Initial commit after copying files from flawed repository
This commit is contained in:
David Brazda
771
to_explore/notebooks/StopSignals.ipynb
Normal file
771
to_explore/notebooks/StopSignals.ipynb
Normal file
@ -0,0 +1,771 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Notebook for the article [Stop Loss, Trailing Stop, or Take Profit? 2 Million Backtests Shed Light](https://polakowo.medium.com/stop-loss-trailing-stop-or-take-profit-2-million-backtests-shed-light-dde23bda40be)."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from vectorbtpro import *\n",
|
||||
"# whats_imported()\n",
|
||||
"\n",
|
||||
"import ipywidgets\n",
|
||||
"\n",
|
||||
"vbt.settings.set_theme('dark')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"seed = 42\n",
|
||||
"symbols = [\n",
|
||||
" \"BTC-USD\", \"ETH-USD\", \"XRP-USD\", \"BCH-USD\", \"LTC-USD\", \n",
|
||||
" \"BNB-USD\", \"EOS-USD\", \"XLM-USD\", \"XMR-USD\", \"ADA-USD\"\n",
|
||||
"]\n",
|
||||
"start_date = vbt.utc_timestamp(\"2018-01-01\")\n",
|
||||
"end_date = vbt.utc_timestamp(\"2021-01-01\")\n",
|
||||
"time_delta = end_date - start_date\n",
|
||||
"window_len = vbt.timedelta(\"180d\")\n",
|
||||
"window_cnt = 400\n",
|
||||
"exit_types = [\"SL\", \"TS\", \"TP\", \"Random\", \"Holding\"]\n",
|
||||
"step = 0.01\n",
|
||||
"stops = np.arange(step, 1 + step, step)\n",
|
||||
"\n",
|
||||
"vbt.settings.wrapping[\"freq\"] = \"d\"\n",
|
||||
"vbt.settings.plotting[\"layout\"][\"template\"] = \"vbt_dark\"\n",
|
||||
"vbt.settings.portfolio[\"init_cash\"] = 100.\n",
|
||||
"\n",
|
||||
"print(pd.Series({\n",
|
||||
" \"Start date\": start_date,\n",
|
||||
" \"End date\": end_date,\n",
|
||||
" \"Time period (days)\": time_delta.days,\n",
|
||||
" \"Assets\": len(symbols),\n",
|
||||
" \"Window length\": window_len,\n",
|
||||
" \"Windows\": window_cnt,\n",
|
||||
" \"Exit types\": len(exit_types),\n",
|
||||
" \"Stop values\": len(stops),\n",
|
||||
" \"Tests per asset\": window_cnt * len(stops) * len(exit_types),\n",
|
||||
" \"Tests per window\": len(symbols) * len(stops) * len(exit_types),\n",
|
||||
" \"Tests per exit type\": len(symbols) * window_cnt * len(stops),\n",
|
||||
" \"Tests per stop type and value\": len(symbols) * window_cnt,\n",
|
||||
" \"Tests total\": len(symbols) * window_cnt * len(stops) * len(exit_types)\n",
|
||||
"}))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"cols = [\"Open\", \"Low\", \"High\", \"Close\", \"Volume\"]\n",
|
||||
"yfdata = vbt.YFData.pull(symbols, start=start_date, end=end_date)\n",
|
||||
"\n",
|
||||
"print(yfdata.data.keys())\n",
|
||||
"print(yfdata.data[\"BTC-USD\"].shape)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"yfdata.plot(symbol=\"BTC-USD\").show_svg()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"ohlcv = yfdata.concat()\n",
|
||||
"\n",
|
||||
"print(ohlcv.keys())\n",
|
||||
"print(ohlcv[\"Open\"].shape)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"splitter = vbt.Splitter.from_n_rolling(\n",
|
||||
" ohlcv[\"Open\"].index, \n",
|
||||
" n=window_cnt,\n",
|
||||
" length=window_len.days\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"split_ohlcv = {}\n",
|
||||
"for k, v in ohlcv.items():\n",
|
||||
" split_ohlcv[k] = splitter.take(v, into=\"reset_stacked\")\n",
|
||||
"print(split_ohlcv[\"Open\"].shape)\n",
|
||||
"\n",
|
||||
"split_indexes = splitter.take(ohlcv[\"Open\"].index)\n",
|
||||
"print(split_indexes)\n",
|
||||
"print(split_indexes[10])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print(split_ohlcv[\"Open\"].columns)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"entries = pd.DataFrame.vbt.signals.empty_like(split_ohlcv[\"Open\"])\n",
|
||||
"entries.iloc[0, :] = True\n",
|
||||
"\n",
|
||||
"print(entries.shape)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# We use OHLCSTX instead of built-in stop-loss in Portfolio.from_signals\n",
|
||||
"# because we want to analyze signals before simulation + it's easier to construct param grids\n",
|
||||
"# For reality check, run the same setup using Portfolio.from_signals alone\n",
|
||||
"\n",
|
||||
"sl_ohlcstx = vbt.OHLCSTX.run(\n",
|
||||
" entries, \n",
|
||||
" entry_price=split_ohlcv[\"Close\"], \n",
|
||||
" open=split_ohlcv[\"Open\"], \n",
|
||||
" high=split_ohlcv[\"High\"], \n",
|
||||
" low=split_ohlcv[\"Low\"], \n",
|
||||
" close=split_ohlcv[\"Close\"], \n",
|
||||
" sl_stop=list(stops),\n",
|
||||
" stop_type=None\n",
|
||||
")\n",
|
||||
"sl_exits = sl_ohlcstx.exits.copy()\n",
|
||||
"sl_price = sl_ohlcstx.close.copy()\n",
|
||||
"sl_price[sl_exits] = sl_ohlcstx.stop_price\n",
|
||||
"del sl_ohlcstx\n",
|
||||
"\n",
|
||||
"print(sl_exits.shape)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"tsl_ohlcstx = vbt.OHLCSTX.run(\n",
|
||||
" entries, \n",
|
||||
" entry_price=split_ohlcv[\"Close\"], \n",
|
||||
" open=split_ohlcv[\"Open\"], \n",
|
||||
" high=split_ohlcv[\"High\"], \n",
|
||||
" low=split_ohlcv[\"Low\"], \n",
|
||||
" close=split_ohlcv[\"Close\"], \n",
|
||||
" tsl_stop=list(stops),\n",
|
||||
" stop_type=None\n",
|
||||
")\n",
|
||||
"tsl_exits = tsl_ohlcstx.exits.copy()\n",
|
||||
"tsl_price = tsl_ohlcstx.close.copy()\n",
|
||||
"tsl_price[tsl_exits] = tsl_ohlcstx.stop_price\n",
|
||||
"del tsl_ohlcstx\n",
|
||||
"\n",
|
||||
"print(tsl_exits.shape)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"tp_ohlcstx = vbt.OHLCSTX.run(\n",
|
||||
" entries, \n",
|
||||
" entry_price=split_ohlcv[\"Close\"], \n",
|
||||
" open=split_ohlcv[\"Open\"], \n",
|
||||
" high=split_ohlcv[\"High\"], \n",
|
||||
" low=split_ohlcv[\"Low\"], \n",
|
||||
" close=split_ohlcv[\"Close\"], \n",
|
||||
" tp_stop=list(stops),\n",
|
||||
" stop_type=None\n",
|
||||
")\n",
|
||||
"tp_exits = tp_ohlcstx.exits.copy()\n",
|
||||
"tp_price = tp_ohlcstx.close.copy()\n",
|
||||
"tp_price[tp_exits] = tp_ohlcstx.stop_price\n",
|
||||
"del tp_ohlcstx\n",
|
||||
"\n",
|
||||
"print(tp_exits.shape)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def rename_stop_level(df):\n",
|
||||
" return df.vbt.rename_levels({\n",
|
||||
" \"ohlcstx_sl_stop\": \"stop_value\",\n",
|
||||
" \"ohlcstx_tsl_stop\": \"stop_value\",\n",
|
||||
" \"ohlcstx_tp_stop\": \"stop_value\"\n",
|
||||
" }, strict=False)\n",
|
||||
"\n",
|
||||
"sl_exits = rename_stop_level(sl_exits)\n",
|
||||
"tsl_exits = rename_stop_level(tsl_exits)\n",
|
||||
"tp_exits = rename_stop_level(tp_exits)\n",
|
||||
"\n",
|
||||
"sl_price = rename_stop_level(sl_price)\n",
|
||||
"tsl_price = rename_stop_level(tsl_price)\n",
|
||||
"tp_price = rename_stop_level(tp_price)\n",
|
||||
"\n",
|
||||
"print(sl_exits.columns)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print(pd.Series({\n",
|
||||
" \"SL\": sl_exits.vbt.signals.total().mean(),\n",
|
||||
" \"TS\": tsl_exits.vbt.signals.total().mean(),\n",
|
||||
" \"TP\": tp_exits.vbt.signals.total().mean()\n",
|
||||
"}, name=\"avg_num_signals\"))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def groupby_stop_value(df):\n",
|
||||
" return df.vbt.signals.total().groupby(\"stop_value\").mean()\n",
|
||||
"\n",
|
||||
"pd.DataFrame({\n",
|
||||
" \"Stop Loss\": groupby_stop_value(sl_exits),\n",
|
||||
" \"Trailing Stop\": groupby_stop_value(tsl_exits),\n",
|
||||
" \"Take Profit\": groupby_stop_value(tp_exits)\n",
|
||||
"}).vbt.plot(\n",
|
||||
" xaxis_title=\"Stop value\", \n",
|
||||
" yaxis_title=\"Avg number of signals\"\n",
|
||||
").show_svg()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"sl_exits.iloc[-1, :] = True\n",
|
||||
"tsl_exits.iloc[-1, :] = True\n",
|
||||
"tp_exits.iloc[-1, :] = True\n",
|
||||
"\n",
|
||||
"sl_exits = sl_exits.vbt.signals.first_after(entries)\n",
|
||||
"tsl_exits = tsl_exits.vbt.signals.first_after(entries)\n",
|
||||
"tp_exits = tp_exits.vbt.signals.first_after(entries)\n",
|
||||
"\n",
|
||||
"print(pd.Series({\n",
|
||||
" \"SL\": sl_exits.vbt.signals.total().mean(),\n",
|
||||
" \"TS\": tsl_exits.vbt.signals.total().mean(),\n",
|
||||
" \"TP\": tp_exits.vbt.signals.total().mean()\n",
|
||||
"}, name=\"avg_num_signals\"))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"hold_exits = pd.DataFrame.vbt.signals.empty_like(sl_exits)\n",
|
||||
"hold_exits.iloc[-1, :] = True\n",
|
||||
"hold_price = vbt.broadcast_to(split_ohlcv[\"Close\"], sl_price)\n",
|
||||
"\n",
|
||||
"print(hold_exits.shape)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"rand_exits = hold_exits.vbt.shuffle(seed=seed)\n",
|
||||
"rand_price = hold_price\n",
|
||||
"\n",
|
||||
"print(rand_exits.shape)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"exits = pd.DataFrame.vbt.concat(\n",
|
||||
" sl_exits, \n",
|
||||
" tsl_exits, \n",
|
||||
" tp_exits, \n",
|
||||
" rand_exits, \n",
|
||||
" hold_exits, \n",
|
||||
" keys=pd.Index(exit_types, name=\"exit_type\")\n",
|
||||
")\n",
|
||||
"del sl_exits\n",
|
||||
"del tsl_exits\n",
|
||||
"del tp_exits\n",
|
||||
"del rand_exits\n",
|
||||
"del hold_exits\n",
|
||||
"\n",
|
||||
"print(exits.shape)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"price = pd.DataFrame.vbt.concat(\n",
|
||||
" sl_price, \n",
|
||||
" tsl_price, \n",
|
||||
" tp_price, \n",
|
||||
" rand_price, \n",
|
||||
" hold_price, \n",
|
||||
" keys=pd.Index(exit_types, name=\"exit_type\")\n",
|
||||
")\n",
|
||||
"del sl_price\n",
|
||||
"del tsl_price\n",
|
||||
"del tp_price\n",
|
||||
"del rand_price\n",
|
||||
"del hold_price\n",
|
||||
"\n",
|
||||
"print(price.shape)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print(exits.columns)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print(exits.vbt.getsize())"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print(price.vbt.getsize())"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"avg_distance = entries.vbt.signals.between_ranges(target=exits)\\\n",
|
||||
" .duration.mean()\\\n",
|
||||
" .groupby([\"exit_type\", \"stop_value\"])\\\n",
|
||||
" .mean()\\\n",
|
||||
" .unstack(level=\"exit_type\")\n",
|
||||
"\n",
|
||||
"print(avg_distance.mean())"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"avg_distance[exit_types].vbt.plot(\n",
|
||||
" xaxis_title=\"Stop value\", \n",
|
||||
" yaxis_title=\"Avg distance to entry\"\n",
|
||||
").show_svg()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"%%time\n",
|
||||
"pf = vbt.Portfolio.from_signals(\n",
|
||||
" split_ohlcv[\"Close\"], \n",
|
||||
" entries, \n",
|
||||
" exits, \n",
|
||||
" price=price\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"print(len(pf.orders))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"total_return = pf.total_return\n",
|
||||
"del pf\n",
|
||||
"\n",
|
||||
"print(total_return.shape)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import gc\n",
|
||||
"\n",
|
||||
"total_returns = []\n",
|
||||
"for i in vbt.ProgressBar(range(len(exit_types))):\n",
|
||||
" chunk_mask = exits.columns.get_level_values(\"exit_type\") == exit_types[i]\n",
|
||||
" chunk_pf = vbt.Portfolio.from_signals(\n",
|
||||
" split_ohlcv[\"Close\"], \n",
|
||||
" entries, \n",
|
||||
" exits.loc[:, chunk_mask],\n",
|
||||
" price=price.loc[:, chunk_mask]\n",
|
||||
" )\n",
|
||||
" total_returns.append(chunk_pf.total_return)\n",
|
||||
" \n",
|
||||
" del chunk_pf\n",
|
||||
" gc.collect()\n",
|
||||
" \n",
|
||||
"total_return = pd.concat(total_returns)\n",
|
||||
"\n",
|
||||
"print(total_return.shape)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"return_by_type = total_return.unstack(level=\"exit_type\")[exit_types]\n",
|
||||
"\n",
|
||||
"print(return_by_type[\"Holding\"].describe(percentiles=[]))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"purple_color = vbt.settings[\"plotting\"][\"color_schema\"][\"purple\"]\n",
|
||||
"return_by_type[\"Holding\"].vbt.histplot(\n",
|
||||
" xaxis_title=\"Total return\",\n",
|
||||
" xaxis_tickformat=\".2%\",\n",
|
||||
" yaxis_title=\"Count\",\n",
|
||||
" trace_kwargs=dict(marker_color=purple_color)\n",
|
||||
").show_svg()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print(pd.DataFrame({\n",
|
||||
" \"Mean\": return_by_type.mean(),\n",
|
||||
" \"Median\": return_by_type.median(),\n",
|
||||
" \"Std\": return_by_type.std(),\n",
|
||||
"}))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"return_by_type.vbt.boxplot(\n",
|
||||
" trace_kwargs=dict(boxpoints=False),\n",
|
||||
" yaxis_title=\"Total return\",\n",
|
||||
" yaxis_tickformat=\".2%\"\n",
|
||||
").show_svg()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print((return_by_type > 0).mean().rename(\"win_rate\"))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"init_cash = vbt.settings.portfolio[\"init_cash\"]\n",
|
||||
"\n",
|
||||
"def get_expectancy(return_by_type, level_name):\n",
|
||||
" grouped = return_by_type.groupby(level_name, axis=0)\n",
|
||||
" win_rate = grouped.apply(lambda x: (x > 0).mean())\n",
|
||||
" avg_win = grouped.apply(lambda x: init_cash * x[x > 0].mean())\n",
|
||||
" avg_win = avg_win.fillna(0)\n",
|
||||
" avg_loss = grouped.apply(lambda x: init_cash * x[x < 0].mean())\n",
|
||||
" avg_loss = avg_loss.fillna(0)\n",
|
||||
" return win_rate * avg_win - (1 - win_rate) * np.abs(avg_loss)\n",
|
||||
" \n",
|
||||
"expectancy_by_stop = get_expectancy(return_by_type, \"stop_value\")\n",
|
||||
"\n",
|
||||
"print(expectancy_by_stop.mean())"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"expectancy_by_stop.vbt.plot(\n",
|
||||
" xaxis_title=\"Stop value\", \n",
|
||||
" yaxis_title=\"Expectancy\"\n",
|
||||
").show_svg()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"return_values = np.sort(return_by_type[\"Holding\"].values)\n",
|
||||
"idxs = np.ceil(np.linspace(0, len(return_values) - 1, 21)).astype(int)\n",
|
||||
"bins = return_values[idxs][:-1]\n",
|
||||
"\n",
|
||||
"def bin_return(return_by_type):\n",
|
||||
" classes = pd.cut(return_by_type[\"Holding\"], bins=bins, right=True)\n",
|
||||
" new_level = np.array(classes.apply(lambda x: x.right))\n",
|
||||
" new_level = pd.Index(new_level, name=\"bin_right\")\n",
|
||||
" return return_by_type.vbt.add_levels(new_level, axis=0)\n",
|
||||
"\n",
|
||||
"binned_return_by_type = bin_return(return_by_type)\n",
|
||||
"\n",
|
||||
"expectancy_by_bin = get_expectancy(binned_return_by_type, \"bin_right\")\n",
|
||||
"\n",
|
||||
"expectancy_by_bin.vbt.plot(\n",
|
||||
" trace_kwargs=dict(mode=\"lines\"),\n",
|
||||
" xaxis_title=\"Total return of holding\",\n",
|
||||
" xaxis_tickformat=\".2%\",\n",
|
||||
" yaxis_title=\"Expectancy\"\n",
|
||||
").show_svg()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"range_starts = pd.DatetimeIndex(list(map(lambda x: x[0], split_indexes)))\n",
|
||||
"range_ends = pd.DatetimeIndex(list(map(lambda x: x[-1], split_indexes)))\n",
|
||||
"\n",
|
||||
"symbol_lvl = return_by_type.index.get_level_values(\"symbol\")\n",
|
||||
"split_lvl = return_by_type.index.get_level_values(\"split\")\n",
|
||||
"range_start_lvl = range_starts[split_lvl]\n",
|
||||
"range_end_lvl = range_ends[split_lvl]\n",
|
||||
"\n",
|
||||
"asset_multi_select = ipywidgets.SelectMultiple(\n",
|
||||
" options=symbols,\n",
|
||||
" value=symbols,\n",
|
||||
" rows=len(symbols),\n",
|
||||
" description=\"Symbols\"\n",
|
||||
")\n",
|
||||
"dates = np.unique(yfdata.wrapper.index)\n",
|
||||
"date_range_slider = ipywidgets.SelectionRangeSlider(\n",
|
||||
" options=dates,\n",
|
||||
" index=(0, len(dates)-1),\n",
|
||||
" orientation=\"horizontal\",\n",
|
||||
" readout=False,\n",
|
||||
" continuous_update=False\n",
|
||||
")\n",
|
||||
"range_start_label = ipywidgets.Label()\n",
|
||||
"range_end_label = ipywidgets.Label()\n",
|
||||
"metric_dropdown = ipywidgets.Dropdown(\n",
|
||||
" options=[\"Mean\", \"Median\", \"Win Rate\", \"Expectancy\"],\n",
|
||||
" value=\"Expectancy\"\n",
|
||||
")\n",
|
||||
"stop_scatter = vbt.Scatter(\n",
|
||||
" trace_names=exit_types,\n",
|
||||
" x_labels=stops, \n",
|
||||
" xaxis_title=\"Stop value\", \n",
|
||||
" yaxis_title=\"Expectancy\"\n",
|
||||
")\n",
|
||||
"stop_scatter_img = ipywidgets.Image(\n",
|
||||
" format=\"png\",\n",
|
||||
" width=stop_scatter.fig.layout.width,\n",
|
||||
" height=stop_scatter.fig.layout.height\n",
|
||||
")\n",
|
||||
"bin_scatter = vbt.Scatter(\n",
|
||||
" trace_names=exit_types,\n",
|
||||
" x_labels=expectancy_by_bin.index, \n",
|
||||
" trace_kwargs=dict(mode=\"lines\"),\n",
|
||||
" xaxis_title=\"Total return of holding\",\n",
|
||||
" xaxis_tickformat=\"%\",\n",
|
||||
" yaxis_title=\"Expectancy\"\n",
|
||||
")\n",
|
||||
"bin_scatter_img = ipywidgets.Image(\n",
|
||||
" format=\"png\",\n",
|
||||
" width=bin_scatter.fig.layout.width,\n",
|
||||
" height=bin_scatter.fig.layout.height\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def update_scatter(*args, **kwargs):\n",
|
||||
" _symbols = asset_multi_select.value\n",
|
||||
" _from = date_range_slider.value[0]\n",
|
||||
" _to = date_range_slider.value[1]\n",
|
||||
" _metric_name = metric_dropdown.value\n",
|
||||
" \n",
|
||||
" range_mask = (range_start_lvl >= _from) & (range_end_lvl <= _to)\n",
|
||||
" asset_mask = symbol_lvl.isin(_symbols)\n",
|
||||
" filt = return_by_type[range_mask & asset_mask]\n",
|
||||
" \n",
|
||||
" filt_binned = bin_return(filt)\n",
|
||||
" if _metric_name == \"Mean\":\n",
|
||||
" filt_metric = filt.groupby(\"stop_value\").mean()\n",
|
||||
" filt_bin_metric = filt_binned.groupby(\"bin_right\").mean()\n",
|
||||
" elif _metric_name == \"Median\":\n",
|
||||
" filt_metric = filt.groupby(\"stop_value\").median()\n",
|
||||
" filt_bin_metric = filt_binned.groupby(\"bin_right\").median()\n",
|
||||
" elif _metric_name == \"Win Rate\":\n",
|
||||
" filt_metric = (filt > 0).groupby(\"stop_value\").mean()\n",
|
||||
" filt_bin_metric = (filt_binned > 0).groupby(\"bin_right\").mean()\n",
|
||||
" elif _metric_name == \"Expectancy\":\n",
|
||||
" filt_metric = get_expectancy(filt, \"stop_value\")\n",
|
||||
" filt_bin_metric = get_expectancy(filt_binned, \"bin_right\")\n",
|
||||
" \n",
|
||||
" stop_scatter.fig.update_layout(yaxis_title=_metric_name)\n",
|
||||
" stop_scatter.update(filt_metric)\n",
|
||||
" stop_scatter_img.value = stop_scatter.fig.to_image(format=\"png\")\n",
|
||||
" \n",
|
||||
" bin_scatter.fig.update_layout(yaxis_title=_metric_name)\n",
|
||||
" bin_scatter.update(filt_bin_metric)\n",
|
||||
" bin_scatter_img.value = bin_scatter.fig.to_image(format=\"png\")\n",
|
||||
" \n",
|
||||
" range_start_label.value = np.datetime_as_string(_from.to_datetime64(), unit=\"D\")\n",
|
||||
" range_end_label.value = np.datetime_as_string(_to.to_datetime64(), unit=\"D\")\n",
|
||||
" \n",
|
||||
"asset_multi_select.observe(update_scatter, names=\"value\")\n",
|
||||
"date_range_slider.observe(update_scatter, names=\"value\")\n",
|
||||
"metric_dropdown.observe(update_scatter, names=\"value\")\n",
|
||||
"update_scatter()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"dashboard = ipywidgets.VBox([\n",
|
||||
" asset_multi_select,\n",
|
||||
" ipywidgets.HBox([\n",
|
||||
" range_start_label,\n",
|
||||
" date_range_slider,\n",
|
||||
" range_end_label\n",
|
||||
" ]),\n",
|
||||
" metric_dropdown,\n",
|
||||
" stop_scatter_img,\n",
|
||||
" bin_scatter_img\n",
|
||||
"])\n",
|
||||
"dashboard"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"dashboard.close()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3 (ipykernel)",
|
||||
"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": "3.9.12"
|
||||
},
|
||||
"widgets": {
|
||||
"application/vnd.jupyter.widget-state+json": {
|
||||
"state": {},
|
||||
"version_major": 2,
|
||||
"version_minor": 0
|
||||
}
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 4
|
||||
}
|
||||
Reference in New Issue
Block a user