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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from utils.db import dbconn\n",
"conn = dbconn('dawndb')\n",
"import pandas as pd\n",
"pd.options.display.float_format = \"{:,.2f}\".format\n",
"df_rates = pd.read_sql_query(\"SELECT date, rate FROM rates where name='FED_FUND'\",\n",
" conn,\n",
" parse_dates=['date'],\n",
" index_col=['date']).sort_index()\n",
"df_balances = pd.read_sql_query(\"SELECT * FROM strategy_im\",\n",
" conn,\n",
" parse_dates=['date'],\n",
" index_col=['date']).sort_index()\n",
"df_balances[['broker', 'strategy']] = df_balances[['broker', 'strategy']].astype('category')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from IPython.display import display\n",
"from pandas.tseries.offsets import BDay\n",
"import numpy as np\n",
"\n",
"def f(df_balances, df_rates, broker, start_date, end_date):\n",
" df = (df_balances[df_balances.broker == broker].\n",
" set_index(\"strategy\", append=True)[\"amount\"].\n",
" unstack(\"strategy\"))\n",
" df[df.isnull()] = 0.\n",
" drange = pd.date_range(pd.Timestamp(start_date) - BDay(), end_date)\n",
" rates = df_rates.reindex(drange, method=\"ffill\") /100 /360\n",
" df = df.reindex(drange, method=\"ffill\")\n",
" if broker in [\"BAML_ISDA\", \"CITI\"]:\n",
" d = {}\n",
" for strat in df:\n",
" s = df.loc[start_date:, strat]\n",
" ir_bal = 0.\n",
" for bal, r in zip(s.values, rates.loc[start_date:, 'rate'].values):\n",
" bal += ir_bal\n",
" ir_bal += bal * r\n",
" d[strat] = ir_bal\n",
" result = pd.Series(d, name='amount')\n",
" else:\n",
" result = (df.loc[start_date:] * rates.loc[start_date:].values).sum().to_frame(name='amount')\n",
" display(result)\n",
" print(result.sum())\n",
" \n",
"from functools import partial\n",
"f_print = partial(f, df_balances, df_rates)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from ipywidgets import widgets, Layout\n",
"import datetime\n",
"broker_widget = widgets.Dropdown(\n",
" options=df_balances.broker.cat.categories,\n",
" value='CITI',\n",
" description='Broker:',\n",
" disabled=False,\n",
")\n",
"start_date = widgets.DatePicker(\n",
" description='start:',\n",
" disabled=False,\n",
" value=datetime.date(2019, 9, 1)\n",
")\n",
"end_date = widgets.DatePicker(\n",
" description='end:',\n",
" disabled=False,\n",
" value=datetime.date(2019, 9, 30)\n",
")\n",
"output = widgets.interactive_output(f_print, {'broker': broker_widget, 'start_date': start_date, 'end_date': end_date})\n",
"output.layout= Layout(margin='auto auto auto 90px')\n",
"widgets.VBox([widgets.HBox([broker_widget, start_date, end_date]), output])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df_balances[df_balances.broker=='BAML_ISDA'].loc[\"2019-06-10\"]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df_baml=df_balances[df_balances.broker == \"BAML_ISDA\"]\n",
"df_baml.groupby(df_baml.index)['amount'].sum()[\"2019-08-30\":]"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.4"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
|
