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"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import os\n",
"import datetime\n",
"import pytz\n",
"import numpy as np\n",
"from utils.ipynb_helpers import read_data, write_df, convert_tz\n",
"\n",
"\n",
"# Location to open raw data from data providers\n",
"DATA_RAW = \"data/raw\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Read Data From All-Data CSV (Multi Index Columns)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"stock=True\n",
"df_all = read_data(os.path.join(DATA_RAW, \"realdata_pol_1h.csv\"), stock=stock)\n",
"# df_all = read_data(os.path.join(DATA_RAW, \"other/PEMSBAY.csv\"), stock=stock)\n",
"\n",
"df_all = df_all[df_all.columns[:-12]]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Filtering & Processing the Master Dataset"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def percentage_nans(data, sort=True):\n",
" percent_missing = data.isnull().sum() * 100 / len(data)\n",
" missing_value_df = pd.DataFrame(\n",
" {\"percent_missing\": percent_missing} #'column_name': data.columns,\n",
" )\n",
" if sort:\n",
" missing_value_df.sort_values(\"percent_missing\", inplace=True)\n",
" return missing_value_df\n",
"\n",
"\n",
"def filter_percentage_nans(data, thresh=0.1):\n",
" thresh *= 100\n",
" per_nans = percentage_nans(data, sort=False)\n",
" return data.loc[:, per_nans[per_nans[\"percent_missing\"] < thresh].index]\n",
"\n",
"\n",
"def filter_intra_ticker(data, cols=[\"close\"]):\n",
" if cols is None:\n",
" return data\n",
" return data.iloc[\n",
" :, data.columns.get_level_values(1).isin(cols)\n",
" ] # data.xs(\"close\",level=1, axis=1)\n",
"\n",
"\n",
"def no_premarket_after_hours(data):\n",
" mkt_start = datetime.time(hour=9, minute=30, tzinfo=pytz.timezone(\"US/Eastern\"))\n",
" mkt_end = datetime.time(hour=15, minute=59, tzinfo=pytz.timezone(\"US/Eastern\"))\n",
" data = convert_tz(data, time_zone=\"US/Eastern\")\n",
" data = data.between_time(mkt_start, mkt_end)\n",
" data = convert_tz(data, time_zone=\"UTC\")\n",
" return data\n",
"\n",
"\n",
"def add_technical(data):\n",
" for ticker in data.columns.get_level_values(0).unique():\n",
" # Assumption: close/open values are positive and a zero value means that datapoint is missing so we say no change\n",
" data[ticker, \"pctchange\"] = (\n",
" data[ticker, \"close\"] / data[ticker, \"open\"] - 1\n",
" ).fillna(0.0).replace([np.inf, -np.inf, -1], 0.0)\n",
" data[ticker, \"logpctchange\"] = np.log(\n",
" data[ticker, \"close\"] / data[ticker, \"open\"]\n",
" ).fillna(0.0).replace([np.inf, -np.inf], 0.0)\n",
"\n",
"\n",
" # data[ticker, \"pctchange-1\"] = data[ticker, \"pctchange\"].shift(1,fill_value=0.0)\n",
" # data[ticker, \"pctchange-2\"] = data[ticker, \"pctchange\"].shift(2,fill_value=0.0)\n",
"\n",
" data[ticker, \"shortsma\"] = (\n",
" data[ticker, \"close\"].rolling(5).mean().fillna(data[ticker, \"close\"])\n",
" )\n",
" # data[ticker,'shortma-1'] = data[ticker,'shortsma'].shift(1)\n",
" # data[ticker,'shortma-2'] = data[ticker,'shortsma'].shift(2)\n",
" # print(data.columns.sort_values())\n",
" data = data.reindex(sorted(data.columns), axis=1)\n",
" # data.reindex(columns=data.columns.sort_values().get_level_values(0).unique(), level=0)\n",
" return data\n",
"\n",
"if stock:\n",
" # Filter df_all to normal hours\n",
" df_all = no_premarket_after_hours(df_all)\n",
"\n",
"percentage_nans(df_all).tail(40)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df = filter_percentage_nans(df_all, 0.08) #0.40\n",
"print(df.columns.get_level_values(0).unique())\n",
"df.columns"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Add & filter columns\n",
"df = add_technical(df)\n",
"\n",
"# None\n",
"# [\"close\"]\n",
"# [\"pctchange\"]\n",
"# [\"open\", \"high\", \"low\", \"close\", \"volume\", 'pctchange', \"shortsma\"]\n",
"df = filter_intra_ticker(\n",
" df, cols=[\"open\", \"close\", \"pctchange\", \"logpctchange\", \"shortsma\"]\n",
")\n",
"\n",
"df.head(20)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"df_t = df[\"WTI\", \"pctchange\"]\n",
"start_date = \"2022-10-01\"\n",
"end_date = \"2022-11-01\"\n",
"f1 = df_t[df.index > start_date]\n",
"f2 = f1[f1.index < end_date]\n",
"print(f2)\n",
"# f = plt.figure()\n",
"# f.set_figwidth(60)\n",
"# f.set_figheight(20)\n",
"plt.figure(figsize=(24,4))\n",
"plt.plot(np.arange(f2.index.to_numpy().shape[0]), 3.3* np.cumprod(f2.to_numpy()+1))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Fill NaNs"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def ffill_nans(data):\n",
" data = data.fillna(method=\"ffill\")\n",
" data = data.dropna()\n",
" return data\n",
"\n",
"\n",
"def del_nans_ffill(data, thresh):\n",
" data = data.dropna(thresh=thresh)\n",
" data = ffill_nans(data)\n",
" return data"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df = ffill_nans(df)\n",
"df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Clip Outliers"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def clip_outliers(data, p=0.005):\n",
" lower = data.quantile(p)\n",
" upper = data.quantile(1 - p)\n",
"\n",
" return data.clip(lower=lower, upper=upper, axis=1)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"if stock:\n",
" df = clip_outliers(df)\n",
"\n",
"df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Save Data"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Sometimes it errors bc the path doesn't exist but just run it again\n",
"write_df(df, \"data/stock/material_1h.csv\")\n",
"# write_df(df, \"data/other/PEMSBAY.csv\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Extras"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Read data and convert to percent delta"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# df_new = read_data(\"data/stock/close_1h.csv\")\n",
"\n",
"# print(\"Before:\\n\", df_new.head())\n",
"# df_new = df_new.pct_change()\n",
"# df_new.iloc[0] = 0\n",
"\n",
"# print(\"After:\\n\",df_new.head())\n",
"# write_df(df_new, \"data/stock/close_1h_pct_change.csv\")"
]
}
],
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