TESLA STOCK ANALYSIS

This analysis explores a relationship amongst stock price, price to sales ratio, twitter sentiment and financial statements for Tesla Stock.

Tesla Data sources

SEC Filings
Twitter
Yahoo Historical Stock Data
Zacks.com

Lessons Learned

Historical twitter data is not easily accessible without a paid license
GMT to PST date conversion
Merging multiple data sources
Plotting with multiple y-axis
Working with various date formats
Understand your underlying data

>>> Financials Section >>>

Pipeline - Begin

# dependencies
import pandas as pd
import seaborn as sns
import numpy as np
import matplotlib.pyplot as plt

from datetime import datetime
from datetime import timedelta

# import and initialize Sentiment Analyzer
from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
analyzer = SentimentIntensityAnalyzer()

# financial data resources
financials = "./Resources/Tesla Financials/Testla Operations Financials.xlsx"
stock_price = "./Resources/Tesla Stock Price/TSLAstock.xlsx"
ps_ratio = "./Resources/Tesla Stock Price/TSLApsratio.xlsx"
twitter = "./Resources/Tesla Twitter/twitter tweets.xlsx"

# twitter data resources
tsla_tweet = pd.read_csv("Resources/tslatweets.csv")
elonmusk_tweet = pd.read_csv("Resources/ElonMusktweets.csv")
tsla_stock = pd.read_csv("Resources/TSLA_stock.csv")

# set df_variable: financial statement
financials_excel = pd.read_excel(financials)
financials_df = financials_excel[["date","operating_income","cf_from_operations"]]

# set df_variabel: stock price
stock_price_excel = pd.read_excel(stock_price)
stock_price_df = stock_price_excel[["date","Close","Volume"]]

# set df_variable: price-to-sales ratio
ps_ratio_xlsx = pd.read_excel(ps_ratio)
ps_ratio_df = ps_ratio_xlsx[["date","P/S Ratio"]]

# set df_variable: twitter sentiment
twitter_csv = pd.read_excel(twitter)
twitter_df = twitter_csv[["date","TSLA_compound","ElonMusk_compound"]]

# merge: financials and stock price (AS: tesla_df_1)
tesla_df_1 = pd.merge(financials_df,stock_price_df, how="outer",on="date")

# merge: tesla_df_1 and ps ratio (AS: tesla_df_2)
tesla_df_2 = pd.merge(tesla_df_1,ps_ratio_df, how="outer",on="date")

# merge: tesla_df_2 and twitter (AS: tesla_df_3)
tesla_df_3 = pd.merge(tesla_df_2,twitter_df, how="outer",on="date")

# converting column: convert 'date' column to pandas.to_datetime(), THEN adding it as a new column to df
tesla_df_3["date_converted"] = pd.to_datetime(tesla_df_3["date"])

# converting column: convert 'date' column to pandas.to_numeric(), THEN adding it as a new column to df
tesla_df_3["date_converted_to_numeric"] = pd.to_numeric(tesla_df_3["date_converted"])

# sort: by date (final wrangling/applying as: tesla_df )
tesla_df = tesla_df_3.sort_values(by=["date_converted"],ascending = False)
tesla_df.head()

# use to this code: to audit via excel
# tesla_df.to_csv("tesla_df.csv",index=False,header=True)

	date	operating_income	cf_from_operations	Close	Volume	P/S Ratio	TSLA_compound	ElonMusk_compound	date_converted	date_converted_to_numeric
623	2018-06-19	NaN	NaN	352.550000	12537147.0	5.125	0.078213	0.078213	2018-06-19	1529366400000000000
622	2018-06-18	NaN	NaN	370.829987	11985600.0	5.030	0.108785	0.108785	2018-06-18	1529280000000000000
625	2018-06-17	NaN	NaN	NaN	NaN	NaN	0.130824	0.130824	2018-06-17	1529193600000000000
624	2018-06-16	NaN	NaN	NaN	NaN	NaN	0.130824	0.130824	2018-06-16	1529107200000000000
621	2018-06-15	NaN	NaN	358.170013	10848300.0	4.899	0.108785	0.108785	2018-06-15	1529020800000000000

Pipeline - End

len(tesla_df["date"])

tesla_df.dtypes

date                                 object
operating_income                    float64
cf_from_operations                  float64
Close                               float64
Volume                              float64
P/S Ratio                           float64
TSLA_compound                       float64
ElonMusk_compound                   float64
date_converted               datetime64[ns]
date_converted_to_numeric             int64
dtype: object

* Financial Graphs *

# Plot - Operating Income vs Closing Prices

plt.figure(figsize=(900,6))

ax = tesla_df.plot(x="date_converted_to_numeric",
                   y="Close",
                   legend=False,
                   color="Red")
    
ax2 = ax.twinx()

tesla_df.plot(x="date_converted_to_numeric",
              y="operating_income",
              kind="scatter",
              marker='x',
              alpha=1,
              c="blue",
              s=100,
              edgecolor="green",
              legend=False,
              figsize=(10,7),
              ax=ax2)

ax.figure.legend()

plt.title("Income Generated vs Stock Price")
ax.set_xlabel("Date")
ax.set_ylabel("Closing Prices ($)")
ax2.set_ylabel("Income Generated ($)")

plt.savefig("Income Generated vs Stock Prices.png")

<matplotlib.figure.Figure at 0x1cd57089fd0>

# Plot - CASH Generated from Business Operations vs Closing Prices

# Plot - Operating Income vs Closing Prices

plt.figure(figsize=(900,6))

ax = tesla_df.plot(x="date_converted_to_numeric",
                   y="Close",
                   legend=False,
                   color="Red")
    
ax2 = ax.twinx()

tesla_df.plot(x="date_converted_to_numeric",
              y="cf_from_operations",
              kind="scatter",
              marker='o',
              alpha=1,
              c="none",
              s=100,
              edgecolor="green",
              legend=False,
              figsize=(10,7),
              ax=ax2)

ax.figure.legend()

plt.title("Cash Generated vs Stock Price")
ax.set_xlabel("Date")
ax.set_ylabel("Closing Prices ($)")
ax2.set_ylabel("Cash Generated ($)")

plt.savefig("Cash Generated vs Stock Prices.png")

<matplotlib.figure.Figure at 0x1cd574aa160>

# plot - Stock Price vs PS ratio

andy_df = tesla_df[["date","Close","P/S Ratio"]].dropna(how="any")

ax = andy_df.plot(x="date",
                  y="Close",
                  legend=False,
                  color="red")

ax2 = ax.twinx()

andy_df.plot(x="date",
             y="P/S Ratio",
             ax=ax2,
             legend=False,
             color="Green",
             figsize=(10,7))

ax.figure.legend()

plt.title("Stock Price vs Price-To-Sale Ratio")
ax.set_xlabel("Date")
ax.set_ylabel("Stock Price ($)")
ax2.set_ylabel("Price-To-Sale Ratio (%)")


plt.savefig("Stock Price vs Price-To_Sale Ratio.png")

# plot - Operating Income vs PS ratio

plt.figure(figsize=(900,6))

ax = tesla_df.plot(x="date_converted_to_numeric",
                   y="operating_income",
                   marker='x',
                   legend=False,
                   color="blue",
                   kind="scatter",
                   s=100)

ax2 = ax.twinx()

tesla_df.plot(x="date_converted_to_numeric",
             y="P/S Ratio",
             ax=ax2,
             legend=False,
             color="Green",
             figsize=(10,7))

# plt.xticks(tesla_df["date_converted_to_numeric"], tesla_df["date"], rotation='vertical')

ax.figure.legend()

plt.title("Income Generated vs Price-To-Sale Ratio")
ax.set_xlabel("Date")
ax.set_ylabel("Stock Price")
ax2.set_ylabel("Price-To-Sale Ratio (%)")

plt.savefig("Operating Income vs Price-To_Sale Ratio.png")

<matplotlib.figure.Figure at 0x1cd57e44208>

>>> Twitter section >>>

#Convert Date column to Pandas Date Time format
tsla_tweet["Date"] = pd.to_datetime(tsla_tweet["Date"])
elonmusk_tweet["Date"] = pd.to_datetime(elonmusk_tweet["Date"])
tsla_stock["Date"] = pd.to_datetime(tsla_stock["Date"])

tsla_tweet.head()

	Count	Compound	Date	Neutral	Negative	Positive	Tweet	User
0	0	0.0000	2018-06-15 03:28:15	1.000	0.000	0.000	Check out the companies producing headlines be...	easier2live
1	1	-0.6486	2018-06-15 03:28:07	0.538	0.462	0.000	$TSLA shorts are literally dead. https://t.co/...	BankofVol
2	2	0.3818	2018-06-15 03:26:31	0.698	0.000	0.302	RT @TeslaCharts: Growth synergies bro.\r\n$TSL...	librab103
3	3	0.2263	2018-06-15 03:25:38	0.667	0.133	0.200	RT @MelaynaLokosky: Fraud is not a sustainable...	engineerhunter
4	4	0.0000	2018-06-15 03:23:50	1.000	0.000	0.000	RT @blainefundlp: Obviously Bullish $TSLA htt...	Marcelozinn

elonmusk_tweet.head()

	Count	Compound	Date	Neutral	Negative	Positive	Tweet	User
0	0	-0.3182	2018-06-15 03:55:39	0.905	0.095	0.000	RT @tictoc: .@elonmusk says the Boring Co.’s h...	LewisFranck
1	1	-0.4019	2018-06-15 03:55:21	0.769	0.231	0.000	@tictoc @elonmusk Anything the deflect from th...	IanA_Aus
2	2	0.6697	2018-06-15 03:55:06	0.814	0.000	0.186	@tictoc @elonmusk He is quite clearly on some ...	__R_Adair__
3	3	-0.3182	2018-06-15 03:54:44	0.905	0.095	0.000	RT @tictoc: .@elonmusk says the Boring Co.’s h...	shauntheyawn
4	4	0.0000	2018-06-15 03:54:28	1.000	0.000	0.000	RT @MonsNoobis: @seekevinrun @elonmusk @boring...	paizurizilla

tsla_stock.head()

	Date	Open	High	Low	Close	Adj Close	Volume	PS Ratio
0	2018-06-13	346.709991	347.200012	339.799988	344.779999	344.779999	9469800	4.858
1	2018-06-14	347.630005	358.750000	346.600006	357.720001	357.720001	10981000	4.898
2	2018-06-15	353.839996	364.670013	351.250000	358.170013	358.170013	10531700	4.899
3	2018-06-18	355.400000	373.730000	354.500000	370.830000	370.830000	11693075	5.030
4	2018-06-19	365.160000	370.000000	346.250000	352.550000	352.550000	12537147	5.125

# add new date column - converted "DATE" to PST
tsla_tweet["datetime_pst"] = tsla_tweet['Date'].map(lambda x: x-timedelta(hours = 8))
#tsla_tweet
elonmusk_tweet["datetime_pst"] = elonmusk_tweet['Date'].map(lambda x: x-timedelta(hours = 8))
#elonmusk_tweet

# add new date only column based on new column above datetime_pst
tsla_tweet["date_only"] = tsla_tweet['datetime_pst'].map(lambda x: datetime.date(x))
tsla_tweet.head()

	Count	Compound	Date	Neutral	Negative	Positive	Tweet	User	datetime_pst	date_only
0	0	0.0000	2018-06-15 03:28:15	1.000	0.000	0.000	Check out the companies producing headlines be...	easier2live	2018-06-14 19:28:15	2018-06-14
1	1	-0.6486	2018-06-15 03:28:07	0.538	0.462	0.000	$TSLA shorts are literally dead. https://t.co/...	BankofVol	2018-06-14 19:28:07	2018-06-14
2	2	0.3818	2018-06-15 03:26:31	0.698	0.000	0.302	RT @TeslaCharts: Growth synergies bro.\r\n$TSL...	librab103	2018-06-14 19:26:31	2018-06-14
3	3	0.2263	2018-06-15 03:25:38	0.667	0.133	0.200	RT @MelaynaLokosky: Fraud is not a sustainable...	engineerhunter	2018-06-14 19:25:38	2018-06-14
4	4	0.0000	2018-06-15 03:23:50	1.000	0.000	0.000	RT @blainefundlp: Obviously Bullish $TSLA htt...	Marcelozinn	2018-06-14 19:23:50	2018-06-14

# add new date only column based on new column above datetime_pst
elonmusk_tweet["date_only"] = elonmusk_tweet['datetime_pst'].map(lambda x: datetime.date(x))
elonmusk_tweet.head()

	Count	Compound	Date	Neutral	Negative	Positive	Tweet	User	datetime_pst	date_only
0	0	-0.3182	2018-06-15 03:55:39	0.905	0.095	0.000	RT @tictoc: .@elonmusk says the Boring Co.’s h...	LewisFranck	2018-06-14 19:55:39	2018-06-14
1	1	-0.4019	2018-06-15 03:55:21	0.769	0.231	0.000	@tictoc @elonmusk Anything the deflect from th...	IanA_Aus	2018-06-14 19:55:21	2018-06-14
2	2	0.6697	2018-06-15 03:55:06	0.814	0.000	0.186	@tictoc @elonmusk He is quite clearly on some ...	__R_Adair__	2018-06-14 19:55:06	2018-06-14
3	3	-0.3182	2018-06-15 03:54:44	0.905	0.095	0.000	RT @tictoc: .@elonmusk says the Boring Co.’s h...	shauntheyawn	2018-06-14 19:54:44	2018-06-14
4	4	0.0000	2018-06-15 03:54:28	1.000	0.000	0.000	RT @MonsNoobis: @seekevinrun @elonmusk @boring...	paizurizilla	2018-06-14 19:54:28	2018-06-14

# Reorganizing the columns using double brackets
tsla_sentiment = tsla_tweet[["date_only", "Compound","Negative","Neutral","Positive"]]
tsla_sentiment.head()

	date_only	Compound	Negative	Neutral	Positive
0	2018-06-14	0.0000	0.000	1.000	0.000
1	2018-06-14	-0.6486	0.462	0.538	0.000
2	2018-06-14	0.3818	0.000	0.698	0.302
3	2018-06-14	0.2263	0.133	0.667	0.200
4	2018-06-14	0.0000	0.000	1.000	0.000

# Reorganizing the columns using double brackets
elonmusk_sentiment = elonmusk_tweet[["date_only", "Compound","Negative","Neutral","Positive"]]
elonmusk_sentiment.head()

	date_only	Compound	Negative	Neutral	Positive
0	2018-06-14	-0.3182	0.095	0.905	0.000
1	2018-06-14	-0.4019	0.231	0.769	0.000
2	2018-06-14	0.6697	0.000	0.814	0.186
3	2018-06-14	-0.3182	0.095	0.905	0.000
4	2018-06-14	0.0000	0.000	1.000	0.000

#calc mean on sentiment columns
tsla_summary = tsla_sentiment.groupby(["date_only"]).mean()
tsla_summary

	Compound	Negative	Neutral	Positive
date_only
2018-06-13	0.098802	0.049198	0.860688	0.090127
2018-06-14	0.086925	0.053664	0.858410	0.087931
2018-06-15	0.112132	0.046684	0.867034	0.086257
2018-06-16	0.092435	0.045460	0.875024	0.079519
2018-06-17	0.094116	0.046624	0.871212	0.082160
2018-06-18	0.019366	0.068341	0.854305	0.077362
2018-06-19	-0.012250	0.069882	0.861970	0.068145

#create dataframe of summary
tsla_summary_df = pd.DataFrame(tsla_summary).reset_index()
tsla_summary_df

	date_only	Compound	Negative	Neutral	Positive
0	2018-06-13	0.098802	0.049198	0.860688	0.090127
1	2018-06-14	0.086925	0.053664	0.858410	0.087931
2	2018-06-15	0.112132	0.046684	0.867034	0.086257
3	2018-06-16	0.092435	0.045460	0.875024	0.079519
4	2018-06-17	0.094116	0.046624	0.871212	0.082160
5	2018-06-18	0.019366	0.068341	0.854305	0.077362
6	2018-06-19	-0.012250	0.069882	0.861970	0.068145

#calc mean on sentiment columns
elonmusk_summary = elonmusk_sentiment.groupby(["date_only"]).mean()
elonmusk_summary

	Compound	Negative	Neutral	Positive
date_only
2018-06-14	0.079546	0.042809	0.873463	0.083724
2018-06-15	0.111768	0.047194	0.851610	0.101198
2018-06-16	0.225176	0.045684	0.822515	0.131810
2018-06-17	0.063654	0.060865	0.846699	0.092447
2018-06-18	0.071938	0.052710	0.858175	0.089124
2018-06-19	0.120690	0.049550	0.844380	0.106071

#create dataframe of summary
elonmusk_summary_df = pd.DataFrame(elonmusk_summary).reset_index()
elonmusk_summary_df

	date_only	Compound	Negative	Neutral	Positive
0	2018-06-14	0.079546	0.042809	0.873463	0.083724
1	2018-06-15	0.111768	0.047194	0.851610	0.101198
2	2018-06-16	0.225176	0.045684	0.822515	0.131810
3	2018-06-17	0.063654	0.060865	0.846699	0.092447
4	2018-06-18	0.071938	0.052710	0.858175	0.089124
5	2018-06-19	0.120690	0.049550	0.844380	0.106071

#rename columns TSLA
tsla_final_df = tsla_summary_df.rename(columns={"date_only":"date", "Compound":"TSLA_compound", "Negative":"TSLA_neg", "Positive":"TSLA_pos", "Neutral":"TSLA_neu"})
tsla_final_df.head()

	date	TSLA_compound	TSLA_neg	TSLA_neu	TSLA_pos
0	2018-06-13	0.098802	0.049198	0.860688	0.090127
1	2018-06-14	0.086925	0.053664	0.858410	0.087931
2	2018-06-15	0.112132	0.046684	0.867034	0.086257
3	2018-06-16	0.092435	0.045460	0.875024	0.079519
4	2018-06-17	0.094116	0.046624	0.871212	0.082160

#rename columns Elon Musk
elonmusk_final_df = elonmusk_summary_df.rename(columns={"date_only":"date", "Compound":"ElonMusk_compound", "Negative":"ElonMusk_neg", "Positive":"ElonMusk_pos", "Neutral":"ElonMusk_neu"})
elonmusk_final_df.head()

	date	ElonMusk_compound	ElonMusk_neg	ElonMusk_neu	ElonMusk_pos
0	2018-06-14	0.079546	0.042809	0.873463	0.083724
1	2018-06-15	0.111768	0.047194	0.851610	0.101198
2	2018-06-16	0.225176	0.045684	0.822515	0.131810
3	2018-06-17	0.063654	0.060865	0.846699	0.092447
4	2018-06-18	0.071938	0.052710	0.858175	0.089124

#merge ElonMusk and TSLA data frames
tweet_sentiment_merged = pd.merge(tsla_final_df, elonmusk_final_df, on="date", how="left")
tweet_sentiment_merged.head()

	date	TSLA_compound	TSLA_neg	TSLA_neu	TSLA_pos	ElonMusk_compound	ElonMusk_neg	ElonMusk_neu	ElonMusk_pos
0	2018-06-13	0.098802	0.049198	0.860688	0.090127	NaN	NaN	NaN	NaN
1	2018-06-14	0.086925	0.053664	0.858410	0.087931	0.079546	0.042809	0.873463	0.083724
2	2018-06-15	0.112132	0.046684	0.867034	0.086257	0.111768	0.047194	0.851610	0.101198
3	2018-06-16	0.092435	0.045460	0.875024	0.079519	0.225176	0.045684	0.822515	0.131810
4	2018-06-17	0.094116	0.046624	0.871212	0.082160	0.063654	0.060865	0.846699	0.092447

#reorder final data frame using double brackets
tweet_sentiment_final = tweet_sentiment_merged[["date","TSLA_compound","ElonMusk_compound","TSLA_neg", "ElonMusk_neg", "TSLA_pos", "ElonMusk_pos", "TSLA_neu", "ElonMusk_neu"]]
tweet_sentiment_final

	date	TSLA_compound	ElonMusk_compound	TSLA_neg	ElonMusk_neg	TSLA_pos	ElonMusk_pos	TSLA_neu	ElonMusk_neu
0	2018-06-13	0.098802	NaN	0.049198	NaN	0.090127	NaN	0.860688	NaN
1	2018-06-14	0.086925	0.079546	0.053664	0.042809	0.087931	0.083724	0.858410	0.873463
2	2018-06-15	0.112132	0.111768	0.046684	0.047194	0.086257	0.101198	0.867034	0.851610
3	2018-06-16	0.092435	0.225176	0.045460	0.045684	0.079519	0.131810	0.875024	0.822515
4	2018-06-17	0.094116	0.063654	0.046624	0.060865	0.082160	0.092447	0.871212	0.846699
5	2018-06-18	0.019366	0.071938	0.068341	0.052710	0.077362	0.089124	0.854305	0.858175
6	2018-06-19	-0.012250	0.120690	0.069882	0.049550	0.068145	0.106071	0.861970	0.844380

#change datatype for date column so that you can merge on this column below
tweet_sentiment_final["date"] = pd.to_datetime(tweet_sentiment_final["date"])

#rename date column for stock data
tsla_stock = tsla_stock.rename(columns={"Date":"date"})
tsla_stock.head()

	date	Open	High	Low	Close	Adj Close	Volume	PS Ratio
0	2018-06-13	346.709991	347.200012	339.799988	344.779999	344.779999	9469800	4.858
1	2018-06-14	347.630005	358.750000	346.600006	357.720001	357.720001	10981000	4.898
2	2018-06-15	353.839996	364.670013	351.250000	358.170013	358.170013	10531700	4.899
3	2018-06-18	355.400000	373.730000	354.500000	370.830000	370.830000	11693075	5.030
4	2018-06-19	365.160000	370.000000	346.250000	352.550000	352.550000	12537147	5.125

#double check dataframes for field types (if necessary)
tweet_sentiment_final.info()
tsla_stock.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 7 entries, 0 to 6
Data columns (total 9 columns):
date                 7 non-null datetime64[ns]
TSLA_compound        7 non-null float64
ElonMusk_compound    6 non-null float64
TSLA_neg             7 non-null float64
ElonMusk_neg         6 non-null float64
TSLA_pos             7 non-null float64
ElonMusk_pos         6 non-null float64
TSLA_neu             7 non-null float64
ElonMusk_neu         6 non-null float64
dtypes: datetime64[ns](1), float64(8)
memory usage: 560.0 bytes
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 5 entries, 0 to 4
Data columns (total 8 columns):
date         5 non-null datetime64[ns]
Open         5 non-null float64
High         5 non-null float64
Low          5 non-null float64
Close        5 non-null float64
Adj Close    5 non-null float64
Volume       5 non-null int64
PS Ratio     5 non-null float64
dtypes: datetime64[ns](1), float64(6), int64(1)
memory usage: 400.0 bytes

#merge data frames - add TSLA Stock
tweet_stock_merged = pd.merge(tweet_sentiment_final, tsla_stock, on="date", how="right")
tweet_stock_merged

	date	TSLA_compound	ElonMusk_compound	TSLA_neg	ElonMusk_neg	TSLA_pos	ElonMusk_pos	TSLA_neu	ElonMusk_neu	Open	High	Low	Close	Adj Close	Volume	PS Ratio
0	2018-06-13	0.098802	NaN	0.049198	NaN	0.090127	NaN	0.860688	NaN	346.709991	347.200012	339.799988	344.779999	344.779999	9469800	4.858
1	2018-06-14	0.086925	0.079546	0.053664	0.042809	0.087931	0.083724	0.858410	0.873463	347.630005	358.750000	346.600006	357.720001	357.720001	10981000	4.898
2	2018-06-15	0.112132	0.111768	0.046684	0.047194	0.086257	0.101198	0.867034	0.851610	353.839996	364.670013	351.250000	358.170013	358.170013	10531700	4.899
3	2018-06-18	0.019366	0.071938	0.068341	0.052710	0.077362	0.089124	0.854305	0.858175	355.400000	373.730000	354.500000	370.830000	370.830000	11693075	5.030
4	2018-06-19	-0.012250	0.120690	0.069882	0.049550	0.068145	0.106071	0.861970	0.844380	365.160000	370.000000	346.250000	352.550000	352.550000	12537147	5.125

tsla_stock.head()

	date	Open	High	Low	Close	Adj Close	Volume	PS Ratio
0	2018-06-13	346.709991	347.200012	339.799988	344.779999	344.779999	9469800	4.858
1	2018-06-14	347.630005	358.750000	346.600006	357.720001	357.720001	10981000	4.898
2	2018-06-15	353.839996	364.670013	351.250000	358.170013	358.170013	10531700	4.899
3	2018-06-18	355.400000	373.730000	354.500000	370.830000	370.830000	11693075	5.030
4	2018-06-19	365.160000	370.000000	346.250000	352.550000	352.550000	12537147	5.125

* Twitter Graphs *

#stock chart
plt.plot(tsla_stock.date,tsla_stock.Close)
plt.xticks(rotation=70)

(array([736858., 736859., 736860., 736861., 736862., 736863., 736864.]),
 <a list of 7 Text xticklabel objects>)

ax = tweet_stock_merged.plot(x="date", y="Close", legend=False, marker="x")
ax2 = ax.twinx()
tweet_stock_merged.plot(x="date", y="TSLA_compound", ax=ax2, legend=False, color="r", marker="o", figsize=(10,7))
#tweet_stock_merged.plot(x="date", y="TSLA_pos", ax=ax2, legend=False, color="b", marker="o")
#tweet_stock_merged.plot(x="date", y="TSLA_neg", ax=ax2, legend=False, color="b", marker="o")
#tweet_stock_merged.plot(x="date", y="TSLA_neu", ax=ax2, legend=False, color="b", marker="o")
ax.figure.legend()
plt.title("TSLA Sentiment Vs. Stock Price")
ax.set_xlabel("Date")
ax.set_ylabel("Stock Price")
ax2.set_ylabel("Sentiment")
plt.savefig("TSLASentimentchart.png")

ax = tweet_stock_merged.plot(x="date", y="Close", legend=False, marker="x")
ax2 = ax.twinx()
tweet_stock_merged.plot(x="date", y="ElonMusk_compound", ax=ax2, legend=False, color="r", marker="o", figsize=(10,7))
#tweet_stock_merged.plot(x="date", y="TSLA_pos", ax=ax2, legend=False, color="b", marker="o")
#tweet_stock_merged.plot(x="date", y="TSLA_neg", ax=ax2, legend=False, color="b", marker="o")
#tweet_stock_merged.plot(x="date", y="TSLA_neu", ax=ax2, legend=False, color="b", marker="o")
ax.figure.legend()
plt.title("Elon Musk Sentiment Vs. Stock Price")
ax.set_xlabel("Date")
ax.set_ylabel("Stock Price")
ax2.set_ylabel("Sentiment")
plt.savefig("ElonMuskSentimentchart.png")

ax = tweet_stock_merged.plot(x="date", y="PS Ratio", legend=False, marker="x")
ax2 = ax.twinx()
tweet_stock_merged.plot(x="date", y="TSLA_compound", ax=ax2, legend=False, color="r", marker="o", figsize=(10,7))
#tweet_stock_merged.plot(x="date", y="TSLA_pos", ax=ax2, legend=False, color="b", marker="o")
#tweet_stock_merged.plot(x="date", y="TSLA_neg", ax=ax2, legend=False, color="b", marker="o")
#tweet_stock_merged.plot(x="date", y="TSLA_neu", ax=ax2, legend=False, color="b", marker="o")
ax.figure.legend()
plt.title("TSLA Sentiment Vs. P/S Ratio")
ax.set_xlabel("Date")
ax.set_ylabel("P/S Ratio")
ax2.set_ylabel("Sentiment")
plt.savefig("TSLA_PSRatiochart.png")

ax = tweet_stock_merged.plot(x="date", y="PS Ratio", legend=False, marker="x")
ax2 = ax.twinx()
tweet_stock_merged.plot(x="date", y="ElonMusk_compound", ax=ax2, legend=False, color="r", marker="o", figsize=(10,7))
#tweet_stock_merged.plot(x="date", y="TSLA_pos", ax=ax2, legend=False, color="b", marker="o")
#tweet_stock_merged.plot(x="date", y="TSLA_neg", ax=ax2, legend=False, color="b", marker="o")
#tweet_stock_merged.plot(x="date", y="TSLA_neu", ax=ax2, legend=False, color="b", marker="o")
ax.figure.legend()
plt.title("Elon Musk Sentiment Vs. P/S Ratio")
ax.set_xlabel("Date")
ax.set_ylabel("P/S Ratio")
ax2.set_ylabel("Sentiment")
plt.savefig("ElonMusk_PSRatio.png")

#export to csv
tweet_stock_merged.to_csv("tweet_stock_merged.csv", index=False)

Name		Name	Last commit message	Last commit date
Latest commit History 10 Commits
Graphs		Graphs
Resources		Resources
README.md		README.md
TESLA_ANALYSIS.ipynb		TESLA_ANALYSIS.ipynb

Provide feedback

Saved searches

Use saved searches to filter your results more quickly

Repository files navigation

TESLA STOCK ANALYSIS

This analysis explores a relationship amongst stock price, price to sales ratio, twitter sentiment and financial statements for Tesla Stock.

Tesla Data sources

Lessons Learned

>>> Financials Section >>>

Pipeline - Begin

Pipeline - End

* Financial Graphs *

>>> Twitter section >>>

* Twitter Graphs *

About

Releases

Packages

Languages

MariannaMangat/Tesla_Stock_Analysis

Folders and files

Latest commit

History

Repository files navigation

TESLA STOCK ANALYSIS

This analysis explores a relationship amongst stock price, price to sales ratio, twitter sentiment and financial statements for Tesla Stock.

Tesla Data sources

Lessons Learned

>>> Financials Section >>>

Pipeline - Begin

Pipeline - End

*** Financial Graphs ***

>>> Twitter section >>>

*** Twitter Graphs ***

About

Resources

Stars

Watchers

Forks

Releases

Packages 0

Languages

* Financial Graphs *

* Twitter Graphs *

Packages