{"id":1919,"date":"2018-02-24T22:06:55","date_gmt":"2018-02-24T22:06:55","guid":{"rendered":"http:\/\/intelligentonlinetools.com\/blog\/?p=1919"},"modified":"2018-03-08T01:29:12","modified_gmt":"2018-03-08T01:29:12","slug":"prediction-next-stock-market-correction","status":"publish","type":"post","link":"http:\/\/intelligentonlinetools.com\/blog\/2018\/02\/24\/prediction-next-stock-market-correction\/","title":{"rendered":"Prediction on Next Stock Market Correction"},"content":{"rendered":"

On Feb. 6, 2018, the stock market officially entered “correction” territory. A stock market correction is defined as a drop of at least 10% or more for an index or stock from its recent high. [1] During one week the stock data prices (closed price) were decreasing for many stocks. Are there any signals that can be used to predict next stock market correction? <\/p>\n

I pulled historical data from 20 stocks selected randomly and then created python program that counts how many stocks (closed price) were decreased, increased or did not change for each day (comparing with previous day). The numbers then converted into percentage. So if all 20 stock closed prices decreased at some day it would be 100%. For now I was just looking at % of decreased stocks per day. Below is the graph for decreasing stocks. Highlighted zone A is when we many decreasing stocks during the correction. <\/p>\n

\"Number
Number of decreasing stocks per day in %<\/figcaption><\/figure>\n

Observations<\/h3>\n

I did not find good strong signal to predict market correction but probably more analysis needed. However before this correction there was some increasing trend for number of stocks that close at lower prices. This is shown below. On this graph the trend line can be viewed as indicator of stock market direction. <\/p>\n

\"Number-of-decreasing-stocks-per-day-before-correction\"
Number of decreasing stocks per day before correction in %<\/figcaption><\/figure>\n

Python Source Code to download Stock Data<\/h3>\n

Here is the script that was used to download data:<\/p>\n

\r\nfrom pandas_datareader import data as pdr \r\nimport time   \r\n\r\n# put below actual symbols as many as you need\r\nsymbols=['XXX','XXX', 'XXX', ...... 'XXX']\r\n \r\n\r\ndef get_data (symbol):\r\n    \r\n    data = pdr.get_data_google(symbol,'1970-01-01','2018-02-19')\r\n    path="C:\\\\Users\\\\stocks\\\\"\r\n    data.to_csv( path + symbol+".csv")\r\n \r\n    return data\r\n\r\n\r\n    \r\nfor symbol in symbols:\r\n        get_data(symbol)    \r\n        time.sleep(7)\r\n<\/pre>\n
Script for Stock Data Analysis<\/h3>\n
Here is the program that takes downloaded data and counts the number of decreased\/increased\/same stocks per day. The results are saved in the file and also plotted. Plots are shown after source code below.<\/p>\n
And here<\/a> is the link to the data output from the below program.<\/p>\n
\r\n# -*- coding: utf-8 -*-\r\n\r\nimport os\r\n\r\npath="C:\\\\Users\\\\stocks\\\\"\r\nfrom datetime import datetime\r\nimport pandas as pd\r\nimport numpy as np\r\n\r\ndef days_between(d1, d2):\r\n    d1 = datetime.strptime(d1, "%Y-%m-%d")\r\n    d2 = datetime.strptime(d2, "%Y-%m-%d")\r\n    print (d1)\r\n    print (d2)\r\n    return abs((d2 - d1).days)\r\n\r\n\r\ni=10000   # index to replace date\r\nj=20      # index for stock symbols\r\nk=5       # other attributes\r\ndata = np.zeros((i,j,k))           \r\nsymbols=[]           \r\n\r\ncount=0        \r\n\r\n# get index of previous trade day\r\n# because there is no trades on weekend or holidays\r\n# need to calculate prvious trade day index instead\r\n# of just subracting 1\r\ndef get_previous_ind(row_ind, col_count ):\r\n    \r\n    k=1\r\n    print (str(row_ind) + "   " + str(col_count))\r\n    while True:\r\n        if  data[row_ind-k][col_count][0] == 1:\r\n            return row_ind-k\r\n        else:\r\n            k=k+1\r\n    \r\n        if k > 1000 :\r\n            print ("ERROR: PREVIOUS ROW IS NOT FOUND")\r\n            return -1\r\n\r\ndates=["" for i in range(10000) ]          \r\n# read the entries\r\nlistOfEntries = os.scandir(path)\r\nfor entry in  listOfEntries: \r\n        \r\n     if entry.is_file():\r\n            print(entry.name)\r\n            stock_data = pd.read_csv (str(path) + str(entry.name))\r\n            symbols.append (entry.name)\r\n\r\n                     \r\n            for index, row in stock_data.iterrows():\r\n                 ind=days_between(row['Date'], "2002-01-01") \r\n                \r\n                 dates[ind] = row['Date']\r\n                 data[ind][count][0] = 1\r\n                 data[ind][count][1] = row['Close']\r\n                 \r\n                 if (index > 1):\r\n                     print(entry.name)\r\n                     prev_ind=get_previous_ind(ind, count)\r\n                     delta= 1000*(row['Close'] - data[prev_ind][count][1])\r\n                     change=0\r\n                     if (delta > 0) :\r\n                          change = 1\r\n                     if (delta < 0) :\r\n                          change = -1\r\n                     data[ind][count][3] = change  \r\n                     data[ind][count][4] = 1   \r\n                \r\n                 \r\n            count=count+1                      \r\n\r\n    \r\nupchange=[0 for i in range(10000)]\r\ndownchange=[0 for i in range(10000)]\r\nzerochange=[0 for i in range(10000)]\r\ndatesnew = ["" for i in range(10000) ]\r\nicount=0\r\nfor i in range(10000):\r\n       total=0 \r\n       for j in range (count):\r\n           \r\n           if data[i][j][4] == 1 :\r\n               datesnew[icount]=dates[i]\r\n               total=total+1\r\n               if (data[i][j][3] ==0):\r\n                       zerochange[icount]=zerochange[icount]+1\r\n               if (data[i][j][3] ==1):\r\n                       upchange[icount]=upchange[icount] + 1\r\n               if (data[i][j][3] == - 1):\r\n                       downchange[icount]=downchange[icount] + 1\r\n         \r\n           \r\n       if (total != 0) :\r\n               upchange[icount]=100* upchange[icount] \/ total\r\n               downchange[icount]=100* downchange[icount] \/ total\r\n               zerochange[icount]=100* zerochange[icount] \/ total    \r\n               print (str(upchange[icount]) + "  " +  str(downchange[icount]) + "  " + str(zerochange[icount]))\r\n               icount=icount+1\r\n\r\n            \r\n\r\ndf=pd.DataFrame({'Date':datesnew, 'upchange':upchange, 'downchange':downchange, 'zerochange':zerochange })\r\nprint (df)\r\ndf.to_csv("changes.csv", encoding='utf-8', index=False)               \r\n            \r\n\r\nimport matplotlib.pyplot as plt\r\n\r\ndownchange=downchange[icount-200:icount]\r\nupchange=upchange[icount-200:icount]\r\nzerochange=zerochange[icount-200:icount]\r\n\r\n\r\n# Two subplots, the axes array is 1-d\r\nf, axarr = plt.subplots(3, sharex=True)\r\naxarr[0].plot(downchange)\r\naxarr[0].set_title('downchange')\r\naxarr[1].plot(upchange)\r\naxarr[1].set_title('upchange')\r\naxarr[2].plot(zerochange)\r\naxarr[2].set_title('zerochange')\r\nplt.show()\r\n<\/pre>\n
\"Number
Number of stocks increasing decreasing same in %<\/figcaption><\/figure>\n
References<\/strong>
\n1. 6 Things You Should Know About a Stock Market Correction<\/a>
\n2. How to Predict the Eventual Stock Market Correction Before Anyone Else <\/a>
\n3. 4 Ways To Predict Market Performance<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"
On Feb. 6, 2018, the stock market officially entered “correction” territory. A stock market correction is defined as a drop of at least 10% or more for an index or stock from its recent high. [1] During one week the stock data prices (closed price) were decreasing for many stocks. Are there any signals that … Read more<\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"jetpack_post_was_ever_published":false,"jetpack_publicize_message":"","jetpack_is_tweetstorm":false,"jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":true,"jetpack_social_options":[]},"categories":[2,10,3,60],"tags":[27,61,62],"jetpack_publicize_connections":[],"yoast_head":"\nPrediction on Next Stock Market Correction - Machine Learning Applications<\/title>\n<meta name=\"description\" content=\"Prediction on next stock market correction using the number of decreasing stocks per day. 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Facebook team have implemented in Prophet two trend models that can cover many applications: a saturating growth model, and a piecewise linear model. [4] With growth model Prophet can be used for prediction growth\/decay - for\u2026","rel":"","context":"In "Machine Learning"","img":{"alt_text":"","src":"https:\/\/i0.wp.com\/intelligentonlinetools.com\/blog\/wp-content\/uploads\/2017\/12\/time-series-analysis-python-300x180.png?resize=350%2C200","width":350,"height":200},"classes":[]},{"id":1682,"url":"http:\/\/intelligentonlinetools.com\/blog\/2017\/12\/26\/prediction-data-stock-price-prophet-report\/","url_meta":{"origin":1919,"position":1},"title":"Prediction Data Stock Prices with Prophet","date":"December 26, 2017","format":false,"excerpt":"In the previous post I showed how to use the Prophet for time series analysis with python. I used Prophet for data stock price prediction. But it was used only for one stock and only for next 10 days. In this post we will select more data and will test\u2026","rel":"","context":"In "Data Mining"","img":{"alt_text":"prediction data stock prices with Prophet - accuracy vs used data range","src":"https:\/\/i0.wp.com\/intelligentonlinetools.com\/blog\/wp-content\/uploads\/2017\/12\/prediction-data-stock-prices-with-Prophet-accuracy-vs-used-data-range-1-e1514396944169.png?resize=350%2C200","width":350,"height":200},"classes":[]},{"id":2117,"url":"http:\/\/intelligentonlinetools.com\/blog\/2018\/06\/16\/fibonacci-stock-trading-using-fibonacci-retracement-stock-market-prediction\/","url_meta":{"origin":1919,"position":2},"title":"Fibonacci Stock Trading – Using Fibonacci Retracement for Stock Market Prediction","date":"June 16, 2018","format":false,"excerpt":"As stated on allstarcharts.com by expert with more than 10 years, Fibonacci Analysis is one of the most valuable and easy to use tools for stock market technical analysis. And Fibonacci tools can be applied to longer-term as well as to short-term. [3] In this post we will take a\u2026","rel":"","context":"In "Fibonacci Numbers"","img":{"alt_text":"Fibonacci numbers","src":"https:\/\/i0.wp.com\/intelligentonlinetools.com\/blog\/wp-content\/uploads\/2018\/06\/fibonacci-1601158_640.png?resize=350%2C200","width":350,"height":200},"classes":[]},{"id":1974,"url":"http:\/\/intelligentonlinetools.com\/blog\/2018\/04\/03\/machine-learning-stock-market-prediction-lstm-keras\/","url_meta":{"origin":1919,"position":3},"title":"Machine Learning Stock Market Prediction with LSTM Keras","date":"April 3, 2018","format":false,"excerpt":"In the previous posts [1,2] I created script for machine learning stock market price on next day prediction. But it was pointed by readers that in stock market prediction, it is more important to know the trend: will the stock go up or down. So I updated the script to\u2026","rel":"","context":"In "Machine Learning"","img":{"alt_text":"Stock Data Prices Prediction with LSTM","src":"https:\/\/i0.wp.com\/intelligentonlinetools.com\/blog\/wp-content\/uploads\/2018\/04\/timeseries_differenced_and_inverted_back.png?resize=350%2C200","width":350,"height":200},"classes":[]},{"id":1783,"url":"http:\/\/intelligentonlinetools.com\/blog\/2018\/01\/19\/machine-learning-stock-prediction-lstm-keras\/","url_meta":{"origin":1919,"position":4},"title":"Machine Learning Stock Prediction with LSTM and Keras","date":"January 19, 2018","format":false,"excerpt":"In this post I will share experiments on machine learning stock prediction with LSTM and Keras with one step ahead. I tried to do first multiple steps ahead with few techniques described in the papers on the web. But I discovered that I need fully understand and test the simplest\u2026","rel":"","context":"In "Machine Learning"","img":{"alt_text":"Forecasting one step ahead LSTM 60","src":"https:\/\/i0.wp.com\/intelligentonlinetools.com\/blog\/wp-content\/uploads\/2018\/01\/Forecasting-one-step-ahead-LSTM-60-3_1_2018.png?resize=350%2C200","width":350,"height":200},"classes":[]},{"id":120,"url":"http:\/\/intelligentonlinetools.com\/blog\/2016\/02\/20\/input-for-stock-data-prediction-algorithms\/","url_meta":{"origin":1919,"position":5},"title":"Input for Stock Data Prediction Algorithms","date":"February 20, 2016","format":false,"excerpt":"What can be used for input to stock data prediction system? In this post we will consider some indicator that often are used for stock data forecasting. The links to information about indicators and how to calculate will be also provided. Moving averages are often used in technical analysis. A\u2026","rel":"","context":"In "Stock data analysis"","img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]}],"_links":{"self":[{"href":"http:\/\/intelligentonlinetools.com\/blog\/wp-json\/wp\/v2\/posts\/1919"}],"collection":[{"href":"http:\/\/intelligentonlinetools.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"http:\/\/intelligentonlinetools.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"http:\/\/intelligentonlinetools.com\/blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"http:\/\/intelligentonlinetools.com\/blog\/wp-json\/wp\/v2\/comments?post=1919"}],"version-history":[{"count":14,"href":"http:\/\/intelligentonlinetools.com\/blog\/wp-json\/wp\/v2\/posts\/1919\/revisions"}],"predecessor-version":[{"id":1971,"href":"http:\/\/intelligentonlinetools.com\/blog\/wp-json\/wp\/v2\/posts\/1919\/revisions\/1971"}],"wp:attachment":[{"href":"http:\/\/intelligentonlinetools.com\/blog\/wp-json\/wp\/v2\/media?parent=1919"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/intelligentonlinetools.com\/blog\/wp-json\/wp\/v2\/categories?post=1919"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/intelligentonlinetools.com\/blog\/wp-json\/wp\/v2\/tags?post=1919"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}