logger.info('target_group = %s', specs['target_group']) @@ -221,6 +231,7 @@ def market_pipeline(model, market_specs): lag_period = market_specs['lag_period'] leaders = market_specs['leaders'] predict_history = market_specs['predict_history'] + resample_data = market_specs['resample_data'] target_group = market_specs['target_group'] # Get the system specifications @@ -251,10 +262,12 @@ def market_pipeline(model, market_specs): # predict_history resets to the actual history obtained. lookback = predict_history if predict_mode else data_history - new_history = get_feed_data(group, lookback) + new_history = get_market_data(model, group, lookback, resample_data) if new_history < data_history: logger.info("Maximum Data History is %d, not %d", new_history, data_history) + if new_history == 0: + raise ValueError("Could not get market data from source") # Apply the features to all of the frames diff --git a/alphapy/system.py b/alphapy/system.py index 3f53940..e7117fe 100644 --- a/alphapy/system.py +++ b/alphapy/system.py @@ -263,7 +263,8 @@ def long_short(system, name, space, quantity): # Function open_range_breakout # -def open_range_breakout(name, space, quantity, t1=3, t2=12): +def open_range_breakout(name, space, quantity, t1=3, t2=12, + long_only=False): r"""Run an Opening Range Breakout (ORB) system. An ORB system is an intraday strategy that waits for price to @@ -329,7 +330,7 @@ def open_range_breakout(name, space, quantity, t1=3, t2=12): tradelist.append((dt, [name, Orders.le, quantity, hh])) inlong = True traded = True - if l < ll and not traded: + if l < ll and not traded and not long_only: # short breakout triggers tradelist.append((dt, [name, Orders.se, -quantity, ll])) inshort = True diff --git a/docs/tutorials/closer_market.yml b/docs/tutorials/closer_market.yml index 635824e..d01f3bd 100644 --- a/docs/tutorials/closer_market.yml +++ b/docs/tutorials/closer_market.yml @@ -1,10 +1,13 @@ market: - data_history : 1000 + data_history : 500 forecast_period : 1 fractal : 1d + lag_period : 1 leaders : [] predict_history : 50 - schema : prices + resample_data : False + schema : quandl_wiki + subject : stock target_group : faang system: diff --git a/docs/tutorials/market.rst b/docs/tutorials/market.rst index a227c4a..06d0fc6 100644 --- a/docs/tutorials/market.rst +++ b/docs/tutorials/market.rst @@ -85,7 +85,7 @@ what we are trying to predict. **Step 2**: Now, let's run MarketFlow:: - mflow --pdate 2017-01-01 + mflow --pdate 2017-10-01 As ``mflow`` runs, you will see the progress of the workflow, and the logging output is saved in ``market_flow.log``. When the @@ -100,7 +100,9 @@ with a different datestamp:: ├── model.yml └── data └── input + ├── test_20170420.csv ├── test.csv + ├── train_20170420.csv ├── train.csv └── model ├── feature_map_20170420.pkl diff --git a/docs/tutorials/rrover_market.yml b/docs/tutorials/rrover_market.yml index 7f73975..3ed085e 100644 --- a/docs/tutorials/rrover_market.yml +++ b/docs/tutorials/rrover_market.yml @@ -1,10 +1,13 @@ market: - data_history : 2000 + data_history : 500 forecast_period : 1 fractal : 1d + lag_period : 1 leaders : ['gap', 'gapbadown', 'gapbaup', 'gapdown', 'gapup'] predict_history : 100 - schema : prices + resample_data : False + schema : yahoo + subject : stock target_group : test groups: diff --git a/docs/user_guide/alphapy.log b/docs/user_guide/alphapy.log index 0f81e31..053f136 100644 --- a/docs/user_guide/alphapy.log +++ b/docs/user_guide/alphapy.log @@ -1,354 +1,361 @@ -[04/18/17 12:08:34] INFO ******************************************************************************** -[04/18/17 12:08:34] INFO AlphaPy Start -[04/18/17 12:08:34] INFO ******************************************************************************** -[04/18/17 12:08:34] INFO Model Configuration -[04/18/17 12:08:34] INFO No Treatments Found -[04/18/17 12:08:34] INFO MODEL PARAMETERS: -[04/18/17 12:08:34] INFO algorithms = ['RF', 'XGB'] -[04/18/17 12:08:34] INFO balance_classes = True -[04/18/17 12:08:34] INFO calibration = False -[04/18/17 12:08:34] INFO cal_type = sigmoid -[04/18/17 12:08:34] INFO calibration_plot = False -[04/18/17 12:08:34] INFO clustering = True -[04/18/17 12:08:34] INFO cluster_inc = 3 -[04/18/17 12:08:34] INFO cluster_max = 30 -[04/18/17 12:08:34] INFO cluster_min = 3 -[04/18/17 12:08:34] INFO confusion_matrix = True -[04/18/17 12:08:34] INFO counts = True -[04/18/17 12:08:34] INFO cv_folds = 3 -[04/18/17 12:08:34] INFO directory = /Users/markconway/Projects/Titanic -[04/18/17 12:08:34] INFO extension = csv -[04/18/17 12:08:34] INFO drop = ['PassengerId'] -[04/18/17 12:08:34] INFO encoder = -[04/18/17 12:08:34] INFO esr = 20 -[04/18/17 12:08:34] INFO factors = [] -[04/18/17 12:08:34] INFO features [X] = * -[04/18/17 12:08:34] INFO feature_selection = False -[04/18/17 12:08:34] INFO fs_percentage = 50 -[04/18/17 12:08:34] INFO fs_score_func = -[04/18/17 12:08:34] INFO fs_uni_grid = [5, 10, 15, 20, 25] -[04/18/17 12:08:34] INFO grid_search = True -[04/18/17 12:08:34] INFO gs_iters = 50 -[04/18/17 12:08:34] INFO gs_random = True -[04/18/17 12:08:34] INFO gs_sample = False -[04/18/17 12:08:34] INFO gs_sample_pct = 0.200000 -[04/18/17 12:08:34] INFO importances = True -[04/18/17 12:08:34] INFO interactions = True -[04/18/17 12:08:34] INFO isomap = False -[04/18/17 12:08:34] INFO iso_components = 2 -[04/18/17 12:08:34] INFO iso_neighbors = 5 -[04/18/17 12:08:34] INFO isample_pct = 10 -[04/18/17 12:08:34] INFO learning_curve = True -[04/18/17 12:08:34] INFO logtransform = False -[04/18/17 12:08:34] INFO lv_remove = True -[04/18/17 12:08:34] INFO lv_threshold = 0.100000 -[04/18/17 12:08:34] INFO model_type = -[04/18/17 12:08:34] INFO n_estimators = 51 -[04/18/17 12:08:34] INFO n_jobs = -1 -[04/18/17 12:08:34] INFO ngrams_max = 3 -[04/18/17 12:08:34] INFO numpy = True -[04/18/17 12:08:34] INFO pca = False -[04/18/17 12:08:34] INFO pca_inc = 1 -[04/18/17 12:08:34] INFO pca_max = 10 -[04/18/17 12:08:34] INFO pca_min = 2 -[04/18/17 12:08:34] INFO pca_whiten = False -[04/18/17 12:08:34] INFO poly_degree = 5 -[04/18/17 12:08:34] INFO pvalue_level = 0.010000 -[04/18/17 12:08:34] INFO rfe = True -[04/18/17 12:08:34] INFO rfe_step = 3 -[04/18/17 12:08:34] INFO roc_curve = True -[04/18/17 12:08:34] INFO rounding = 2 -[04/18/17 12:08:34] INFO sampling = False -[04/18/17 12:08:34] INFO sampling_method = -[04/18/17 12:08:34] INFO sampling_ratio = 0.500000 -[04/18/17 12:08:34] INFO scaler_option = True -[04/18/17 12:08:34] INFO scaler_type = -[04/18/17 12:08:34] INFO scipy = False -[04/18/17 12:08:34] INFO scorer = roc_auc -[04/18/17 12:08:34] INFO seed = 42 -[04/18/17 12:08:34] INFO sentinel = -1 -[04/18/17 12:08:34] INFO separator = , -[04/18/17 12:08:34] INFO shuffle = False -[04/18/17 12:08:34] INFO split = 0.400000 -[04/18/17 12:08:34] INFO submission_file = gender_submission -[04/18/17 12:08:34] INFO submit_probas = False -[04/18/17 12:08:34] INFO target [y] = Survived -[04/18/17 12:08:34] INFO target_value = 1 -[04/18/17 12:08:34] INFO treatments = None -[04/18/17 12:08:34] INFO tsne = False -[04/18/17 12:08:34] INFO tsne_components = 2 -[04/18/17 12:08:34] INFO tsne_learn_rate = 1000.000000 -[04/18/17 12:08:34] INFO tsne_perplexity = 30.000000 -[04/18/17 12:08:34] INFO vectorize = False -[04/18/17 12:08:34] INFO verbosity = 0 -[04/18/17 12:08:34] INFO Creating Model -[04/18/17 12:08:34] INFO Calling Pipeline -[04/18/17 12:08:34] INFO Training Pipeline -[04/18/17 12:08:34] INFO Loading Data -[04/18/17 12:08:34] INFO Loading data from /Users/markconway/Projects/Titanic/input/train.csv -[04/18/17 12:08:34] INFO Found target Survived in data frame -[04/18/17 12:08:34] INFO Dropping target Survived from data frame -[04/18/17 12:08:34] INFO Loading Data -[04/18/17 12:08:34] INFO Loading data from /Users/markconway/Projects/Titanic/input/test.csv -[04/18/17 12:08:34] INFO Target Survived not found in partition Partition.test -[04/18/17 12:08:34] INFO Saving New Features in Model -[04/18/17 12:08:34] INFO Dropping Features: ['PassengerId'] -[04/18/17 12:08:34] INFO Saving New Features in Model -[04/18/17 12:08:34] INFO Original Feature Statistics -[04/18/17 12:08:34] INFO Number of Training Rows : 891 -[04/18/17 12:08:34] INFO Number of Training Columns : 10 -[04/18/17 12:08:34] INFO Unique Training Values for Survived : [0 1] -[04/18/17 12:08:34] INFO Unique Training Counts for Survived : [549 342] -[04/18/17 12:08:34] INFO Number of Testing Rows : 418 -[04/18/17 12:08:34] INFO Number of Testing Columns : 10 -[04/18/17 12:08:34] INFO Original Features : Index(['Pclass', 'Name', 'Sex', 'Age', 'SibSp', 'Parch', 'Ticket', 'Fare', - 'Cabin', 'Embarked'], +[12/30/17 23:17:49] INFO ******************************************************************************** +[12/30/17 23:17:49] INFO AlphaPy Start +[12/30/17 23:17:49] INFO ******************************************************************************** +[12/30/17 23:17:49] INFO Model Configuration +[12/30/17 23:17:49] INFO No Treatments Found +[12/30/17 23:17:49] INFO MODEL PARAMETERS: +[12/30/17 23:17:49] INFO algorithms = ['RF', 'XGB'] +[12/30/17 23:17:49] INFO balance_classes = True +[12/30/17 23:17:49] INFO calibration = False +[12/30/17 23:17:49] INFO cal_type = sigmoid +[12/30/17 23:17:49] INFO calibration_plot = False +[12/30/17 23:17:49] INFO clustering = True +[12/30/17 23:17:49] INFO cluster_inc = 3 +[12/30/17 23:17:49] INFO cluster_max = 30 +[12/30/17 23:17:49] INFO cluster_min = 3 +[12/30/17 23:17:49] INFO confusion_matrix = True +[12/30/17 23:17:49] INFO counts = True +[12/30/17 23:17:49] INFO cv_folds = 3 +[12/30/17 23:17:49] INFO directory = . +[12/30/17 23:17:49] INFO extension = csv +[12/30/17 23:17:49] INFO drop = ['PassengerId'] +[12/30/17 23:17:49] INFO encoder = +[12/30/17 23:17:50] INFO esr = 20 +[12/30/17 23:17:50] INFO factors = [] +[12/30/17 23:17:50] INFO features [X] = * +[12/30/17 23:17:50] INFO feature_selection = False +[12/30/17 23:17:50] INFO fs_percentage = 50 +[12/30/17 23:17:50] INFO fs_score_func = +[12/30/17 23:17:50] INFO fs_uni_grid = [5, 10, 15, 20, 25] +[12/30/17 23:17:50] INFO grid_search = True +[12/30/17 23:17:50] INFO gs_iters = 50 +[12/30/17 23:17:50] INFO gs_random = True +[12/30/17 23:17:50] INFO gs_sample = False +[12/30/17 23:17:50] INFO gs_sample_pct = 0.200000 +[12/30/17 23:17:50] INFO importances = True +[12/30/17 23:17:50] INFO interactions = True +[12/30/17 23:17:50] INFO isomap = False +[12/30/17 23:17:50] INFO iso_components = 2 +[12/30/17 23:17:50] INFO iso_neighbors = 5 +[12/30/17 23:17:50] INFO isample_pct = 10 +[12/30/17 23:17:50] INFO learning_curve = True +[12/30/17 23:17:50] INFO logtransform = False +[12/30/17 23:17:50] INFO lv_remove = True +[12/30/17 23:17:50] INFO lv_threshold = 0.100000 +[12/30/17 23:17:50] INFO model_type = +[12/30/17 23:17:50] INFO n_estimators = 51 +[12/30/17 23:17:50] INFO n_jobs = -1 +[12/30/17 23:17:50] INFO ngrams_max = 3 +[12/30/17 23:17:50] INFO numpy = True +[12/30/17 23:17:50] INFO pca = False +[12/30/17 23:17:50] INFO pca_inc = 1 +[12/30/17 23:17:50] INFO pca_max = 10 +[12/30/17 23:17:50] INFO pca_min = 2 +[12/30/17 23:17:50] INFO pca_whiten = False +[12/30/17 23:17:50] INFO poly_degree = 5 +[12/30/17 23:17:50] INFO pvalue_level = 0.010000 +[12/30/17 23:17:50] INFO rfe = True +[12/30/17 23:17:50] INFO rfe_step = 3 +[12/30/17 23:17:50] INFO roc_curve = True +[12/30/17 23:17:50] INFO rounding = 2 +[12/30/17 23:17:50] INFO sampling = False +[12/30/17 23:17:50] INFO sampling_method = +[12/30/17 23:17:50] INFO sampling_ratio = 0.500000 +[12/30/17 23:17:50] INFO scaler_option = True +[12/30/17 23:17:50] INFO scaler_type = +[12/30/17 23:17:50] INFO scipy = False +[12/30/17 23:17:50] INFO scorer = roc_auc +[12/30/17 23:17:50] INFO seed = 42 +[12/30/17 23:17:50] INFO sentinel = -1 +[12/30/17 23:17:50] INFO separator = , +[12/30/17 23:17:50] INFO shuffle = False +[12/30/17 23:17:50] INFO split = 0.400000 +[12/30/17 23:17:50] INFO submission_file = gender_submission +[12/30/17 23:17:50] INFO submit_probas = False +[12/30/17 23:17:50] INFO target [y] = Survived +[12/30/17 23:17:50] INFO target_value = 1 +[12/30/17 23:17:50] INFO treatments = None +[12/30/17 23:17:50] INFO tsne = False +[12/30/17 23:17:50] INFO tsne_components = 2 +[12/30/17 23:17:50] INFO tsne_learn_rate = 1000.000000 +[12/30/17 23:17:50] INFO tsne_perplexity = 30.000000 +[12/30/17 23:17:50] INFO vectorize = False +[12/30/17 23:17:50] INFO verbosity = 0 +[12/30/17 23:17:50] INFO Creating directory ./data +[12/30/17 23:17:50] INFO Creating directory ./model +[12/30/17 23:17:50] INFO Creating directory ./output +[12/30/17 23:17:50] INFO Creating directory ./plots +[12/30/17 23:17:50] INFO Creating Model +[12/30/17 23:17:50] INFO Calling Pipeline +[12/30/17 23:17:50] INFO Training Pipeline +[12/30/17 23:17:50] INFO Loading Data +[12/30/17 23:17:50] INFO Loading data from ./input/train.csv +[12/30/17 23:17:50] INFO Found target Survived in data frame +[12/30/17 23:17:50] INFO Labels (y) found for Partition.train +[12/30/17 23:17:50] INFO Loading Data +[12/30/17 23:17:50] INFO Loading data from ./input/test.csv +[12/30/17 23:17:50] INFO Target Survived not found in Partition.test +[12/30/17 23:17:50] INFO Saving New Features in Model +[12/30/17 23:17:50] INFO Original Feature Statistics +[12/30/17 23:17:50] INFO Number of Training Rows : 891 +[12/30/17 23:17:50] INFO Number of Training Columns : 11 +[12/30/17 23:17:50] INFO Unique Training Values for Survived : [0 1] +[12/30/17 23:17:50] INFO Unique Training Counts for Survived : [549 342] +[12/30/17 23:17:50] INFO Number of Testing Rows : 418 +[12/30/17 23:17:50] INFO Number of Testing Columns : 11 +[12/30/17 23:17:50] INFO Original Features : Index(['PassengerId', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp', 'Parch', + 'Ticket', 'Fare', 'Cabin', 'Embarked'], dtype='object') -[04/18/17 12:08:34] INFO Feature Count : 10 -[04/18/17 12:08:34] INFO Applying Treatments -[04/18/17 12:08:34] INFO New Feature Count : 10 -[04/18/17 12:08:34] INFO Saving New Features in Model -[04/18/17 12:08:34] INFO Creating Cross-Tabulations -[04/18/17 12:08:34] INFO Original Features : Index(['Pclass', 'Name', 'Sex', 'Age', 'SibSp', 'Parch', 'Ticket', 'Fare', +[12/30/17 23:17:50] INFO Feature Count : 11 +[12/30/17 23:17:50] INFO Applying Treatments +[12/30/17 23:17:50] INFO No Treatments Specified +[12/30/17 23:17:50] INFO New Feature Count : 11 +[12/30/17 23:17:50] INFO Dropping Features: ['PassengerId'] +[12/30/17 23:17:50] INFO Original Feature Count : 11 +[12/30/17 23:17:50] INFO Reduced Feature Count : 10 +[12/30/17 23:17:50] INFO Writing data frame to ./input/train_20171230.csv +[12/30/17 23:17:50] INFO Writing data frame to ./input/test_20171230.csv +[12/30/17 23:17:50] INFO Creating Cross-Tabulations +[12/30/17 23:17:50] INFO Original Features : Index(['Pclass', 'Name', 'Sex', 'Age', 'SibSp', 'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'], dtype='object') -[04/18/17 12:08:34] INFO Feature Count : 10 -[04/18/17 12:08:34] INFO Creating Count Features -[04/18/17 12:08:34] INFO NA Counts -[04/18/17 12:08:34] INFO Number Counts -[04/18/17 12:08:34] INFO New Feature Count : 21 -[04/18/17 12:08:34] INFO Creating Base Features -[04/18/17 12:08:34] INFO Feature 1: Pclass is a numerical feature of type int64 with 3 unique values -[04/18/17 12:08:34] INFO Feature 2: Name is a text feature [12:82] with 1307 unique values -[04/18/17 12:08:34] INFO Feature 2: Name => Factorization -[04/18/17 12:08:34] INFO Feature 3: Sex is a text feature [4:6] with 2 unique values -[04/18/17 12:08:34] INFO Feature 3: Sex => Factorization -[04/18/17 12:08:34] INFO Feature 4: Age is a numerical feature of type float64 with 99 unique values -[04/18/17 12:08:34] INFO Feature 5: SibSp is a numerical feature of type int64 with 7 unique values -[04/18/17 12:08:34] INFO Feature 6: Parch is a numerical feature of type int64 with 8 unique values -[04/18/17 12:08:34] INFO Feature 7: Ticket is a text feature [3:18] with 929 unique values -[04/18/17 12:08:34] INFO Feature 7: Ticket => Factorization -[04/18/17 12:08:34] INFO Feature 8: Fare is a numerical feature of type float64 with 282 unique values -[04/18/17 12:08:34] INFO Feature 9: Cabin is a text feature [1:15] with 187 unique values -[04/18/17 12:08:34] INFO Feature 9: Cabin => Factorization -[04/18/17 12:08:34] INFO Feature 10: Embarked is a text feature [1:1] with 4 unique values -[04/18/17 12:08:34] INFO Feature 10: Embarked => Factorization -[04/18/17 12:08:34] INFO Feature 11: nan_count is a numerical feature of type int64 with 3 unique values -[04/18/17 12:08:34] INFO Feature 12: count_0 is a numerical feature of type int64 with 4 unique values -[04/18/17 12:08:34] INFO Feature 13: count_1 is a numerical feature of type int64 with 4 unique values -[04/18/17 12:08:34] INFO Feature 14: count_2 is a numerical feature of type int64 with 4 unique values -[04/18/17 12:08:34] INFO Feature 15: count_3 is a numerical feature of type int64 with 4 unique values -[04/18/17 12:08:34] INFO Feature 16: count_4 is a numerical feature of type int64 with 3 unique values -[04/18/17 12:08:34] INFO Feature 17: count_5 is a numerical feature of type int64 with 2 unique values -[04/18/17 12:08:34] INFO Feature 18: count_6 is a numerical feature of type int64 with 2 unique values -[04/18/17 12:08:34] INFO Feature 19: count_7 is a numerical feature of type int64 with 2 unique values -[04/18/17 12:08:34] INFO Feature 20: count_8 is a numerical feature of type int64 with 3 unique values -[04/18/17 12:08:34] INFO Feature 21: count_9 is a numerical feature of type int64 with 3 unique values -[04/18/17 12:08:34] INFO New Feature Count : 21 -[04/18/17 12:08:34] INFO Scaling Base Features -[04/18/17 12:08:34] INFO Creating NumPy Features -[04/18/17 12:08:34] INFO NumPy Feature: sum -[04/18/17 12:08:34] INFO NumPy Feature: mean -[04/18/17 12:08:34] INFO NumPy Feature: standard deviation -[04/18/17 12:08:34] INFO NumPy Feature: variance -[04/18/17 12:08:34] INFO NumPy Feature Count : 4 -[04/18/17 12:08:34] INFO New Feature Count : 25 -[04/18/17 12:08:34] INFO Creating Clustering Features -[04/18/17 12:08:34] INFO Cluster Minimum : 3 -[04/18/17 12:08:34] INFO Cluster Maximum : 30 -[04/18/17 12:08:34] INFO Cluster Increment : 3 -[04/18/17 12:08:34] INFO k = 3 -[04/18/17 12:08:34] INFO k = 6 -[04/18/17 12:08:34] INFO k = 9 -[04/18/17 12:08:34] INFO k = 12 -[04/18/17 12:08:34] INFO k = 15 -[04/18/17 12:08:34] INFO k = 18 -[04/18/17 12:08:34] INFO k = 21 -[04/18/17 12:08:34] INFO k = 24 -[04/18/17 12:08:34] INFO k = 27 -[04/18/17 12:08:34] INFO k = 30 -[04/18/17 12:08:35] INFO Clustering Feature Count : 10 -[04/18/17 12:08:35] INFO New Feature Count : 35 -[04/18/17 12:08:35] INFO Saving New Features in Model -[04/18/17 12:08:35] INFO Creating Interactions -[04/18/17 12:08:35] INFO Initial Feature Count : 35 -[04/18/17 12:08:35] INFO Generating Polynomial Features -[04/18/17 12:08:35] INFO Interaction Percentage : 10 -[04/18/17 12:08:35] INFO Polynomial Degree : 5 -[04/18/17 12:08:35] INFO Polynomial Feature Count : 15 -[04/18/17 12:08:35] INFO New Total Feature Count : 50 -[04/18/17 12:08:35] INFO Saving New Features in Model -[04/18/17 12:08:35] INFO Removing Low-Variance Features -[04/18/17 12:08:35] INFO Low-Variance Threshold : 0.10 -[04/18/17 12:08:35] INFO Original Feature Count : 50 -[04/18/17 12:08:35] INFO Reduced Feature Count : 50 -[04/18/17 12:08:35] INFO Saving New Features in Model -[04/18/17 12:08:35] INFO Skipping Shuffling -[04/18/17 12:08:35] INFO Skipping Sampling -[04/18/17 12:08:35] INFO Getting Class Weights -[04/18/17 12:08:35] INFO Class Weight for target Survived [1]: 1.605263 -[04/18/17 12:08:35] INFO Getting All Estimators -[04/18/17 12:08:35] INFO Algorithm Configuration -[04/18/17 12:08:35] INFO Selecting Models -[04/18/17 12:08:35] INFO Algorithm: RF -[04/18/17 12:08:35] INFO Fitting Initial Model -[04/18/17 12:08:35] INFO Recursive Feature Elimination with CV -[04/18/17 12:08:58] INFO RFECV took 23.22 seconds for step 3 and 3 folds -[04/18/17 12:08:58] INFO Algorithm: RF, Selected Features: 14, Ranking: [ 5 1 1 1 8 11 1 1 4 8 11 8 9 9 9 13 12 13 13 11 12 1 1 1 1 - 12 10 7 6 2 7 4 6 7 3 3 1 5 10 1 2 10 1 4 6 1 2 3 5 1] -[04/18/17 12:08:58] INFO Randomized Grid Search -[04/18/17 12:09:39] INFO Grid Search took 40.42 seconds for 50 candidate parameter settings. -[04/18/17 12:09:39] INFO Model with rank: 1 -[04/18/17 12:09:39] INFO Mean validation score: 0.862 (std: 0.022) -[04/18/17 12:09:39] INFO Parameters: {'est__n_estimators': 201, 'est__min_samples_split': 10, 'est__min_samples_leaf': 1, 'est__max_depth': 7, 'est__criterion': 'entropy', 'est__bootstrap': True} -[04/18/17 12:09:39] INFO Model with rank: 2 -[04/18/17 12:09:39] INFO Mean validation score: 0.860 (std: 0.019) -[04/18/17 12:09:39] INFO Parameters: {'est__n_estimators': 201, 'est__min_samples_split': 10, 'est__min_samples_leaf': 3, 'est__max_depth': 10, 'est__criterion': 'gini', 'est__bootstrap': True} -[04/18/17 12:09:39] INFO Model with rank: 3 -[04/18/17 12:09:39] INFO Mean validation score: 0.859 (std: 0.022) -[04/18/17 12:09:39] INFO Parameters: {'est__n_estimators': 201, 'est__min_samples_split': 2, 'est__min_samples_leaf': 3, 'est__max_depth': 5, 'est__criterion': 'gini', 'est__bootstrap': True} -[04/18/17 12:09:39] INFO Algorithm: RF, Best Score: 0.8617, Best Parameters: {'est__n_estimators': 201, 'est__min_samples_split': 10, 'est__min_samples_leaf': 1, 'est__max_depth': 7, 'est__criterion': 'entropy', 'est__bootstrap': True} -[04/18/17 12:09:39] INFO Final Model Predictions for RF -[04/18/17 12:09:39] INFO Skipping Calibration -[04/18/17 12:09:39] INFO Making Predictions -[04/18/17 12:09:39] INFO Predictions Complete -[04/18/17 12:09:39] INFO Algorithm: XGB -[04/18/17 12:09:39] INFO Fitting Initial Model -[04/18/17 12:09:39] INFO No RFE Available for XGB -[04/18/17 12:09:39] INFO Randomized Grid Search -[04/18/17 12:10:03] INFO Grid Search took 23.32 seconds for 50 candidate parameter settings. -[04/18/17 12:10:03] INFO Model with rank: 1 -[04/18/17 12:10:03] INFO Mean validation score: 0.857 (std: 0.016) -[04/18/17 12:10:03] INFO Parameters: {'est__subsample': 0.5, 'est__n_estimators': 51, 'est__min_child_weight': 1.1, 'est__max_depth': 9, 'est__learning_rate': 0.02, 'est__colsample_bytree': 0.8} -[04/18/17 12:10:03] INFO Model with rank: 2 -[04/18/17 12:10:03] INFO Mean validation score: 0.857 (std: 0.017) -[04/18/17 12:10:03] INFO Parameters: {'est__subsample': 0.5, 'est__n_estimators': 21, 'est__min_child_weight': 1.1, 'est__max_depth': 10, 'est__learning_rate': 0.01, 'est__colsample_bytree': 1.0} -[04/18/17 12:10:03] INFO Model with rank: 3 -[04/18/17 12:10:03] INFO Mean validation score: 0.857 (std: 0.016) -[04/18/17 12:10:03] INFO Parameters: {'est__subsample': 0.8, 'est__n_estimators': 51, 'est__min_child_weight': 1.1, 'est__max_depth': 5, 'est__learning_rate': 0.05, 'est__colsample_bytree': 0.9} -[04/18/17 12:10:03] INFO Algorithm: XGB, Best Score: 0.8574, Best Parameters: {'est__subsample': 0.5, 'est__n_estimators': 51, 'est__min_child_weight': 1.1, 'est__max_depth': 9, 'est__learning_rate': 0.02, 'est__colsample_bytree': 0.8} -[04/18/17 12:10:03] INFO Final Model Predictions for XGB -[04/18/17 12:10:03] INFO Skipping Calibration -[04/18/17 12:10:03] INFO Making Predictions -[04/18/17 12:10:03] INFO Predictions Complete -[04/18/17 12:10:03] INFO Blending Models -[04/18/17 12:10:03] INFO Blending Start: 2017-04-18 12:10:03.066771 -[04/18/17 12:10:03] INFO Blending Complete: 0:00:00.008102 -[04/18/17 12:10:03] INFO ================================================================================ -[04/18/17 12:10:03] INFO Metrics for: Partition.train -[04/18/17 12:10:03] INFO -------------------------------------------------------------------------------- -[04/18/17 12:10:03] INFO Algorithm: RF -[04/18/17 12:10:03] INFO accuracy: 0.894500561167 -[04/18/17 12:10:03] INFO adjusted_rand_score: 0.61985360714 -[04/18/17 12:10:03] INFO average_precision: 0.937994687253 -[04/18/17 12:10:03] INFO confusion_matrix: [[525 24] - [ 70 272]] -[04/18/17 12:10:03] INFO explained_variance: 0.565195624154 -[04/18/17 12:10:03] INFO f1: 0.852664576803 -[04/18/17 12:10:03] INFO mean_absolute_error: 0.105499438833 -[04/18/17 12:10:03] INFO median_absolute_error: 0.0 -[04/18/17 12:10:03] INFO neg_log_loss: 0.304153797611 -[04/18/17 12:10:03] INFO neg_mean_squared_error: 0.105499438833 -[04/18/17 12:10:03] INFO precision: 0.918918918919 -[04/18/17 12:10:03] INFO r2: 0.553925798102 -[04/18/17 12:10:03] INFO recall: 0.795321637427 -[04/18/17 12:10:03] INFO roc_auc: 0.953855494839 -[04/18/17 12:10:03] INFO -------------------------------------------------------------------------------- -[04/18/17 12:10:03] INFO Algorithm: XGB -[04/18/17 12:10:03] INFO accuracy: 0.89898989899 -[04/18/17 12:10:03] INFO adjusted_rand_score: 0.634084281404 -[04/18/17 12:10:03] INFO average_precision: 0.933465422975 -[04/18/17 12:10:03] INFO confusion_matrix: [[529 20] - [ 70 272]] -[04/18/17 12:10:03] INFO explained_variance: 0.586222690911 -[04/18/17 12:10:03] INFO f1: 0.858044164038 -[04/18/17 12:10:03] INFO mean_absolute_error: 0.10101010101 -[04/18/17 12:10:03] INFO median_absolute_error: 0.0 -[04/18/17 12:10:03] INFO neg_log_loss: 0.413047928351 -[04/18/17 12:10:03] INFO neg_mean_squared_error: 0.10101010101 -[04/18/17 12:10:03] INFO precision: 0.931506849315 -[04/18/17 12:10:03] INFO r2: 0.572907679034 -[04/18/17 12:10:03] INFO recall: 0.795321637427 -[04/18/17 12:10:03] INFO roc_auc: 0.950199192578 -[04/18/17 12:10:03] INFO ================================================================================ -[04/18/17 12:10:03] INFO Metrics for: Partition.test -[04/18/17 12:10:03] INFO No labels for generating Partition.test metrics -[04/18/17 12:10:03] INFO ================================================================================ -[04/18/17 12:10:03] INFO Selecting Best Model -[04/18/17 12:10:03] INFO Scoring for: Partition.train -[04/18/17 12:10:03] INFO Best Model Selection Start: 2017-04-18 12:10:03.106245 -[04/18/17 12:10:03] INFO Scoring RF Model -[04/18/17 12:10:03] INFO Scoring XGB Model -[04/18/17 12:10:03] INFO Scoring BLEND Model -[04/18/17 12:10:03] INFO Best Model is BLEND with a roc_auc score of 0.9539 -[04/18/17 12:10:03] INFO Best Model Selection Complete: 0:00:00.001146 -[04/18/17 12:10:03] INFO ================================================================================ -[04/18/17 12:10:03] INFO Generating Plots for partition: train -[04/18/17 12:10:03] INFO Generating Calibration Plot -[04/18/17 12:10:03] INFO Calibration for Algorithm: RF -[04/18/17 12:10:03] INFO Calibration for Algorithm: XGB -[04/18/17 12:10:03] INFO Writing plot to /Users/markconway/Projects/Titanic/plots/calibration_train.png -[04/18/17 12:10:03] INFO Generating Confusion Matrices -[04/18/17 12:10:03] INFO Confusion Matrix for Algorithm: RF -[04/18/17 12:10:03] INFO Confusion Matrix: -[04/18/17 12:10:03] INFO [[525 24] - [ 70 272]] -[04/18/17 12:10:03] INFO Writing plot to /Users/markconway/Projects/Titanic/plots/confusion_train_RF.png -[04/18/17 12:10:03] INFO Confusion Matrix for Algorithm: XGB -[04/18/17 12:10:03] INFO Confusion Matrix: -[04/18/17 12:10:03] INFO [[529 20] - [ 70 272]] -[04/18/17 12:10:03] INFO Writing plot to /Users/markconway/Projects/Titanic/plots/confusion_train_XGB.png -[04/18/17 12:10:04] INFO Generating ROC Curves -[04/18/17 12:10:04] INFO ROC Curve for Algorithm: RF -[04/18/17 12:10:04] INFO ROC Curve for Algorithm: XGB -[04/18/17 12:10:04] INFO Writing plot to /Users/markconway/Projects/Titanic/plots/roc_curve_train.png -[04/18/17 12:10:04] INFO Generating Learning Curves -[04/18/17 12:10:04] INFO Algorithm Configuration -[04/18/17 12:10:04] INFO Learning Curve for Algorithm: RF -[04/18/17 12:10:06] INFO Writing plot to /Users/markconway/Projects/Titanic/plots/learning_curve_train_RF.png -[04/18/17 12:10:06] INFO Learning Curve for Algorithm: XGB -[04/18/17 12:10:06] INFO Writing plot to /Users/markconway/Projects/Titanic/plots/learning_curve_train_XGB.png -[04/18/17 12:10:06] INFO Generating Feature Importance Plots -[04/18/17 12:10:06] INFO Feature Importances for Algorithm: RF -[04/18/17 12:10:06] INFO Feature Ranking: -[04/18/17 12:10:06] INFO 1. Feature 2 (0.108046) -[04/18/17 12:10:06] INFO 2. Feature 36 (0.073566) -[04/18/17 12:10:06] INFO 3. Feature 39 (0.057581) -[04/18/17 12:10:06] INFO 4. Feature 3 (0.051578) -[04/18/17 12:10:06] INFO 5. Feature 7 (0.046824) -[04/18/17 12:10:06] INFO 6. Feature 42 (0.046571) -[04/18/17 12:10:06] INFO 7. Feature 24 (0.045251) -[04/18/17 12:10:06] INFO 8. Feature 23 (0.044804) -[04/18/17 12:10:06] INFO 9. Feature 22 (0.038629) -[04/18/17 12:10:06] INFO 10. Feature 21 (0.037361) -[04/18/17 12:10:06] INFO Writing plot to /Users/markconway/Projects/Titanic/plots/feature_importance_train_RF.png -[04/18/17 12:10:07] INFO Feature Importances for Algorithm: XGB -[04/18/17 12:10:07] INFO Feature Ranking: -[04/18/17 12:10:07] INFO 1. Feature 2 (0.138577) -[04/18/17 12:10:07] INFO 2. Feature 3 (0.119850) -[04/18/17 12:10:07] INFO 3. Feature 0 (0.116105) -[04/18/17 12:10:07] INFO 4. Feature 7 (0.108614) -[04/18/17 12:10:07] INFO 5. Feature 31 (0.104869) -[04/18/17 12:10:07] INFO 6. Feature 23 (0.074906) -[04/18/17 12:10:07] INFO 7. Feature 40 (0.067416) -[04/18/17 12:10:07] INFO 8. Feature 4 (0.033708) -[04/18/17 12:10:07] INFO 9. Feature 33 (0.029963) -[04/18/17 12:10:07] INFO 10. Feature 9 (0.029963) -[04/18/17 12:10:07] INFO Writing plot to /Users/markconway/Projects/Titanic/plots/feature_importance_train_XGB.png -[04/18/17 12:10:07] INFO ================================================================================ -[04/18/17 12:10:07] INFO Saving Model Predictor -[04/18/17 12:10:07] INFO Writing model predictor to /Users/markconway/Projects/Titanic/model/model_20170418.pkl -[04/18/17 12:10:07] INFO Saving Feature Map -[04/18/17 12:10:07] INFO Writing feature map to /Users/markconway/Projects/Titanic/model/feature_map_20170418.pkl -[04/18/17 12:10:07] INFO Loading data from /Users/markconway/Projects/Titanic/input/test.csv -[04/18/17 12:10:07] INFO Saving Predictions -[04/18/17 12:10:07] INFO Storing output to /Users/markconway/Projects/Titanic/output/predictions_20170418.csv -[04/18/17 12:10:07] INFO Saving Probabilities -[04/18/17 12:10:07] INFO Storing output to /Users/markconway/Projects/Titanic/output/probabilities_20170418.csv -[04/18/17 12:10:07] INFO Saving Ranked Predictions -[04/18/17 12:10:07] INFO Writing data frame to /Users/markconway/Projects/Titanic/output/rankings_20170418.csv -[04/18/17 12:10:07] INFO Saving Submission -[04/18/17 12:10:07] INFO ******************************************************************************** -[04/18/17 12:10:07] INFO AlphaPy End -[04/18/17 12:10:07] INFO ******************************************************************************** +[12/30/17 23:17:50] INFO Feature Count : 10 +[12/30/17 23:17:50] INFO Creating Count Features +[12/30/17 23:17:50] INFO NA Counts +[12/30/17 23:17:50] INFO Number Counts +[12/30/17 23:17:50] INFO New Feature Count : 21 +[12/30/17 23:17:50] INFO Creating Base Features +[12/30/17 23:17:50] INFO Feature 1: Pclass is a numerical feature of type int64 with 3 unique values +[12/30/17 23:17:50] INFO Feature 2: Name is a text feature [12:82] with 1307 unique values +[12/30/17 23:17:50] INFO Feature 2: Name => Factorization +[12/30/17 23:17:50] INFO Feature 3: Sex is a text feature [4:6] with 2 unique values +[12/30/17 23:17:50] INFO Feature 3: Sex => Factorization +[12/30/17 23:17:50] INFO Feature 4: Age is a numerical feature of type float64 with 99 unique values +[12/30/17 23:17:50] INFO Feature 5: SibSp is a numerical feature of type int64 with 7 unique values +[12/30/17 23:17:50] INFO Feature 6: Parch is a numerical feature of type int64 with 8 unique values +[12/30/17 23:17:50] INFO Feature 7: Ticket is a text feature [3:18] with 929 unique values +[12/30/17 23:17:50] INFO Feature 7: Ticket => Factorization +[12/30/17 23:17:50] INFO Feature 8: Fare is a numerical feature of type float64 with 282 unique values +[12/30/17 23:17:50] INFO Feature 9: Cabin is a text feature [1:15] with 187 unique values +[12/30/17 23:17:50] INFO Feature 9: Cabin => Factorization +[12/30/17 23:17:50] INFO Feature 10: Embarked is a text feature [1:1] with 4 unique values +[12/30/17 23:17:50] INFO Feature 10: Embarked => Factorization +[12/30/17 23:17:50] INFO Feature 11: nan_count is a numerical feature of type int64 with 3 unique values +[12/30/17 23:17:50] INFO Feature 12: count_0 is a numerical feature of type int64 with 4 unique values +[12/30/17 23:17:50] INFO Feature 13: count_1 is a numerical feature of type int64 with 4 unique values +[12/30/17 23:17:50] INFO Feature 14: count_2 is a numerical feature of type int64 with 4 unique values +[12/30/17 23:17:50] INFO Feature 15: count_3 is a numerical feature of type int64 with 4 unique values +[12/30/17 23:17:50] INFO Feature 16: count_4 is a numerical feature of type int64 with 3 unique values +[12/30/17 23:17:50] INFO Feature 17: count_5 is a numerical feature of type int64 with 2 unique values +[12/30/17 23:17:50] INFO Feature 18: count_6 is a numerical feature of type int64 with 2 unique values +[12/30/17 23:17:50] INFO Feature 19: count_7 is a numerical feature of type int64 with 2 unique values +[12/30/17 23:17:50] INFO Feature 20: count_8 is a numerical feature of type int64 with 3 unique values +[12/30/17 23:17:50] INFO Feature 21: count_9 is a numerical feature of type int64 with 3 unique values +[12/30/17 23:17:50] INFO New Feature Count : 21 +[12/30/17 23:17:50] INFO Scaling Base Features +[12/30/17 23:17:50] INFO Creating NumPy Features +[12/30/17 23:17:50] INFO NumPy Feature: sum +[12/30/17 23:17:50] INFO NumPy Feature: mean +[12/30/17 23:17:50] INFO NumPy Feature: standard deviation +[12/30/17 23:17:50] INFO NumPy Feature: variance +[12/30/17 23:17:50] INFO NumPy Feature Count : 4 +[12/30/17 23:17:50] INFO New Feature Count : 25 +[12/30/17 23:17:50] INFO Creating Clustering Features +[12/30/17 23:17:50] INFO Cluster Minimum : 3 +[12/30/17 23:17:50] INFO Cluster Maximum : 30 +[12/30/17 23:17:50] INFO Cluster Increment : 3 +[12/30/17 23:17:50] INFO k = 3 +[12/30/17 23:17:50] INFO k = 6 +[12/30/17 23:17:50] INFO k = 9 +[12/30/17 23:17:50] INFO k = 12 +[12/30/17 23:17:50] INFO k = 15 +[12/30/17 23:17:50] INFO k = 18 +[12/30/17 23:17:50] INFO k = 21 +[12/30/17 23:17:50] INFO k = 24 +[12/30/17 23:17:50] INFO k = 27 +[12/30/17 23:17:51] INFO k = 30 +[12/30/17 23:17:51] INFO Clustering Feature Count : 10 +[12/30/17 23:17:51] INFO New Feature Count : 35 +[12/30/17 23:17:51] INFO Saving New Features in Model +[12/30/17 23:17:51] INFO Creating Interactions +[12/30/17 23:17:51] INFO Initial Feature Count : 35 +[12/30/17 23:17:51] INFO Generating Polynomial Features +[12/30/17 23:17:51] INFO Interaction Percentage : 10 +[12/30/17 23:17:51] INFO Polynomial Degree : 5 +[12/30/17 23:17:51] INFO Polynomial Feature Count : 15 +[12/30/17 23:17:51] INFO New Total Feature Count : 50 +[12/30/17 23:17:51] INFO Saving New Features in Model +[12/30/17 23:17:51] INFO Removing Low-Variance Features +[12/30/17 23:17:51] INFO Low-Variance Threshold : 0.10 +[12/30/17 23:17:51] INFO Original Feature Count : 50 +[12/30/17 23:17:51] INFO Reduced Feature Count : 50 +[12/30/17 23:17:51] INFO Saving New Features in Model +[12/30/17 23:17:51] INFO Skipping Shuffling +[12/30/17 23:17:51] INFO Skipping Sampling +[12/30/17 23:17:51] INFO Getting Class Weights +[12/30/17 23:17:51] INFO Class Weight for target Survived [1]: 1.605263 +[12/30/17 23:17:51] INFO Getting All Estimators +[12/30/17 23:17:51] INFO Algorithm Configuration +[12/30/17 23:17:51] INFO Selecting Models +[12/30/17 23:17:51] INFO Algorithm: RF +[12/30/17 23:17:51] INFO Fitting Initial Model +[12/30/17 23:17:51] INFO Recursive Feature Elimination with CV +[12/30/17 23:18:14] INFO RFECV took 22.72 seconds for step 3 and 3 folds +[12/30/17 23:18:14] INFO Algorithm: RF, Selected Features: 20, Ranking: [ 2 1 1 1 5 9 1 1 2 6 8 7 8 6 7 10 11 11 11 10 10 1 1 1 1 + 9 6 9 5 1 5 4 2 4 1 1 1 3 7 1 1 8 1 1 4 1 1 3 3 1] +[12/30/17 23:18:14] INFO Randomized Grid Search +[12/30/17 23:19:08] INFO Grid Search took 54.03 seconds for 50 candidate parameter settings. +[12/30/17 23:19:08] INFO Model with rank: 1 +[12/30/17 23:19:08] INFO Mean validation score: 0.863 (std: 0.014) +[12/30/17 23:19:08] INFO Parameters: {'est__n_estimators': 501, 'est__min_samples_split': 5, 'est__min_samples_leaf': 3, 'est__max_depth': 7, 'est__criterion': 'entropy', 'est__bootstrap': True} +[12/30/17 23:19:08] INFO Model with rank: 2 +[12/30/17 23:19:08] INFO Mean validation score: 0.862 (std: 0.015) +[12/30/17 23:19:08] INFO Parameters: {'est__n_estimators': 201, 'est__min_samples_split': 10, 'est__min_samples_leaf': 2, 'est__max_depth': 7, 'est__criterion': 'entropy', 'est__bootstrap': True} +[12/30/17 23:19:08] INFO Model with rank: 3 +[12/30/17 23:19:08] INFO Mean validation score: 0.861 (std: 0.014) +[12/30/17 23:19:08] INFO Parameters: {'est__n_estimators': 101, 'est__min_samples_split': 2, 'est__min_samples_leaf': 3, 'est__max_depth': 7, 'est__criterion': 'entropy', 'est__bootstrap': True} +[12/30/17 23:19:08] INFO Algorithm: RF, Best Score: 0.8627, Best Parameters: {'est__n_estimators': 501, 'est__min_samples_split': 5, 'est__min_samples_leaf': 3, 'est__max_depth': 7, 'est__criterion': 'entropy', 'est__bootstrap': True} +[12/30/17 23:19:08] INFO Final Model Predictions for RF +[12/30/17 23:19:08] INFO Skipping Calibration +[12/30/17 23:19:08] INFO Making Predictions +[12/30/17 23:19:09] INFO Predictions Complete +[12/30/17 23:19:09] INFO Algorithm: XGB +[12/30/17 23:19:09] INFO Fitting Initial Model +[12/30/17 23:19:09] INFO No RFE Available for XGB +[12/30/17 23:19:09] INFO Randomized Grid Search +[12/30/17 23:19:32] INFO Grid Search took 23.44 seconds for 50 candidate parameter settings. +[12/30/17 23:19:32] INFO Model with rank: 1 +[12/30/17 23:19:32] INFO Mean validation score: 0.863 (std: 0.020) +[12/30/17 23:19:32] INFO Parameters: {'est__subsample': 0.6, 'est__n_estimators': 21, 'est__min_child_weight': 1.1, 'est__max_depth': 12, 'est__learning_rate': 0.1, 'est__colsample_bytree': 0.7} +[12/30/17 23:19:32] INFO Model with rank: 2 +[12/30/17 23:19:32] INFO Mean validation score: 0.856 (std: 0.014) +[12/30/17 23:19:32] INFO Parameters: {'est__subsample': 0.5, 'est__n_estimators': 51, 'est__min_child_weight': 1.0, 'est__max_depth': 8, 'est__learning_rate': 0.01, 'est__colsample_bytree': 0.7} +[12/30/17 23:19:32] INFO Model with rank: 3 +[12/30/17 23:19:32] INFO Mean validation score: 0.855 (std: 0.023) +[12/30/17 23:19:32] INFO Parameters: {'est__subsample': 0.5, 'est__n_estimators': 21, 'est__min_child_weight': 1.0, 'est__max_depth': 7, 'est__learning_rate': 0.05, 'est__colsample_bytree': 0.6} +[12/30/17 23:19:32] INFO Algorithm: XGB, Best Score: 0.8627, Best Parameters: {'est__subsample': 0.6, 'est__n_estimators': 21, 'est__min_child_weight': 1.1, 'est__max_depth': 12, 'est__learning_rate': 0.1, 'est__colsample_bytree': 0.7} +[12/30/17 23:19:32] INFO Final Model Predictions for XGB +[12/30/17 23:19:32] INFO Skipping Calibration +[12/30/17 23:19:32] INFO Making Predictions +[12/30/17 23:19:32] INFO Predictions Complete +[12/30/17 23:19:32] INFO Blending Models +[12/30/17 23:19:32] INFO Blending Start: 2017-12-30 23:19:32.734086 +[12/30/17 23:19:32] INFO Blending Complete: 0:00:00.010781 +[12/30/17 23:19:32] INFO ================================================================================ +[12/30/17 23:19:32] INFO Metrics for: Partition.train +[12/30/17 23:19:32] INFO -------------------------------------------------------------------------------- +[12/30/17 23:19:32] INFO Algorithm: RF +[12/30/17 23:19:32] INFO accuracy: 0.895622895623 +[12/30/17 23:19:32] INFO adjusted_rand_score: 0.623145355109 +[12/30/17 23:19:32] INFO average_precision: 0.939127507197 +[12/30/17 23:19:32] INFO confusion_matrix: [[530 19] + [ 74 268]] +[12/30/17 23:19:32] INFO explained_variance: 0.574782432706 +[12/30/17 23:19:32] INFO f1: 0.852146263911 +[12/30/17 23:19:32] INFO mean_absolute_error: 0.104377104377 +[12/30/17 23:19:32] INFO median_absolute_error: 0.0 +[12/30/17 23:19:32] INFO neg_log_loss: 0.299193724458 +[12/30/17 23:19:32] INFO neg_mean_squared_error: 0.104377104377 +[12/30/17 23:19:32] INFO precision: 0.933797909408 +[12/30/17 23:19:32] INFO r2: 0.558671268335 +[12/30/17 23:19:32] INFO recall: 0.783625730994 +[12/30/17 23:19:32] INFO roc_auc: 0.954665047561 +[12/30/17 23:19:32] INFO -------------------------------------------------------------------------------- +[12/30/17 23:19:32] INFO Algorithm: XGB +[12/30/17 23:19:32] INFO accuracy: 0.915824915825 +[12/30/17 23:19:32] INFO adjusted_rand_score: 0.689583531462 +[12/30/17 23:19:32] INFO average_precision: 0.958117084885 +[12/30/17 23:19:32] INFO confusion_matrix: [[532 17] + [ 58 284]] +[12/30/17 23:19:32] INFO explained_variance: 0.653042746514 +[12/30/17 23:19:32] INFO f1: 0.883359253499 +[12/30/17 23:19:32] INFO mean_absolute_error: 0.0841750841751 +[12/30/17 23:19:32] INFO median_absolute_error: 0.0 +[12/30/17 23:19:32] INFO neg_log_loss: 0.295991903662 +[12/30/17 23:19:32] INFO neg_mean_squared_error: 0.0841750841751 +[12/30/17 23:19:32] INFO precision: 0.943521594684 +[12/30/17 23:19:32] INFO r2: 0.644089732528 +[12/30/17 23:19:32] INFO recall: 0.830409356725 +[12/30/17 23:19:32] INFO roc_auc: 0.971127728246 +[12/30/17 23:19:32] INFO ================================================================================ +[12/30/17 23:19:32] INFO Metrics for: Partition.test +[12/30/17 23:19:32] INFO No labels for generating Partition.test metrics +[12/30/17 23:19:32] INFO ================================================================================ +[12/30/17 23:19:32] INFO Selecting Best Model +[12/30/17 23:19:32] INFO Scoring for: Partition.train +[12/30/17 23:19:32] INFO Best Model Selection Start: 2017-12-30 23:19:32.779281 +[12/30/17 23:19:32] INFO Scoring RF Model +[12/30/17 23:19:32] INFO Scoring XGB Model +[12/30/17 23:19:32] INFO Scoring BLEND Model +[12/30/17 23:19:32] INFO Best Model is XGB with a roc_auc score of 0.9711 +[12/30/17 23:19:32] INFO Best Model Selection Complete: 0:00:00.000801 +[12/30/17 23:19:32] INFO ================================================================================ +[12/30/17 23:19:32] INFO Generating Plots for partition: train +[12/30/17 23:19:32] INFO Generating Calibration Plot +[12/30/17 23:19:32] INFO Calibration for Algorithm: RF +[12/30/17 23:19:32] INFO Calibration for Algorithm: XGB +[12/30/17 23:19:32] INFO Writing plot to ./plots/calibration_train.png +[12/30/17 23:19:33] INFO Generating Confusion Matrices +[12/30/17 23:19:33] INFO Confusion Matrix for Algorithm: RF +[12/30/17 23:19:33] INFO Confusion Matrix: +[12/30/17 23:19:33] INFO [[530 19] + [ 74 268]] +[12/30/17 23:19:33] INFO Writing plot to ./plots/confusion_train_RF.png +[12/30/17 23:19:33] INFO Confusion Matrix for Algorithm: XGB +[12/30/17 23:19:33] INFO Confusion Matrix: +[12/30/17 23:19:33] INFO [[532 17] + [ 58 284]] +[12/30/17 23:19:33] INFO Writing plot to ./plots/confusion_train_XGB.png +[12/30/17 23:19:33] INFO Generating ROC Curves +[12/30/17 23:19:33] INFO ROC Curve for Algorithm: RF +[12/30/17 23:19:33] INFO ROC Curve for Algorithm: XGB +[12/30/17 23:19:33] INFO Writing plot to ./plots/roc_curve_train.png +[12/30/17 23:19:33] INFO Generating Learning Curves +[12/30/17 23:19:33] INFO Algorithm Configuration +[12/30/17 23:19:33] INFO Learning Curve for Algorithm: RF +[12/30/17 23:19:35] INFO Writing plot to ./plots/learning_curve_train_RF.png +[12/30/17 23:19:35] INFO Learning Curve for Algorithm: XGB +[12/30/17 23:19:36] INFO Writing plot to ./plots/learning_curve_train_XGB.png +[12/30/17 23:19:36] INFO Generating Feature Importance Plots +[12/30/17 23:19:36] INFO Feature Importances for Algorithm: RF +[12/30/17 23:19:36] INFO Feature Ranking: +[12/30/17 23:19:36] INFO 1. Feature 2 (0.106345) +[12/30/17 23:19:36] INFO 2. Feature 36 (0.074083) +[12/30/17 23:19:36] INFO 3. Feature 39 (0.055330) +[12/30/17 23:19:36] INFO 4. Feature 3 (0.050339) +[12/30/17 23:19:36] INFO 5. Feature 7 (0.049228) +[12/30/17 23:19:36] INFO 6. Feature 23 (0.044868) +[12/30/17 23:19:36] INFO 7. Feature 22 (0.042925) +[12/30/17 23:19:36] INFO 8. Feature 42 (0.042850) +[12/30/17 23:19:36] INFO 9. Feature 21 (0.039954) +[12/30/17 23:19:36] INFO 10. Feature 24 (0.038563) +[12/30/17 23:19:36] INFO Writing plot to ./plots/feature_importance_train_RF.png +[12/30/17 23:19:36] INFO Feature Importances for Algorithm: XGB +[12/30/17 23:19:36] INFO Feature Ranking: +[12/30/17 23:19:36] INFO 1. Feature 2 (0.142857) +[12/30/17 23:19:36] INFO 2. Feature 3 (0.120301) +[12/30/17 23:19:36] INFO 3. Feature 0 (0.116541) +[12/30/17 23:19:36] INFO 4. Feature 7 (0.109023) +[12/30/17 23:19:36] INFO 5. Feature 31 (0.105263) +[12/30/17 23:19:36] INFO 6. Feature 23 (0.075188) +[12/30/17 23:19:36] INFO 7. Feature 40 (0.071429) +[12/30/17 23:19:36] INFO 8. Feature 8 (0.033835) +[12/30/17 23:19:36] INFO 9. Feature 4 (0.030075) +[12/30/17 23:19:36] INFO 10. Feature 9 (0.030075) +[12/30/17 23:19:36] INFO Writing plot to ./plots/feature_importance_train_XGB.png +[12/30/17 23:19:36] INFO ================================================================================ +[12/30/17 23:19:36] INFO Saving Model Predictor +[12/30/17 23:19:36] INFO Writing model predictor to ./model/model_20171230.pkl +[12/30/17 23:19:36] INFO Saving Feature Map +[12/30/17 23:19:36] INFO Writing feature map to ./model/feature_map_20171230.pkl +[12/30/17 23:19:36] INFO Loading data from ./input/test.csv +[12/30/17 23:19:36] INFO Saving Predictions +[12/30/17 23:19:36] INFO Storing output to ./output/predictions_20171230.csv +[12/30/17 23:19:36] INFO Saving Probabilities +[12/30/17 23:19:36] INFO Storing output to ./output/probabilities_20171230.csv +[12/30/17 23:19:36] INFO Saving Ranked Predictions +[12/30/17 23:19:36] INFO Writing data frame to ./output/rankings_20171230.csv +[12/30/17 23:19:36] INFO Saving Submission to ./output/submission_20171230.csv +[12/30/17 23:19:36] INFO ******************************************************************************** +[12/30/17 23:19:36] INFO AlphaPy End +[12/30/17 23:19:36] INFO ******************************************************************************** diff --git a/setup.py b/setup.py index 94ef85a..979d183 100644 --- a/setup.py +++ b/setup.py @@ -9,8 +9,8 @@ MAINTAINER = 'ScottFree LLC [Robert D. Scott II, Mark Conway]' MAINTAINER_EMAIL = 'scottfree.analytics@scottfreellc.com' URL = "https://github.com/ScottFreeLLC/AlphaPy" -LICENSE = "Apache License, Version 2.1" -VERSION = "2.1" +LICENSE = "Apache License, Version 2.2" +VERSION = "2.2" classifiers = ['Development Status :: 4 - Beta', 'Programming Language :: Python',