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- "
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+ "
\n",
""
],
"text/plain": [
- ":NdOverlay [ticker]\n",
- " :Curve [time] (close)"
+ ":Bars [period,Variable] (value)"
]
},
- "execution_count": 4,
+ "execution_count": 20,
"metadata": {
"application/vnd.holoviews_exec.v0+json": {
- "id": "p1002"
+ "id": "p1149"
}
},
"output_type": "execute_result"
}
],
"source": [
- "price_df.hvplot.line(x='time', y='close', by='ticker', width=1000, height=400)"
+ "agg_b = processing.aggregate_analytic_df_by_period(analytic_b, 'D')\n",
+ "agg_p = processing.aggregate_analytic_df_by_period(analytic_p, 'D')\n",
+ "bhb_result = processing.calculate_periodic_BHB(agg_b, agg_p)\n",
+ "agg_bhb_result = processing.aggregate_bhb_df(bhb_result)\n",
+ "agg_bhb_result.hvplot.bar(\n",
+ " y=['active_return','allocation',\n",
+ " 'interaction','selection',\n",
+ " 'notional_active_return'],rot=45,legend='top')"
]
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": 131,
"metadata": {},
"outputs": [],
"source": [
- "def _uniformize_time_series(profile_df):\n",
- " '''\n",
- " a helper function to create analytic_df \n",
- " \n",
- " make each entry in the time series has the same dimension\n",
- " by filling none holding stock that was held in previous period has 0 shares and 0 ini_w\n",
- "\n",
- " Parameters\n",
- " ----------\n",
- " profile_df : dataframe\n",
- " portfolio profile dataframe or benchmark profile dataframe\n",
- " \n",
- " Returns\n",
- " -------\n",
- " dataframe\n",
- " dataframe with uniformized time series\n",
- " '''\n",
- " # Get unique time periods\n",
- " time_periods = profile_df['time'].unique()\n",
- " time_periods = sorted(time_periods)\n",
- "\n",
- " # Iterate through time periods\n",
- " for i in range(len(time_periods) - 1):\n",
- " current_period = time_periods[i]\n",
- " next_period = time_periods[i + 1]\n",
- " \n",
- " current_df = profile_df[profile_df['time'] == current_period]\n",
- " next_df = profile_df[profile_df['time'] == next_period]\n",
- " \n",
- " tickers_current = current_df['ticker']\n",
- " tickers_next = next_df['ticker']\n",
- " \n",
- " # row that has ticker not in tickers_next\n",
- " missing_tickers = current_df[~tickers_current.isin(tickers_next)].copy()\n",
- " \n",
- " if len(missing_tickers) != 0:\n",
- " missing_tickers.time = next_period\n",
- " missing_tickers.shares = 0\n",
- " missing_tickers.ini_w = 0\n",
- " profile_df = pd.concat([profile_df, missing_tickers], ignore_index=True)\n",
- " # reset index\n",
- " return profile_df.reset_index(drop=True)\n"
+ "agg_bhb_result['period_str'] = agg_bhb_result.index.map(lambda x: str(x))"
]
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 132,
"metadata": {},
- "outputs": [],
- "source": [
- "# create a merged_df \n",
- "def create_analytic_df(price_df, profile_df):\n",
- " '''\n",
- " create a df for analysis processing\n",
- "\n",
- "\n",
- " '''\n",
- " uni_profile_df = _uniformize_time_series(profile_df)\n",
- " #TODO handle rename column here\n",
- " df = price_df.merge(uni_profile_df, on=['ticker','time'], how='outer')\n",
- " df.sort_values(by=['ticker','time'], inplace=True)\n",
- " # add sector, aggregate_sector, display_name and name to missing rows\n",
- " grouped = df.groupby('ticker')\n",
- " df['sector'] = grouped['sector'].fillna(method='ffill')\n",
- " df['aggregate_sector'] = grouped['aggregate_sector'].fillna(method='ffill')\n",
- " df['display_name'] = grouped['display_name'].fillna(method='ffill')\n",
- " df['name'] = grouped['name'].fillna(method='ffill')\n",
- "\n",
- " # assign missing ini_w\n",
- " df['ini_w'] = grouped['ini_w'].fillna(method='ffill')\n",
- " # assign missing shares\n",
- " df['shares'] = grouped['shares'].fillna(method='ffill')\n",
- " # remove profile and price entry before first profile entry from df\n",
- " df.dropna(subset=['ini_w'], inplace=True)\n",
- " df.dropna(subset=['close'], inplace=True)\n",
- " # remove where weight is 0\n",
- " df = df[df['shares'] != 0].copy()\n",
- " return df\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 14,
- "metadata": {},
- "outputs": [],
- "source": [
- "def calculate_weight_using_cash(df):\n",
- " '''\n",
- " patch df with current weight for each entry\n",
- " use cash to calculate weight\n",
- " \n",
- " Parameters\n",
- " ----------\n",
- " df : dataframe\n",
- " dataframe with processed cash column\n",
- " \n",
- " '''\n",
- " df['cur_w'] = float('nan')\n",
- " grouped = df.groupby('time')\n",
- " df.cur_w = grouped.cash.transform(lambda x: x / x.sum())\n",
- "\n",
- " "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 15,
- "metadata": {},
- "outputs": [],
- "source": [
- "def calculate_cash(df):\n",
- " '''\n",
- " patch df with cash column\n",
- " cash = shares * close\n",
- " \n",
- " Parameters\n",
- " ----------\n",
- " df : dataframe\n",
- " dataframe with processed shares and close column\n",
- " '''\n",
- " df['cash'] = df['shares'] * df['close']"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
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+ " interaction | \n",
+ " selection | \n",
+ " notional_active_return | \n",
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+ "text/plain": [
+ " active_return allocation interaction selection \\\n",
+ "period \n",
+ "2023-08-08/2023-08-14 0.000000 0.000000 0.000000 0.00000 \n",
+ "2023-08-15/2023-08-21 0.043550 0.043253 -0.028373 0.02867 \n",
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+ " notional_active_return period_str \n",
+ "period \n",
+ "2023-08-08/2023-08-14 0.000000 2023-08-08/2023-08-14 \n",
+ "2023-08-15/2023-08-21 0.043550 2023-08-15/2023-08-21 \n",
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+ "2023-08-29/2023-09-04 0.000000 2023-08-29/2023-09-04 "
+ ]
+ },
+ "execution_count": 132,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
- "def calculate_return(df, start, end):\n",
- " '''\n",
- " calculate cumulative return for each entry in the df\n",
- " '''\n",
- " selected_df = df[df.time.between(start, end)].copy()\n",
- " if len(selected_df) == 0:\n",
- " return selected_df\n",
- " selected_df.sort_values(by=['time'], inplace=True)\n",
- " selected_df['return'] = selected_df.groupby('ticker')"
+ "agg_bhb_result"
]
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": 133,
"metadata": {},
"outputs": [
{
- "data": {},
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- "text/plain": [
- ":NdOverlay [ticker]\n",
- " :Curve [time] (cur_w)"
+ ""
]
},
- "execution_count": 12,
- "metadata": {
- "application/vnd.holoviews_exec.v0+json": {
- "id": "p1793"
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+ "model_id": "49cd388784a7420f8243cbcf76d1cd8b",
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+ "BokehModel(combine_events=True, render_bundle={'docs_json': {'fba37937-2449-4574-bb28-cb6a96c8cbc3': {'version…"
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+ "execution_count": 133,
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"source": [
- "# merged_df is sorted on time\n",
- "analytic_df = create_analytic_df(price_df, portfolio_p)\n",
- "calculate_cash(analytic_df)\n",
- "calculate_weight_using_cash(analytic_df)\n",
- "# analytic_df['pct'] = analytic_df.groupby('ticker')['close'].pct_change()\n",
- "# calculate weight\n",
- "# calculate_weight(analytic_df)\n",
- "analytic_df.hvplot.line(x='time', y='cur_w', by='ticker', width=500, height=400)\n",
- "# analytic_d"
+ "import plotly.express as px\n",
+ "import panel as pn\n",
+ "pn.extension('plotly')\n",
+ "fig = px.bar(agg_bhb_result,x='period_str',y=[\n",
+ " 'allocation', 'selection', 'interaction', 'notional_active_return', 'active_return'])\n",
+ "pn.pane.Plotly(fig)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "bhb_result"
]
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": null,
"metadata": {},
"outputs": [
{
@@ -786,1159 +507,32 @@
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cash | \n",
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ini_w | \n",
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ave_price | \n",
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cur_w | \n",
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pct | \n",
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norm_pct | \n",
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- " 消费 | \n",
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- " 消费 | \n",
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- " 消费 | \n",
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- " 消费 | \n",
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- "57 5.657508e+08 900.0 医药生物I 医疗服务II 医疗研发外包III 研究和试验发展 制药与生物科技服务 医药卫生 \n",
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- "79 4.004042e+07 400.0 纺织服装I 纺织制造II 纺织鞋类制造III 皮革、毛皮、羽毛及其制品和制鞋业 鞋帽与配饰 ... \n",
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- "35 3.509711e+09 23500.0 传媒I 数字媒体II 门户网站III 互联网和相关服务 图文媒体 通信服务 \n",
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- "38 3.852462e+09 1000.0 传媒I 数字媒体II 门户网站III 互联网和相关服务 图文媒体 通信服务 \n",
- "39 2.650834e+09 1000.0 传媒I 数字媒体II 门户网站III 互联网和相关服务 图文媒体 通信服务 \n",
- "40 4.299076e+09 1000.0 传媒I 数字媒体II 门户网站III 互联网和相关服务 图文媒体 通信服务 \n",
- "122 7.536682e+08 400.0 食品饮料I 调味发酵品II 调味发酵品III 食品制造业 调味品与食用油 主要消费 \n",
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- "131 1.000000 "
+ "Empty DataFrame\n",
+ "Columns: [time, ticker, open, close, high, low, volume, money, shares, sector, aggregate_sector, display_name, name, cash, ini_w, ave_price, weight, pct, norm_pct, return]\n",
+ "Index: []"
]
},
- "execution_count": 13,
+ "execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "analytic_df"
+ "# check if there a row with same ticker and time\n",
+ "analytic_b[analytic_b.duplicated(['ticker', 'time'])]\n",
+ "analytic_p[analytic_p.duplicated(['ticker', 'time'])]"
]
}
],