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Author: llSourcell

ARIMA.ipynb

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+
+# coding: utf-8
+
+# In[1]:
+
+
+import requests
+import time
+import datetime
+
+f_name = input("dataset name:")
+f = open(f_name,"a")
+keys = ["price_usd","24h_volume_usd","market_cap_usd","available_supply","total_supply","percent_change_1h","percent_change_24h","percent_change_7d"]
+vals = [0]*len(keys)
+
+while True:
+    data = requests.get("https://api.coinmarketcap.com/v1/ticker/bitcoin/").json()[0]
+    #bstamp = requests.get("https://www.bitstamp.net/api/v2/ticker/btcusd/").json() 
+    bkc = requests.get("https://blockchain.info/ticker").json()
+    for d in data.keys():
+        if d in keys:
+            indx = keys.index(d)
+            vals[indx] = data[d]
+    for val in vals:
+        f.write(val+",")
+      
+    #f.write("{},{},".format(bstamp["volume"],bstamp["vwap"]))
+    f.write("{},{},{}".format(bkc["USD"]["sell"],bkc["USD"]["buy"],bkc["USD"]["15m"]))
+    f.write(","+datetime.datetime.now().strftime("%y-%m-%d-%H-%M"))
+    f.write("\n")
+    f.flush()
+    time.sleep(60)
+

ARIMA_v2.ipynb

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+# import tweepy library for twitter api access and textblob libary for sentiment analysis
+import csv
+import tweepy
+import numpy as np
+from textblob import TextBlob
+import datetime
+import time
+
+def main():
+
+    # set twitter api credentials
+    consumer_key= 'MOe623rxcqck6x8y5XhzK8MJT'
+    consumer_secret= 'mcBq9Km1f3OYERRD6vKmOfWSgCjsqzXAreIsn8klxAtPIo40E7'
+    access_token='913787859630460928-RXF8NVN3gGbxD64NCZ7wBma5M2WPwlv'
+    access_token_secret='P4UU9I2DimdnUown2EM6p4WZ0ftdPNsysDNW6xGh0Ts4f'
+
+    # set path of csv file to save sentiment stats
+    path = 'live_tweet.csv'
+    f = open(path,"a")
+    f1 = open('tweet_data','a')
+    # access twitter api via tweepy methods
+    auth = tweepy.OAuthHandler(consumer_key, consumer_secret)
+    auth.set_access_token(access_token, access_token_secret)
+    twitter_api = tweepy.API(auth)
+
+    while True:
+
+        # fetch tweets by keywords
+        tweets = twitter_api.search(q=['bitcoin, price, crypto'], count=100)
+
+        # get polarity
+        polarity = get_polarity(tweets,f1)
+        sentiment = np.mean(polarity)
+
+        # save sentiment data to csv file
+        f.write(str(sentiment))
+        f.write(","+datetime.datetime.now().strftime("%y-%m-%d-%H-%M"))
+        f.write("\n")
+        f.flush()
+        time.sleep(60)
+    
+
+def get_polarity(tweets,f):
+    # run polarity analysis on tweets
+
+    tweet_polarity = []
+
+    for tweet in tweets:
+        f.write(tweet.text+'\n')
+        analysis = TextBlob(tweet.text)
+        tweet_polarity.append(analysis.sentiment.polarity)
+
+    return tweet_polarity
+
+main()