{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/html": [ " \n", " " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " \n", " " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " \n", " " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " \n", " " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import pandas as pd\n", "import numpy as np\n", "import xlrd\n", "from plotly.graph_objs import Scatter, layout\n", "import plotly\n", "import plotly.offline as py\n", "import plotly.graph_objs as go\n", "import cufflinks as cf\n", "from urllib.request import urlopen\n", "import json\n", "import re\n", "cf.go_offline()###这两句是离线生成图片的设置\n", "cf.set_config_file(offline=True, world_readable=True)\n", "plotly.offline.init_notebook_mode(connected=True)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "df = pd.read_csv('data.csv')" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "df.index = df['Unnamed: 0']\n", "df.drop('Unnamed: 0', axis=1, inplace=True)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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ValueUSD (PM)stopTradebitcoinMargingoldMarginbitcoinFivegoldTwenty
Unnamed: 0
2016-09-11621.65NaN1NaNNaNNaNNaN
2016-09-12609.671324.600-0.019271NaNNaNNaN
2016-09-13610.921323.6500.002050-0.000717NaNNaN
2016-09-14608.821321.750-0.003437-0.001435NaNNaN
2016-09-15610.381310.8000.002562-0.008284612.288NaN
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" ], "text/plain": [ " Value USD (PM) stopTrade bitcoinMargin goldMargin \\\n", "Unnamed: 0 \n", "2016-09-11 621.65 NaN 1 NaN NaN \n", "2016-09-12 609.67 1324.60 0 -0.019271 NaN \n", "2016-09-13 610.92 1323.65 0 0.002050 -0.000717 \n", "2016-09-14 608.82 1321.75 0 -0.003437 -0.001435 \n", "2016-09-15 610.38 1310.80 0 0.002562 -0.008284 \n", "\n", " bitcoinFive goldTwenty \n", "Unnamed: 0 \n", "2016-09-11 NaN NaN \n", "2016-09-12 NaN NaN \n", "2016-09-13 NaN NaN \n", "2016-09-14 NaN NaN \n", "2016-09-15 612.288 NaN " ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "# n-days bias\n", "df['bitcoinBias'] = (df['Value'] - df['bitcoinFive']) / df['bitcoinFive']\n", "df['goldBias'] = (df['USD (PM)'] - df['goldTwenty']) / df['goldTwenty']" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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ValueUSD (PM)stopTradebitcoinMargingoldMarginbitcoinFivegoldTwentybitcoinBiasgoldBias
date
2016-09-11621.65NaN1NaNNaNNaNNaNNaNNaN
2016-09-12609.671324.600-0.019271NaNNaNNaNNaNNaN
2016-09-13610.921323.6500.002050-0.000717NaNNaNNaNNaN
2016-09-14608.821321.750-0.003437-0.001435NaNNaNNaNNaN
2016-09-15610.381310.8000.002562-0.008284612.288NaN-0.003116NaN
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" ], "text/plain": [ " Value USD (PM) stopTrade bitcoinMargin goldMargin \\\n", "date \n", "2016-09-11 621.65 NaN 1 NaN NaN \n", "2016-09-12 609.67 1324.60 0 -0.019271 NaN \n", "2016-09-13 610.92 1323.65 0 0.002050 -0.000717 \n", "2016-09-14 608.82 1321.75 0 -0.003437 -0.001435 \n", "2016-09-15 610.38 1310.80 0 0.002562 -0.008284 \n", "\n", " bitcoinFive goldTwenty bitcoinBias goldBias \n", "date \n", "2016-09-11 NaN NaN NaN NaN \n", "2016-09-12 NaN NaN NaN NaN \n", "2016-09-13 NaN NaN NaN NaN \n", "2016-09-14 NaN NaN NaN NaN \n", "2016-09-15 612.288 NaN -0.003116 NaN " ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head()" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [], "source": [ "bit = pd.read_csv('prediction_of_bitcoin.csv')\n", "god = pd.read_csv('prediction_of_gold.csv')" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(1826, 9)" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.shape" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(1806, 2)" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "bit.shape" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(1785, 2)" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "god.shape" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [], "source": [ "data_bit = np.append(np.array([0] * 41), god['data'].values)" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [], "source": [ "df['predGold'] = data_bit" ] }, { "cell_type": "code", "execution_count": 51, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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ValueUSD (PM)stopTradebitcoinMargingoldMarginbitcoinFivegoldTwentybitcoinBiasgoldBiaspredBitcoinpredGold
date
2016-09-11621.650.0010.0000000.0000000.0000.00.0000000.00.00.0
2016-09-12609.671324.600-0.0192710.0000000.0000.00.0000000.00.00.0
2016-09-13610.921323.6500.002050-0.0007170.0000.00.0000000.00.00.0
2016-09-14608.821321.750-0.003437-0.0014350.0000.00.0000000.00.00.0
2016-09-15610.381310.8000.002562-0.008284612.2880.0-0.0031160.00.00.0
\n", "
" ], "text/plain": [ " Value USD (PM) stopTrade bitcoinMargin goldMargin \\\n", "date \n", "2016-09-11 621.65 0.00 1 0.000000 0.000000 \n", "2016-09-12 609.67 1324.60 0 -0.019271 0.000000 \n", "2016-09-13 610.92 1323.65 0 0.002050 -0.000717 \n", "2016-09-14 608.82 1321.75 0 -0.003437 -0.001435 \n", "2016-09-15 610.38 1310.80 0 0.002562 -0.008284 \n", "\n", " bitcoinFive goldTwenty bitcoinBias goldBias predBitcoin \\\n", "date \n", "2016-09-11 0.000 0.0 0.000000 0.0 0.0 \n", "2016-09-12 0.000 0.0 0.000000 0.0 0.0 \n", "2016-09-13 0.000 0.0 0.000000 0.0 0.0 \n", "2016-09-14 0.000 0.0 0.000000 0.0 0.0 \n", "2016-09-15 612.288 0.0 -0.003116 0.0 0.0 \n", "\n", " predGold \n", "date \n", "2016-09-11 0.0 \n", "2016-09-12 0.0 \n", "2016-09-13 0.0 \n", "2016-09-14 0.0 \n", "2016-09-15 0.0 " ] }, "execution_count": 51, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head()" ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [], "source": [ "df = df.fillna(0)" ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [], "source": [ "mx, mn = df['goldMargin'].max(), df['goldMargin'].min()\n", "df['gM_norm'] = df['goldMargin'].apply(lambda x : (x - mn) / (mx - mn))" ] }, { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [], "source": [ "df['bB_norm'] = np.abs(df['bitcoinBias'])\n", "df['gB_norm'] = np.abs(df['goldBias'])" ] }, { "cell_type": "code", "execution_count": 56, "metadata": {}, "outputs": [], "source": [ "mx, mn = df['gB_norm'].max(), df['gB_norm'].min()\n", "df['gB_norm'] = df['gB_norm'].apply(lambda x : (x - mn) / (mx - mn))" ] }, { "cell_type": "code", "execution_count": 57, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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ValueUSD (PM)stopTradebitcoinMargingoldMarginbitcoinFivegoldTwentybitcoinBiasgoldBiaspredBitcoinpredGoldbM_normgM_normbB_normgB_norm
date
2016-09-11621.650.0010.0000000.0000000.0000.00.0000000.00.00.00.6415690.4933110.000000.0
2016-09-12609.671324.600-0.0192710.0000000.0000.00.0000000.00.00.00.6099810.4933110.000000.0
2016-09-13610.921323.6500.002050-0.0007170.0000.00.0000000.00.00.00.6449300.4864120.000000.0
2016-09-14608.821321.750-0.003437-0.0014350.0000.00.0000000.00.00.00.6359350.4795030.000000.0
2016-09-15610.381310.8000.002562-0.008284612.2880.0-0.0031160.00.00.00.6457690.4136210.009150.0
\n", "
" ], "text/plain": [ " Value USD (PM) stopTrade bitcoinMargin goldMargin \\\n", "date \n", "2016-09-11 621.65 0.00 1 0.000000 0.000000 \n", "2016-09-12 609.67 1324.60 0 -0.019271 0.000000 \n", "2016-09-13 610.92 1323.65 0 0.002050 -0.000717 \n", "2016-09-14 608.82 1321.75 0 -0.003437 -0.001435 \n", "2016-09-15 610.38 1310.80 0 0.002562 -0.008284 \n", "\n", " bitcoinFive goldTwenty bitcoinBias goldBias predBitcoin \\\n", "date \n", "2016-09-11 0.000 0.0 0.000000 0.0 0.0 \n", "2016-09-12 0.000 0.0 0.000000 0.0 0.0 \n", "2016-09-13 0.000 0.0 0.000000 0.0 0.0 \n", "2016-09-14 0.000 0.0 0.000000 0.0 0.0 \n", "2016-09-15 612.288 0.0 -0.003116 0.0 0.0 \n", "\n", " predGold bM_norm gM_norm bB_norm gB_norm \n", "date \n", "2016-09-11 0.0 0.641569 0.493311 0.00000 0.0 \n", "2016-09-12 0.0 0.609981 0.493311 0.00000 0.0 \n", "2016-09-13 0.0 0.644930 0.486412 0.00000 0.0 \n", "2016-09-14 0.0 0.635935 0.479503 0.00000 0.0 \n", "2016-09-15 0.0 0.645769 0.413621 0.00915 0.0 " ] }, "execution_count": 57, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head()" ] }, { "cell_type": "code", "execution_count": 61, "metadata": {}, "outputs": [], "source": [ "# 牛市\n", "df['bitcoin_cow'] = df['bM_norm'].rolling(30).mean() * 0.7 + df['bB_norm'].rolling(30).mean() * 0.3\n", "df['gold_cow'] = df['gM_norm'].rolling(90).mean() * 0.7 + df['gB_norm'].rolling(90).mean() * 0.3" ] }, { "cell_type": "code", "execution_count": 82, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "plt.scatter(df.index, df['gold_cow'])\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 89, "metadata": {}, "outputs": [], "source": [ "g_cow = []\n", "g_bear = []\n", "for i in range(89,df.shape[0]):\n", " if df['gold_cow'][i]>0.40:\n", " g_cow.append(i)\n", " if df['gold_cow'][i]<=0.40:\n", " g_bear.append(i)" ] }, { "cell_type": "code", "execution_count": 105, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "mode": "lines", "type": "scatter", "x": [ "2016-09-11", "2016-09-12", "2016-09-13", "2016-09-14", "2016-09-15", "2016-09-16", "2016-09-17", "2016-09-18", "2016-09-19", "2016-09-20", "2016-09-21", "2016-09-22", "2016-09-23", "2016-09-24", "2016-09-25", "2016-09-26", "2016-09-27", "2016-09-28", "2016-09-29", "2016-09-30", "2016-10-01", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = go.Figure()\n", "fig.add_trace(go.Scatter(\n", " x = df.index[89:],\n", " y = df['USD (PM)'][89:],\n", " mode='lines',\n", "))\n", "fig.add_trace(go.Scatter(\n", " x = df.index[89:],\n", " y = df['USD (PM)'][89:],\n", " mode = 'markers',\n", " marker = dict(\n", " size=4,\n", " color=df['gold_cow'][89:],\n", " colorscale='Viridis',\n", " showscale=True,\n", " )\n", "))\n", "fig.update_layout(showlegend=False, title=dict(text='Gold\\'s stock market index', x=0.5, y=0.9),\n", " height=400,\n", " width=500,\n", " xaxis_title='Date',\n", " yaxis_title='U.S. dollars($)',\n", ")\n", "fig.show()" ] }, { "cell_type": "code", "execution_count": 126, "metadata": {}, "outputs": [], "source": [ "df['bitcoin_v'] = np.abs(df['bitcoin_v'])\n", "df['gold_v'] = np.abs(df['gold_v'])" ] }, { "cell_type": "code", "execution_count": 128, "metadata": {}, "outputs": [], "source": [ "mx, mn = df['gold_v'].max(), df['gold_v'].min()\n", "df['gold_v'] = df['gold_v'].apply(lambda x : (x - mn) / (mx - mn))" ] }, { "cell_type": "code", "execution_count": 139, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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ValueUSD (PM)stopTradebitcoinMargingoldMarginbitcoinFivegoldTwentybitcoinBiasgoldBiasbM_normgM_normbB_normgB_normbitcoin_cowgold_cowbitcoin_cow_normbitcoin_vgold_vgold_cow_norm
date
2016-09-11621.650.0010.0000000.0000000.0000.00.0000000.00.6415690.4933110.000000.00.00.00.00.000000.00.0
2016-09-12609.671324.600-0.0192710.0000000.0000.00.0000000.00.6099810.4933110.000000.00.00.00.00.000000.00.0
2016-09-13610.921323.6500.002050-0.0007170.0000.00.0000000.00.6449300.4864120.000000.00.00.00.00.000000.00.0
2016-09-14608.821321.750-0.003437-0.0014350.0000.00.0000000.00.6359350.4795030.000000.00.00.00.00.000000.00.0
2016-09-15610.381310.8000.002562-0.008284612.2880.0-0.0031160.00.6457690.4136210.009150.00.00.00.00.000940.00.0
\n", "
" ], "text/plain": [ " Value USD (PM) stopTrade bitcoinMargin goldMargin \\\n", "date \n", "2016-09-11 621.65 0.00 1 0.000000 0.000000 \n", "2016-09-12 609.67 1324.60 0 -0.019271 0.000000 \n", "2016-09-13 610.92 1323.65 0 0.002050 -0.000717 \n", "2016-09-14 608.82 1321.75 0 -0.003437 -0.001435 \n", "2016-09-15 610.38 1310.80 0 0.002562 -0.008284 \n", "\n", " bitcoinFive goldTwenty bitcoinBias goldBias bM_norm \\\n", "date \n", "2016-09-11 0.000 0.0 0.000000 0.0 0.641569 \n", "2016-09-12 0.000 0.0 0.000000 0.0 0.609981 \n", "2016-09-13 0.000 0.0 0.000000 0.0 0.644930 \n", "2016-09-14 0.000 0.0 0.000000 0.0 0.635935 \n", "2016-09-15 612.288 0.0 -0.003116 0.0 0.645769 \n", "\n", " gM_norm bB_norm gB_norm bitcoin_cow gold_cow \\\n", "date \n", "2016-09-11 0.493311 0.00000 0.0 0.0 0.0 \n", "2016-09-12 0.493311 0.00000 0.0 0.0 0.0 \n", "2016-09-13 0.486412 0.00000 0.0 0.0 0.0 \n", "2016-09-14 0.479503 0.00000 0.0 0.0 0.0 \n", "2016-09-15 0.413621 0.00915 0.0 0.0 0.0 \n", "\n", " bitcoin_cow_norm bitcoin_v gold_v gold_cow_norm \n", "date \n", "2016-09-11 0.0 0.00000 0.0 0.0 \n", "2016-09-12 0.0 0.00000 0.0 0.0 \n", "2016-09-13 0.0 0.00000 0.0 0.0 \n", "2016-09-14 0.0 0.00000 0.0 0.0 \n", "2016-09-15 0.0 0.00094 0.0 0.0 " ] }, "execution_count": 139, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 至此，风险系数三个因素都归一化了，bitcoin_v, gold_v, bitcoin_cow_norm, gold_cow_norm, bB_norm, gB_norm\n", "df.head()" ] }, { "cell_type": "code", "execution_count": 166, "metadata": {}, "outputs": [], "source": [ "# 对于风险来说， 速度， 市场指数， 乖离率都是越大风险越大，因此都是正向指标\n", "b_risk = pd.DataFrame({'v':df['bitcoin_v'], 'cow':df['bitcoin_cow_norm'], 'bias':df['bB_norm']})\n", "g_risk = pd.DataFrame({'v':df['gold_v'], 'cow':df['gold_cow_norm'], 'bias':df['gB_norm']})" ] }, { "cell_type": "code", "execution_count": 168, "metadata": {}, "outputs": [], "source": [ "def f(x):\n", " return np.sqrt(np.sum(np.square(x)))\n", "x, y, z = f(b_risk['v']), f(b_risk['cow']), f(b_risk['bias'])\n", "b_risk['v'] = b_risk['v'] / x\n", "b_risk['cow'] = b_risk['cow'] / y\n", "b_risk['bias'] = b_risk['bias'] / z\n" ] }, { "cell_type": "code", "execution_count": 170, "metadata": {}, "outputs": [], "source": [ "for col in b_risk.columns:\n", " mn, mx = b_risk[col].min(), b_risk[col].max()\n", " b_risk[col] = b_risk[col].apply(lambda x : (x - mn) / (mx - mn))" ] }, { "cell_type": "code", "execution_count": 171, "metadata": {}, "outputs": [], "source": [ "# ~Z\n", "for col in b_risk.columns:\n", " tmp = np.sum(b_risk[col])\n", " b_risk[col] = b_risk[col] / tmp" ] }, { "cell_type": "code", "execution_count": 174, "metadata": {}, "outputs": [], "source": [ "n = b_risk.shape[0]" ] }, { "cell_type": "code", "execution_count": 177, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\霖\\venv\\scrapy\\lib\\site-packages\\pandas\\core\\series.py:679: RuntimeWarning:\n", "\n", "divide by zero encountered in log\n", "\n" ] } ], "source": [ "e = []\n", "for col in b_risk.columns:\n", " e.append(np.sum(np.multiply(b_risk[col], np.log(b_risk[col]))))" ] }, { "cell_type": "code", "execution_count": 180, "metadata": {}, "outputs": [], "source": [ "d = 1 - np.array(e)" ] }, { "cell_type": "code", "execution_count": 182, "metadata": {}, "outputs": [], "source": [ "W = d / np.sum(d)" ] }, { "cell_type": "code", "execution_count": 183, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([0.31689368, 0.34626313, 0.33684319])" ] }, "execution_count": 183, "metadata": {}, "output_type": "execute_result" } ], "source": [ "W" ] }, { "cell_type": "code", "execution_count": 184, "metadata": {}, "outputs": [], "source": [ "# gold\n", "def f(x):\n", " return np.sqrt(np.sum(np.square(x)))\n", "x, y, z = f(g_risk['v']), f(g_risk['cow']), f(g_risk['bias'])\n", "g_risk['v'] = g_risk['v'] / x\n", "g_risk['cow'] = g_risk['cow'] / y\n", "g_risk['bias'] = g_risk['bias'] / z\n" ] }, { "cell_type": "code", "execution_count": 185, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\霖\\venv\\scrapy\\lib\\site-packages\\pandas\\core\\series.py:679: RuntimeWarning:\n", "\n", "divide by zero encountered in log\n", "\n" ] } ], "source": [ "for col in g_risk.columns:\n", " mn, mx = g_risk[col].min(), g_risk[col].max()\n", " g_risk[col] = g_risk[col].apply(lambda x : (x - mn) / (mx - mn))\n", "# ~Z\n", "for col in g_risk.columns:\n", " tmp = np.sum(g_risk[col])\n", " g_risk[col] = g_risk[col] / tmp\n", "n = g_risk.shape[0]\n", "e = []\n", "for col in g_risk.columns:\n", " e.append(np.sum(np.multiply(g_risk[col], np.log(g_risk[col]))))\n", "d = 1 - np.array(e)\n", "W = d / np.sum(d)" ] }, { "cell_type": "code", "execution_count": 186, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([0.32732319, 0.34240056, 0.33027624])" ] }, "execution_count": 186, "metadata": {}, "output_type": "execute_result" } ], "source": [ "W" ] }, { "cell_type": "code", "execution_count": 187, "metadata": {}, "outputs": [], "source": [ "W1 = np.array([0.31689368, 0.34626313, 0.33684319])\n", "W2 = np.array([0.32732319, 0.34240056, 0.33027624])\n", "df['risk_b'] = W1[0] * df['bitcoin_v'] + W[1] * df['bitcoin_cow_norm'] + W[2] * df['bB_norm']\n", "df['risk_g'] = W2[0] * df['gold_v'] + W[1] * df['gold_cow_norm'] + W[2] * df['gB_norm']" ] }, { "cell_type": "code", "execution_count": 188, "metadata": {}, "outputs": [], "source": [ "for col in ['risk_b', 'risk_g']:\n", " mn, mx = df[col].min(), df[col].max()\n", " df[col] = df[col].apply(lambda x : (x - mn) / (mx - mn))" ] }, { "cell_type": "code", "execution_count": 192, "metadata": {}, "outputs": [], "source": [ "y1, y2 = np.mean(df['risk_b']), np.mean(df['risk_g'])" ] }, { "cell_type": "code", "execution_count": 212, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "mode": "lines", "name": "Risk Factor of Gold", "type": "scatter", "x": [ 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