The language to be used is Python 3

Questions:

This company's sentiment data is used by quantitative analysts and traders as a factor in stock selection models. The research we conduct looks at the relationship between sentiment data and future returns of stock prices.

Part I: Import the data, calculate the return, and merge the data.

1.1 Import SPY.csv

Instruction

1. Import SPY.csv, drop missing values, clean up Date, sort the dataframe by Date (earliest value first).

2. Calculate the daily open-to-close return (Buy at Open price, sell at Close price). Name this new value/column: ret_SPY

3. Calculate the future 20-day cumulative return for SPY. Name this new value/column: ret_SPY_c

4. Keep only Date, ret_SPY, and ret_SPY_c

Hints

For 1. Use .sort_values(), make sure to set 'inplace' to 'True', and 'ascending' to 'True'

For 3. Use this:

indexer = pd.api.indexers.FixedForwardWindowIndexer(window_size=20) SPY['ret_SPY_c'] = ((1.+ SPY['ret_SPY']).rolling(window=indexer, min_periods=20).agg(lambda x : x.prod()) - 1).shift(-1)

Note: windows_size = 20 trading days

** Your code below **

1.2 Import TSLA.csv

Instruction

1. Import TSLA.csv, drop missing values, clean up Date, sort the dataframe by Date (earliest value first).

2. Calculate the daily open-to-close return (Buy at Open price, sell at Close price). Name this new value/column: ret_TSLA

3. Calculate the future 20-day cumulative return for TSLA. Name this new value/column: ret_TSLA_c

4. Keep only Date, ret_TSLA, and ret_TSLA_c

** Your code below **

[ ]:

1.3 Import TSLA_Score.csv

TSLA_Score.csv contains the sentiment score computed from tweets that mentioned Tesla.

Instruction

1. Import TSLA_Score.csv, drop missing values, rename column date to Date, clean up Date, sort the dataframe by Date (earliest value first).

2. Keep only Date, s-score, sv-score, s-buzz

** Your code below **

1.4 Merge the data

Instruction

Merge SPY, TSLA, and TSLA_Score from 1.1, 1.2, and 1.3 by Date. Save it to a dataframe called testData.

** Your code below **

SKY.csv

Date	Open	High	Low	Close	Volume	Dividend	Split	Adj_Open	Adj_High	Adj_Low	Adj_Close	Adj_Volume
04-01-2019	284.7	286.16	284.4	285.83	77617945	0	1	284.7	286.16	284.4	285.83	77617945
3/29/2019	282.39	282.84	281.14	282.48	82160899	0	1	282.39	282.84	281.14	282.48	82160899
3/28/2019	280.35	281.21	279.07	280.71	56107818	0	1	280.35	281.21	279.07	280.71	56107818
3/27/2019	281.11	281.76	277.93	279.65	72174824	0	1	281.11	281.76	277.93	279.65	72174824
3/26/2019	280.99	282.18	279.56	281.12	68047406	0	1	280.99	282.18	279.56	281.12	68047406
3/25/2019	278.87	280.1853	277.64	279.04	85312509	0	1	278.87	280.1853	277.64	279.04	85312509

TSLA.csv

Date	ticker	Adj_Open	Adj_Close
04-01-2019	TSLA	282.62	289.18
3/29/2019	TSLA	278.7	279.86
3/28/2019	TSLA	277.16	278.62
3/27/2019	TSLA	268.75	274.83
3/26/2019	TSLA	264.44	267.77
3/25/2019	TSLA	259.71	260.42