{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "1VVIL-rnbSER" }, "source": [ "# Pythonによる米国株テクニカル分析と検証: \n", "# -どのテクニカル指標が良い結果を出すのか過去データを検証しよう\n", "\n", "
\n",
    "Pythonによる米国株テクニカル分析と検証: -どのテクニカル指標が良い結果を出すのか過去データを検証しよう\n",
    "Satoshi (著)  形式: Kindle版\n",
    "ASIN: B092LX6D25\n",
    "発売日: 2021/4/14\n",
    "
\n", "\n", "\n", "本のソースコード: \n", "https://vrquest.org/index.php/technical-indicator-and-anomaly\n", "\n", "\n", "Google Colab:\n", "\n", "https://colab.research.google.com/introp.ipython\n", "\n", "\n", "筆者のTwitter: \n", "\n", "https://twitter.com/beavis28\n", "\n", "\n", "筆者の Youtube:\n", "\n", "https://www.youtube.com/channel/UChXU9b0QMF24hw1Au-VAS0g\n", "" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "is_colab = 'google.colab' in str(get_ipython()) # for Google Colab" ] }, { "cell_type": "markdown", "metadata": { "id": "a0LMdAogcIrI" }, "source": [ "## Alpha Vantage データを取り込む\n", "\n", "データを取り込むライブラリとして Alpha Vantage を無料で利用する。\n", "\n", "API Key を入手: \n", "\n", "https://www.alphavantage.co/suppoert/#api-key\n", "\n", "Alpha Vantage API Documentation: \n", "\n", "https://www.alphavantage.co/documentation/\n", "\n", "pypi Alpha Vantage API Documentation:\n", "\n", "https://pypi.org/project/alpha-vantage/\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "executionInfo": { "elapsed": 3818, "status": "ok", "timestamp": 1649917344457, "user": { "displayName": "Yoshihisa Nitta", "userId": "15888006800030996813" }, "user_tz": -540 }, "id": "Ae4axhmmbJ6b" }, "outputs": [], "source": [ "! pip -q install alpha_vantage" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "if not is_colab:\n", " ! pip -q install numpy matplotlib seaborn" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "if not is_colab:\n", " ! pip -q install scikit-learn\n", " ! pip -q install pandas" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "if not is_colab:\n", " ! pip -q install plotly\n" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "executionInfo": { "elapsed": 419, "status": "ok", "timestamp": 1649917349769, "user": { "displayName": "Yoshihisa Nitta", "userId": "15888006800030996813" }, "user_tz": -540 }, "id": "nCZ8wkAfgh7U" }, "outputs": [], "source": [ "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.tree import DecisionTreeClassifier\n", "from datetime import date\n", "from dateutil.relativedelta import relativedelta\n", "from dateutil import parser\n", "import numpy as np\n", "import math\n", "from alpha_vantage.techindicators import TechIndicators\n", "\n", "import plotly.express as px\n", "import plotly.figure_factory as ff\n", "import plotly.graph_objects as go\n", "import seaborn as sns" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "executionInfo": { "elapsed": 255, "status": "ok", "timestamp": 1649917352445, "user": { "displayName": "Yoshihisa Nitta", "userId": "15888006800030996813" }, "user_tz": -540 }, "id": "_FTR90QmhrM-" }, "outputs": [], "source": [ "plt.style.use('fivethirtyeight')\n", "plt.rcParams['figure.figsize'] = [24, 8]\n", "API_KEY = 'Your API Key' # YOU MUST CHANGE THIS." ] }, { "cell_type": "markdown", "metadata": { "id": "C4HybDjfzlK_" }, "source": [ "### Alpha Vantage から取得したデータから DataFrame を作る\n", "\n", "" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "executionInfo": { "elapsed": 249, "status": "ok", "timestamp": 1649918452910, "user": { "displayName": "Yoshihisa Nitta", "userId": "15888006800030996813" }, "user_tz": -540 }, "id": "OxnbVxUFvNZz" }, "outputs": [], "source": [ "symbol = \"SNAP\"\n", "# symbol = \"TWTR\"" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 710 }, "executionInfo": { "elapsed": 953, "status": "ok", "timestamp": 1649918152487, "user": { "displayName": "Yoshihisa Nitta", "userId": "15888006800030996813" }, "user_tz": -540 }, "id": "1qz_R-NJyO94", "outputId": "1bd5d3ec-948c-4fbd-9941-1e0419c8ec5b" }, "outputs": [ { "data": { "text/html": [ "
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1. open2. high3. low4. close5. volumePrice_Up_From_PreviousDayToday_Price_UpTomorrow_Price_UpPercentageSomeDaysAfterClosingPrice
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" ], "text/plain": [ " 1. open 2. high 3. low 4. close 5. volume \\\n", "date \n", "2022-04-13 33.66 34.92 33.3401 34.68 17181342.0 \n", "2022-04-12 34.72 35.95 33.1900 33.57 19538493.0 \n", "2022-04-11 34.65 35.75 34.0530 34.40 20890421.0 \n", "2022-04-08 35.88 36.83 35.4100 35.67 21718400.0 \n", "2022-04-07 36.39 37.30 34.5800 36.25 21490374.0 \n", "... ... ... ... ... ... \n", "2017-03-08 22.03 23.43 21.3100 22.81 49834423.0 \n", "2017-03-07 22.21 22.50 20.6400 21.44 71899652.0 \n", "2017-03-06 28.17 28.25 23.7700 23.77 72938848.0 \n", "2017-03-03 26.39 29.44 26.0600 27.09 148227379.0 \n", "2017-03-02 24.00 26.05 23.5000 24.48 217109769.0 \n", "\n", " Price_Up_From_PreviousDay Today_Price_Up Tomorrow_Price_Up \\\n", "date \n", "2022-04-13 1 1 0 \n", "2022-04-12 0 0 1 \n", "2022-04-11 0 0 0 \n", "2022-04-08 0 0 0 \n", "2022-04-07 0 0 0 \n", "... ... ... ... \n", "2017-03-08 1 1 0 \n", "2017-03-07 0 0 1 \n", "2017-03-06 0 0 0 \n", "2017-03-03 1 1 0 \n", "2017-03-02 0 1 1 \n", "\n", " Percentage SomeDaysAfterClosingPrice \n", "date \n", "2022-04-13 0.033065 NaN \n", "2022-04-12 -0.024128 NaN \n", "2022-04-11 -0.035604 NaN \n", "2022-04-08 -0.016000 NaN \n", "2022-04-07 -0.006032 NaN \n", "... ... ... \n", "2017-03-08 0.063899 21.82 \n", "2017-03-07 -0.098023 20.38 \n", "2017-03-06 -0.122554 19.93 \n", "2017-03-03 0.106618 19.54 \n", "2017-03-02 NaN 19.89 \n", "\n", "[1290 rows x 10 columns]" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from alpha_vantage.timeseries import TimeSeries\n", "from pprint import pprint\n", "ti = TechIndicators(key=API_KEY, output_format='pandas')\n", "ts = TimeSeries(key=API_KEY, output_format='pandas')\n", "data, meta_data = ts.get_daily(symbol=symbol, outputsize='full')\n", "data['Price_Up_From_PreviousDay'] = np.where(data['4. close'] > data['4. close'].shift(-1), 1, 0)\n", "data['Today_Price_Up'] = np.where(data['4. close'] > data['1. open'], 1, 0)\n", "data['Tomorrow_Price_Up'] = np.where(data['4. close'].shift(1) > data['4. close'], 1, 0)\n", "data['Percentage'] = (data['4. close'] - data['4. close'].shift(-1)) / data['4. close'].shift(-1)\n", "data['SomeDaysAfterClosingPrice'] = data['4. close'].shift(10) # X days later price\n", "data" ] }, { "cell_type": "markdown", "metadata": { "id": "Mmb1VGBb0ao1" }, "source": [ "## 移動平均 (Moving Average)\n", "\n", "### 買い時の指標のひとつ\n", "\n", "ゴールデンクロス ... 短期間の移動平均が、長期間の移動平均を抜いた時" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 596 }, "executionInfo": { "elapsed": 3539, "status": "ok", "timestamp": 1649918917587, "user": { "displayName": "Yoshihisa Nitta", "userId": "15888006800030996813" }, "user_tz": -540 }, "id": "SRXjxaVEyQe3", "outputId": "c5c7c5be-e868-40a4-b16e-6b4687cf59c2" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Golden Cross Point: 36\n", "Actual Price Up: 18\n", "Percentage: 50.0%\n" ] }, { "data": { "text/html": [ " \n", " " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "name": "4. close", "type": "scatter", "x": [ "2022-04-13T00:00:00", "2022-04-12T00:00:00", "2022-04-11T00:00:00", "2022-04-08T00:00:00", "2022-04-07T00:00:00", "2022-04-06T00:00:00", "2022-04-05T00:00:00", "2022-04-04T00:00:00", "2022-04-01T00:00:00", "2022-03-31T00:00:00", "2022-03-30T00:00:00", "2022-03-29T00:00:00", "2022-03-28T00:00:00", "2022-03-25T00:00:00", "2022-03-24T00:00:00", "2022-03-23T00:00:00", "2022-03-22T00:00:00", "2022-03-21T00:00:00", "2022-03-18T00:00:00", "2022-03-17T00:00:00", "2022-03-16T00:00:00", "2022-03-15T00:00:00", "2022-03-14T00:00:00", "2022-03-11T00:00:00", "2022-03-10T00:00:00", "2022-03-09T00:00:00", "2022-03-08T00:00:00", "2022-03-07T00:00:00", "2022-03-04T00:00:00", "2022-03-03T00:00:00", "2022-03-02T00:00:00", "2022-03-01T00:00:00", "2022-02-28T00:00:00", 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"title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 } } }, "title": { "text": "Prices" }, "xaxis": { "anchor": "y", "domain": [ 0, 1 ] }, "yaxis": { "anchor": "x", "domain": [ 0, 1 ] } } }, "text/html": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Moving Average 5 and 20\n", "dataSMA_short, meta_data_sma_short = ti.get_sma(symbol=symbol, time_period=5)\n", "dataSMA_short.columns = ['SMA_short']\n", "data = data.merge(dataSMA_short, left_index=True, right_index=True)\n", "\n", "dataSMA_long, meta_data_sma_long = ti.get_sma(symbol=symbol, time_period=20)\n", "dataSMA_long.columns = ['SMA_long']\n", "data = data.merge(dataSMA_long, left_index=True, right_index=True)\n", "\n", "data['SMATrend'] = np.where(data['SMA_short'] > data['SMA_long'], 1, 0) # Up trend is 1,, Down Trend is 0\n", "data['GoldenCrossHappened'] = np.where(data['SMATrend'] > data['SMATrend'].shift(-1), 1, 0)\n", "#pd.set_option('display.max_rows', None)\n", "\n", "# theory 1\n", "# we should see price up after seeing golden cross between SMA 5 and SMA20.\n", "# this is giving as some days later price as example\n", "turningPointChange = 0\n", "priceActuallyUp = 0\n", "for index, row in data.iterrows():\n", " if row['GoldenCrossHappened'] == 1:\n", " turningPointChange += 1\n", " if row['4. close'] < row['SomeDaysAfterClosingPrice']:\n", " priceActuallyUp += 1\n", "\n", "print(\"Golden Cross Point: \" + str(turningPointChange))\n", "print(\"Actual Price Up: \" + str(priceActuallyUp))\n", "print(\"Percentage: \" + str((priceActuallyUp/turningPointChange)*100) + \"%\")\n", "\n", "def interactive_plot(df, title):\n", " fig = px.line(title = title)\n", " \n", " # Loop through each stock (while ignoring time columns with index 0)\n", " for i in df.columns[:]:\n", " if i == \"4. close\" or i == \"SMA_short\" or i == \"SMA_long\":\n", " fig.add_scatter(x = df.index, y = df[i], name = i) # add a new Scatter trace\n", "\n", " fig.show()\n", "\n", "interactive_plot(data, 'Prices')" ] }, { "cell_type": "markdown", "metadata": { "id": "llUGqpVT2w4d" }, "source": [ "## ボリンジャーバンド\n", "\n", "「価格の大半がボリンジャーバンド (幅) の中に収まる」が、バンドから離れた価格は10日後に戻るか?を検証する。" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 491 }, "executionInfo": { "elapsed": 1233, "status": "error", "timestamp": 1649918929150, "user": { "displayName": "Yoshihisa Nitta", "userId": "15888006800030996813" }, "user_tz": -540 }, "id": "XIr4UTPU1KuC", "outputId": "15e7bbf6-a35f-458b-8c37-52c659936189" }, "outputs": [ { "ename": "ValueError", "evalue": "Thank you for using Alpha Vantage! This is a premium endpoint and there are multiple ways to unlock premium endpoints: (1) become a holder of Alpha Vantage Coin (AVC), an Ethereum-based cryptocurrency that provides various utility & governance functions within the Alpha Vantage ecosystem (AVC mining guide: https://www.alphatournament.com/avc_mining_guide/) to unlock all premium endpoints, (2) subscribe to any of the premium plans at https://www.alphavantage.co/premium/ to instantly unlock all premium endpoints", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", "Input \u001b[1;32mIn [14]\u001b[0m, in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[38;5;66;03m#BB\u001b[39;00m\n\u001b[1;32m----> 2\u001b[0m dataBB, meta_data_bb \u001b[38;5;241m=\u001b[39m \u001b[43mti\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_bbands\u001b[49m\u001b[43m(\u001b[49m\u001b[43msymbol\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msymbol\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 3\u001b[0m data \u001b[38;5;241m=\u001b[39m data\u001b[38;5;241m.\u001b[39mmerge(dataBB, left_index\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, right_index\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[0;32m 4\u001b[0m data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mPriceBelowLowerBB\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mwhere(data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mReal Lower Band\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m>\u001b[39m data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m4. close\u001b[39m\u001b[38;5;124m'\u001b[39m], \u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m0\u001b[39m)\n", "File \u001b[1;32mD:\\sys\\Anaconda3\\envs\\stock\\lib\\site-packages\\alpha_vantage\\alphavantage.py:218\u001b[0m, in \u001b[0;36mAlphaVantage._output_format.._format_wrapper\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 216\u001b[0m \u001b[38;5;129m@wraps\u001b[39m(func)\n\u001b[0;32m 217\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_format_wrapper\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m--> 218\u001b[0m call_response, data_key, meta_data_key \u001b[38;5;241m=\u001b[39m func(\n\u001b[0;32m 219\u001b[0m \u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m 220\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mjson\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moutput_format\u001b[38;5;241m.\u001b[39mlower() \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpandas\u001b[39m\u001b[38;5;124m'\u001b[39m \\\n\u001b[0;32m 221\u001b[0m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moutput_format\u001b[38;5;241m.\u001b[39mlower():\n\u001b[0;32m 222\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m data_key \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", "File \u001b[1;32mD:\\sys\\Anaconda3\\envs\\stock\\lib\\site-packages\\alpha_vantage\\alphavantage.py:160\u001b[0m, in \u001b[0;36mAlphaVantage._call_api_on_func.._call_wrapper\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 158\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 159\u001b[0m url \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;241m.\u001b[39mformat(url, apikey_parameter)\n\u001b[1;32m--> 160\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_handle_api_call\u001b[49m\u001b[43m(\u001b[49m\u001b[43murl\u001b[49m\u001b[43m)\u001b[49m, data_key, meta_data_key\n", "File \u001b[1;32mD:\\sys\\Anaconda3\\envs\\stock\\lib\\site-packages\\alpha_vantage\\alphavantage.py:361\u001b[0m, in \u001b[0;36mAlphaVantage._handle_api_call\u001b[1;34m(self, url)\u001b[0m\n\u001b[0;32m 359\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(json_response[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mError Message\u001b[39m\u001b[38;5;124m\"\u001b[39m])\n\u001b[0;32m 360\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mInformation\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01min\u001b[39;00m json_response \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtreat_info_as_error:\n\u001b[1;32m--> 361\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(json_response[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mInformation\u001b[39m\u001b[38;5;124m\"\u001b[39m])\n\u001b[0;32m 362\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNote\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01min\u001b[39;00m json_response \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtreat_info_as_error:\n\u001b[0;32m 363\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(json_response[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNote\u001b[39m\u001b[38;5;124m\"\u001b[39m])\n", "\u001b[1;31mValueError\u001b[0m: Thank you for using Alpha Vantage! This is a premium endpoint and there are multiple ways to unlock premium endpoints: (1) become a holder of Alpha Vantage Coin (AVC), an Ethereum-based cryptocurrency that provides various utility & governance functions within the Alpha Vantage ecosystem (AVC mining guide: https://www.alphatournament.com/avc_mining_guide/) to unlock all premium endpoints, (2) subscribe to any of the premium plans at https://www.alphavantage.co/premium/ to instantly unlock all premium endpoints" ] } ], "source": [ "#BB\n", "dataBB, meta_data_bb = ti.get_bbands(symbol=symbol)\n", "data = data.merge(dataBB, left_index=True, right_index=True)\n", "data['PriceBelowLowerBB'] = np.where(data['Real Lower Band'] > data['4. close'], 1, 0)\n", "data['TouchDownOnLowerBBHappened'] = np.where(data['PriceBelowLowerBB'] > data['PriceBelowLowerBB'].shift(-1), 1, 0)\n", "# theory 2\n", "# we should see price up after reaching lower BB.\n", "# this is giving as some days later price as example\n", "turningPointChange = 0\n", "priceActuallyUp = 0\n", "for index, row in data.iterrows():\n", " if row['TouchDownOnLowerBBHappened'] == 1:\n", " turningPointChange += 1\n", " if row['4. close'] < row['SomeDaysAfterClosingPrice']:\n", " priceActuallyUp += 1\n", "\n", "print(\"LowerBBTouched Point: \" + str(turningPointChange))\n", "print(\"Actual Price Up: \" + str(priceActuallyUp))\n", "print(\"Percentage: \" + str((priceActuallyUp/turningPointChange)*100) + \"%\")\n", "\n", "def interactive_plot(df, title):\n", " fig = px.line(title = title)\n", " \n", " # Loop through each stock (while ignoring time columns with index 0)\n", " for i in df.columns[:]:\n", " if i == \"4. close\" or i == \"Real Middle Band\" or i == \"Real Upper Band\" or i == \"Real Lower Band\":\n", " fig.add_scatter(x = df.index, y = df[i], name = i) # add a new Scatter trace\n", "\n", " fig.show()\n", "\n", "interactive_plot(data, 'Prices')" ] }, { "cell_type": "markdown", "metadata": { "id": "97ZOJZ__3Sax" }, "source": [ "## RSI (相対力指数)\n", "\n", "終値ベースで、直近の一定期間において上昇変動と下降変動のどちらの勢いが強いか。\n" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "executionInfo": { "elapsed": 702, "status": "ok", "timestamp": 1649919141936, "user": { "displayName": "Yoshihisa Nitta", "userId": "15888006800030996813" }, "user_tz": -540 }, "id": "p3BmYeX11OFo", "outputId": "ef340066-b27f-45e2-d725-12730b5a0b6c" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Price Touch RSI30 Point: 12\n", "Actual Price Up: 5\n", "Percentage: 41.66666666666667%\n" ] } ], "source": [ "# RSI\n", "dataRSI, meta_data_rsi = ti.get_rsi(symbol=symbol)\n", "data = data.merge(dataRSI, left_index=True, right_index=True)\n", "data['RSITouched30'] = np.where(data['RSI'] < 30, 1, 0)\n", "data['TouchDownRSI30Happened'] = np.where(data['RSITouched30'] > data['RSITouched30'].shift(-1), 1, 0)\n", "# theory 3\n", "# we should see price up after reaching RSI30.\n", "# this is giving as some days later price as example\n", "turningPointChange = 0\n", "priceActuallyUp = 0\n", "for index, row in data.iterrows():\n", " if row['TouchDownRSI30Happened'] == 1:\n", " turningPointChange += 1\n", " if row['4. close'] < row['SomeDaysAfterClosingPrice']:\n", " priceActuallyUp += 1\n", "\n", "print(\"Price Touch RSI30 Point: \" + str(turningPointChange))\n", "print(\"Actual Price Up: \" + str(priceActuallyUp))\n", "print(\"Percentage: \" + str((priceActuallyUp/turningPointChange)*100) + \"%\")" ] }, { "cell_type": "markdown", "metadata": { "id": "-y00zIWp3wR6" }, "source": [ "## MACD (Moving Average Convergence Divergence)\n", "\n", "MACD のラインがシグナルを抜いた上で、ヒストグラムも上昇である10日後に価格は上昇するか?" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 473 }, "executionInfo": { "elapsed": 702, "status": "error", "timestamp": 1649919156472, "user": { "displayName": "Yoshihisa Nitta", "userId": "15888006800030996813" }, "user_tz": -540 }, "id": "woSMUhDi2CLi", "outputId": "a5d4c4ea-fb83-419a-f8fe-4c88120bb5da" }, "outputs": [ { "ename": "ValueError", "evalue": "Thank you for using Alpha Vantage! This is a premium endpoint and there are multiple ways to unlock premium endpoints: (1) become a holder of Alpha Vantage Coin (AVC), an Ethereum-based cryptocurrency that provides various utility & governance functions within the Alpha Vantage ecosystem (AVC mining guide: https://www.alphatournament.com/avc_mining_guide/) to unlock all premium endpoints, (2) subscribe to any of the premium plans at https://www.alphavantage.co/premium/ to instantly unlock all premium endpoints", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", "Input \u001b[1;32mIn [16]\u001b[0m, in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[38;5;66;03m#MACD\u001b[39;00m\n\u001b[1;32m----> 2\u001b[0m dataMACD, meta_data_macd \u001b[38;5;241m=\u001b[39m \u001b[43mti\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_macd\u001b[49m\u001b[43m(\u001b[49m\u001b[43msymbol\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msymbol\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minterval\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mdaily\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m 3\u001b[0m data \u001b[38;5;241m=\u001b[39m data\u001b[38;5;241m.\u001b[39mmerge(dataMACD, left_index\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, right_index\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[0;32m 4\u001b[0m data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mMACDOverSignal\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mwhere(data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mMACD\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m>\u001b[39m data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mMACD_Signal\u001b[39m\u001b[38;5;124m'\u001b[39m], \u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m0\u001b[39m)\n", "File \u001b[1;32mD:\\sys\\Anaconda3\\envs\\stock\\lib\\site-packages\\alpha_vantage\\alphavantage.py:218\u001b[0m, in \u001b[0;36mAlphaVantage._output_format.._format_wrapper\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 216\u001b[0m \u001b[38;5;129m@wraps\u001b[39m(func)\n\u001b[0;32m 217\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_format_wrapper\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m--> 218\u001b[0m call_response, data_key, meta_data_key \u001b[38;5;241m=\u001b[39m func(\n\u001b[0;32m 219\u001b[0m \u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m 220\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mjson\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moutput_format\u001b[38;5;241m.\u001b[39mlower() \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpandas\u001b[39m\u001b[38;5;124m'\u001b[39m \\\n\u001b[0;32m 221\u001b[0m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moutput_format\u001b[38;5;241m.\u001b[39mlower():\n\u001b[0;32m 222\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m data_key \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", "File \u001b[1;32mD:\\sys\\Anaconda3\\envs\\stock\\lib\\site-packages\\alpha_vantage\\alphavantage.py:160\u001b[0m, in \u001b[0;36mAlphaVantage._call_api_on_func.._call_wrapper\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 158\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 159\u001b[0m url \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;241m.\u001b[39mformat(url, apikey_parameter)\n\u001b[1;32m--> 160\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_handle_api_call\u001b[49m\u001b[43m(\u001b[49m\u001b[43murl\u001b[49m\u001b[43m)\u001b[49m, data_key, meta_data_key\n", "File \u001b[1;32mD:\\sys\\Anaconda3\\envs\\stock\\lib\\site-packages\\alpha_vantage\\alphavantage.py:361\u001b[0m, in \u001b[0;36mAlphaVantage._handle_api_call\u001b[1;34m(self, url)\u001b[0m\n\u001b[0;32m 359\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(json_response[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mError Message\u001b[39m\u001b[38;5;124m\"\u001b[39m])\n\u001b[0;32m 360\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mInformation\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01min\u001b[39;00m json_response \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtreat_info_as_error:\n\u001b[1;32m--> 361\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(json_response[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mInformation\u001b[39m\u001b[38;5;124m\"\u001b[39m])\n\u001b[0;32m 362\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNote\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01min\u001b[39;00m json_response \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtreat_info_as_error:\n\u001b[0;32m 363\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(json_response[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNote\u001b[39m\u001b[38;5;124m\"\u001b[39m])\n", "\u001b[1;31mValueError\u001b[0m: Thank you for using Alpha Vantage! This is a premium endpoint and there are multiple ways to unlock premium endpoints: (1) become a holder of Alpha Vantage Coin (AVC), an Ethereum-based cryptocurrency that provides various utility & governance functions within the Alpha Vantage ecosystem (AVC mining guide: https://www.alphatournament.com/avc_mining_guide/) to unlock all premium endpoints, (2) subscribe to any of the premium plans at https://www.alphavantage.co/premium/ to instantly unlock all premium endpoints" ] } ], "source": [ "#MACD\n", "dataMACD, meta_data_macd = ti.get_macd(symbol=symbol, interval='daily')\n", "data = data.merge(dataMACD, left_index=True, right_index=True)\n", "data['MACDOverSignal'] = np.where(data['MACD'] > data['MACD_Signal'], 1, 0)\n", "data['MACDHistUp'] = np.where(data['MACD_Hist'] > data['MACD_Hist'].shift(-2), 1, 0)\n", "data['MACDOverSignalTrendHappened'] = np.where(data['MACDOverSignal'] > data['MACDOverSignal'].shift(-1), 1, 0)\n", "data['MACDHistUpTrendHappened'] = np.where(data['MACDHistUp'] > data['MACDHistUp'].shift(-1), 1, 0)\n", "data['MACDUpTrendHappened'] = np.where((data['MACDOverSignalTrendHappened'] == 1) & (data['MACDHistUpTrendHappened'] == 1), 1, 0)\n", "# theory 4\n", "# we should see price up after reaching MACD Trend is up.\n", "# this is giving as some days later price as example\n", "turningPointChange = 0\n", "priceActuallyUp = 0\n", "for index, row in data.iterrows():\n", " if row['MACDUpTrendHappened'] == 1:\n", " turningPointChange += 1\n", " if row['4. close'] < row['SomeDaysAfterClosingPrice']:\n", " priceActuallyUp += 1\n", "\n", "print(\"MACD Uptrend Point: \" + str(turningPointChange))\n", "print(\"Actual Price Up: \" + str(priceActuallyUp))\n", "print(\"Percentage: \" + str((priceActuallyUp/turningPointChange)*100) + \"%\")" ] }, { "cell_type": "markdown", "metadata": { "id": "nlU2m8Xc4R4a" }, "source": [ "# 追加検証\n", "## 曜日アノマリーを分析\n", "\n", "「金曜日は売られやすく、月曜日は買われやすいか」を検証する\n", "\n", "今回のデータでは「金曜日が最も買われやすい」という結論が出た。" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "executionInfo": { "elapsed": 665, "status": "ok", "timestamp": 1649919170330, "user": { "displayName": "Yoshihisa Nitta", "userId": "15888006800030996813" }, "user_tz": -540 }, "id": "yTXKlTZC2Fub", "outputId": "bd469b4a-ec43-45d3-c818-f226cf439f0b" }, "outputs": [ { "data": { "text/plain": [ "{'Monday': 107,\n", " 'Tuesday': 123,\n", " 'Wednesday': 127,\n", " 'Thursday': 131,\n", " 'Friday': 136}" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Calendar data\n", "# which week of day get higher price than previous day\n", "data = data.sort_index(ascending=1)\n", "count = {'Monday': 0, 'Tuesday': 0, 'Wednesday': 0, 'Thursday': 0,'Friday': 0}\n", "for index, row in data.iterrows():\n", " if index.day_name() == 'Monday' and int(row['Price_Up_From_PreviousDay']) == 1:\n", " count['Monday'] += 1\n", " elif index.day_name() == 'Tuesday' and int(row['Price_Up_From_PreviousDay']) == 1:\n", " count['Tuesday'] += 1\n", " elif index.day_name() == 'Wednesday' and int(row['Price_Up_From_PreviousDay']) == 1:\n", " count['Wednesday'] += 1\n", " elif index.day_name() == 'Thursday' and int(row['Price_Up_From_PreviousDay']) == 1:\n", " count['Thursday'] += 1 \n", " elif index.day_name() == 'Friday' and int(row['Price_Up_From_PreviousDay']) == 1:\n", " count['Friday'] += 1 \n", "\n", "count " ] }, { "cell_type": "markdown", "metadata": { "id": "yT7JcVMg5lRz" }, "source": [ "前日も翌日も連続して株価が上がる曜日を調べる。\n", "\n", "今回のデータでは木曜日が最も株価が上がっている。" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "executionInfo": { "elapsed": 270, "status": "ok", "timestamp": 1649920115178, "user": { "displayName": "Yoshihisa Nitta", "userId": "15888006800030996813" }, "user_tz": -540 }, "id": "HOxCkMiv2JHd", "outputId": "a86f2031-ed26-4bee-f19d-ed7c96c38552" }, "outputs": [ { "data": { "text/plain": [ "{'Monday': 43, 'Tuesday': 59, 'Wednesday': 67, 'Thursday': 69, 'Friday': 68}" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Check which day of week get previous day and next day also got up\n", "data = data.sort_index(ascending=1)\n", "series_count = {'Monday': 0, 'Tuesday': 0, 'Wednesday': 0, 'Thursday': 0,'Friday': 0}\n", "for index, row in data.iterrows():\n", " if index.day_name() == 'Monday' and int(row['Price_Up_From_PreviousDay']) == 1 and int(row['Tomorrow_Price_Up']) == 1:\n", " series_count['Monday'] += 1\n", " elif index.day_name() == 'Tuesday' and int(row['Price_Up_From_PreviousDay']) == 1 and int(row['Tomorrow_Price_Up']) == 1:\n", " series_count['Tuesday'] += 1\n", " elif index.day_name() == 'Wednesday' and int(row['Price_Up_From_PreviousDay']) == 1 and int(row['Tomorrow_Price_Up']) == 1:\n", " series_count['Wednesday'] += 1\n", " elif index.day_name() == 'Thursday' and int(row['Price_Up_From_PreviousDay']) == 1 and int(row['Tomorrow_Price_Up']) == 1:\n", " series_count['Thursday'] += 1 \n", " elif index.day_name() == 'Friday' and int(row['Price_Up_From_PreviousDay']) == 1 and int(row['Tomorrow_Price_Up']) == 1:\n", " series_count['Friday'] += 1 \n", "series_count " ] }, { "cell_type": "markdown", "metadata": { "id": "_KZezqOs7yYq" }, "source": [ "前日比からの暴騰率を足し合わせた結果が、最も上昇しやすい曜日を調べる。\n", "\n", "今回のデータでは、金曜日のスコアが一番よい。" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "executionInfo": { "elapsed": 283, "status": "ok", "timestamp": 1649920202168, "user": { "displayName": "Yoshihisa Nitta", "userId": "15888006800030996813" }, "user_tz": -540 }, "id": "Hh0XnuzZ2MbK", "outputId": "6d68b381-4941-434a-9c5b-1e8ead267fbe" }, "outputs": [ { "data": { "text/plain": [ "{'Monday': -0.37827719589172787,\n", " 'Tuesday': 0.06393317369109724,\n", " 'Wednesday': 0.23639491887426275,\n", " 'Thursday': 0.08800158758023528,\n", " 'Friday': 0.6133976315548666}" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# which week of day get higher average raise\n", "percentageUp = {'Monday': 0, 'Tuesday': 0, 'Wednesday': 0, 'Thursday': 0,'Friday': 0}\n", "total = {'Monday': 0, 'Tuesday': 0, 'Wednesday': 0, 'Thursday': 0,'Friday': 0}\n", "for index, row in data.iterrows():\n", " if not math.isnan(row['Percentage']):\n", " if index.day_name() == 'Monday':\n", " percentageUp['Monday'] += row['Percentage']\n", " total['Monday'] += 1\n", " elif index.day_name() == 'Tuesday':\n", " percentageUp['Tuesday'] += row['Percentage']\n", " total['Tuesday'] += 1\n", " elif index.day_name() == 'Wednesday':\n", " percentageUp['Wednesday'] += row['Percentage']\n", " total['Wednesday'] += 1\n", " elif index.day_name() == 'Thursday':\n", " percentageUp['Thursday'] += row['Percentage'] \n", " total['Thursday'] += 1\n", " elif index.day_name() == 'Friday':\n", " percentageUp['Friday'] += row['Percentage'] \n", " total['Friday'] += 1\n", "\n", "percentageUp['Monday'] = percentageUp['Monday']/ total['Monday']*100\n", "percentageUp['Tuesday'] = percentageUp['Tuesday']/ total['Tuesday']*100\n", "percentageUp['Wednesday'] = percentageUp['Wednesday']/ total['Wednesday']*100\n", "percentageUp['Thursday'] = percentageUp['Thursday']/ total['Thursday']*100\n", "percentageUp['Friday'] = percentageUp['Friday']/ total['Friday']*100\n", "percentageUp" ] }, { "cell_type": "markdown", "metadata": { "id": "EZgy-oOY8QgV" }, "source": [ "3 % 以上上昇した曜日をカウントする。" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "executionInfo": { "elapsed": 260, "status": "ok", "timestamp": 1649919204844, "user": { "displayName": "Yoshihisa Nitta", "userId": "15888006800030996813" }, "user_tz": -540 }, "id": "X8UXuXZ52O0W", "outputId": "374f1300-c743-46fe-c96a-f5d59eda1d12" }, "outputs": [ { "data": { "text/plain": [ "{'Monday': 33, 'Tuesday': 41, 'Wednesday': 42, 'Thursday': 46, 'Friday': 50}" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# which week of day get more than 3% raise\n", "\n", "total = {'Monday': 0, 'Tuesday': 0, 'Wednesday': 0, 'Thursday': 0,'Friday': 0}\n", "for index, row in data.iterrows():\n", " if not math.isnan(row['Percentage']) and row['Percentage'] > 0.03:\n", " if index.day_name() == 'Monday':\n", "\n", " total['Monday'] += 1\n", " elif index.day_name() == 'Tuesday':\n", "\n", " total['Tuesday'] += 1\n", " elif index.day_name() == 'Wednesday':\n", "\n", " total['Wednesday'] += 1\n", " elif index.day_name() == 'Thursday':\n", " \n", " total['Thursday'] += 1\n", " elif index.day_name() == 'Friday':\n", "\n", " total['Friday'] += 1\n", "\n", "total" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "executionInfo": { "elapsed": 435, "status": "ok", "timestamp": 1649920801477, "user": { "displayName": "Yoshihisa Nitta", "userId": "15888006800030996813" }, "user_tz": -540 }, "id": "1Wgo1pT12Rpo", "outputId": "5d1f6530-6a08-4a0d-dd6a-23ff721a3208" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2022-02-04 00:00:00\n", "how much percentage they got up 0.5881632653061223\n", "2020-06-23 00:00:00\n", "how many days in row got up 8\n" ] } ], "source": [ "# historically how many days in a row they got price-up\n", "\n", "data = data.sort_index(ascending=1)\n", "\n", "days = 0\n", "max_days = 0\n", "percentage= 0\n", "maxPercentage = 0\n", "recorded_index = \"\"\n", "recorded_index_row = \"\"\n", "for index, row in data.iterrows():\n", " if not math.isnan(row['Percentage']):\n", " if row['Price_Up_From_PreviousDay'] == 1:\n", " days+= 1\n", " if days > max_days:\n", " max_days = days\n", " recorded_index_row = index\n", "\n", " percentage = row['Percentage']\n", " if percentage > maxPercentage:\n", " maxPercentage = percentage\n", " recorded_index = index\n", " else:\n", " #initialize\n", " days = 0 \n", " percentage = 0\n", "\n", "print(recorded_index)\n", "print(\"how much percentage they got up \" + str(maxPercentage))\n", "print(recorded_index_row)\n", "print(\"how many days in row got up \" + str(max_days))" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "executionInfo": { "elapsed": 272, "status": "ok", "timestamp": 1649919229616, "user": { "displayName": "Yoshihisa Nitta", "userId": "15888006800030996813" }, "user_tz": -540 }, "id": "w9sMB_ZS2VCn", "outputId": "c33e27a8-e476-4569-a13d-13e56d866917" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2021-10-22 00:00:00\n", "how much percentage they got down 0.2658767141525762\n", "2022-01-27 00:00:00\n", "how many days in row got down 11\n" ] } ], "source": [ "# historically how many days in a row they got price-down\n", "\n", "data = data.sort_index(ascending=1)\n", "\n", "days = 0\n", "max_days = 0\n", "percentage= 0\n", "maxPercentage = 0\n", "recorded_index = \"\"\n", "recorded_index_row = \"\"\n", "for index, row in data.iterrows():\n", " if not math.isnan(row['Percentage']):\n", " if row['Price_Up_From_PreviousDay'] == 0:\n", " days+= 1\n", " if days > max_days:\n", " max_days = days\n", " recorded_index_row = index\n", "\n", " percentage = row['Percentage']*-1\n", " if percentage > maxPercentage:\n", " maxPercentage = percentage\n", " recorded_index = index\n", " else:\n", "\n", " #initialize\n", " days = 0 \n", " percentage = 0\n", "\n", "\n", "print(recorded_index)\n", "print(\"how much percentage they got down \" + str(maxPercentage))\n", "print(recorded_index_row)\n", "print(\"how many days in row got down \" + str(max_days))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "C8Etasl72XqU" }, "outputs": [], "source": [] } ], "metadata": { "colab": { "authorship_tag": "ABX9TyMqqXcKltRoQnnlOHRh+atp", "collapsed_sections": [], "name": "USStockTechincalAnalysis.ipynb", "provenance": [] }, "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" } }, "nbformat": 4, "nbformat_minor": 1 }