ArcticDB_demo_equity_analytics

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Using ArcticDB for equity analytics: a worked example

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A Simple Workflow for Equity Price Timeseries¶

In this notebook we:

• Source historical tech equity and S&P 500 prices from Yahoo! using the yfinance package
• Store 10 years of daily price raw data in a temporary local ArcticDB library set up in the notebook
• The data sourcing is broken into two steps: an initial load of history then an update for recent prices
• This suggests a typical system workflow: an initial backfill and daily update with recent data
• Take Adjusted Close prices, remove missing data and calculate returns. Then save this to library
• Read the clean returns and use them to calculate rolling beta against the S&P 500 for the stocks

Notice: This notebook sources data from Yahoo!¶

Please read Yahoo terms here before using it

https://policies.yahoo.com/us/en/yahoo/terms/index.htm

Installs and Imports¶

In [2]:

!pip install arcticdb yfinance

!pip install arcticdb yfinance

In [3]:

import arcticdb as adb
import yfinance as yf
import pandas as pd
from typing import List, Tuple
from datetime import datetime 
import matplotlib.pyplot as plt

import arcticdb as adb import yfinance as yf import pandas as pd from typing import List, Tuple from datetime import datetime import matplotlib.pyplot as plt

ArcticDB Setup¶

In [4]:

arctic = adb.Arctic("lmdb://arcticdb_equity")
lib = arctic.get_library('demo', create_if_missing=True)

arctic = adb.Arctic("lmdb://arcticdb_equity") lib = arctic.get_library('demo', create_if_missing=True)

Source Historical Prices from Yahoo!¶

In [24]:

start = datetime(2013, 1, 1) 
end = datetime(2022, 12, 31)
freq = '1d'
symbols = {
    '^GSPC': 'S&P 500',
    'AAPL': 'Apple',
    'MSFT': 'Microsoft',
    'GOOG': 'Google',
    'AMZN': 'Amazon',
    'NVDA': 'Nvidia',
    'META': 'Meta',
    'TSLA': 'Tesla',
    'TSM': 'TSMC',
    'TCEHY': 'Tencent',
    '005930.KS': 'Samsung',
    'ORCL': 'Oracle',
    'ADBE': 'Adobe',
    'ASML': 'ASML',
    'CSCO': 'Cisco'
}

start = datetime(2013, 1, 1) end = datetime(2022, 12, 31) freq = '1d' symbols = { '^GSPC': 'S&P 500', 'AAPL': 'Apple', 'MSFT': 'Microsoft', 'GOOG': 'Google', 'AMZN': 'Amazon', 'NVDA': 'Nvidia', 'META': 'Meta', 'TSLA': 'Tesla', 'TSM': 'TSMC', 'TCEHY': 'Tencent', '005930.KS': 'Samsung', 'ORCL': 'Oracle', 'ADBE': 'Adobe', 'ASML': 'ASML', 'CSCO': 'Cisco' }

In [25]:

hist = yf.download(list(symbols.keys()), interval=freq, start=start, end=end)

hist = yf.download(list(symbols.keys()), interval=freq, start=start, end=end)

[*********************100%%**********************]  15 of 15 completed

In [26]:

# the column levels[0] are the fields for each stock
print(hist.columns.levels[0])
hist['Volume'].head(5)

# the column levels[0] are the fields for each stock print(hist.columns.levels[0]) hist['Volume'].head(5)

Index(['Adj Close', 'Close', 'High', 'Low', 'Open', 'Volume'], dtype='object')

Out[26]:

	005930.KS	AAPL	ADBE	AMZN	ASML	CSCO	GOOG	META	MSFT	NVDA	ORCL	TCEHY	TSLA	TSM	^GSPC
Date
2013-01-02	11449650.0	560518000.0	6483800.0	65420000.0	1824000.0	40304500.0	102033017.0	69846400.0	52899300.0	47883600.0	33758400.0	362500.0	17922000.0	10226100.0	4.202600e+09
2013-01-03	14227400.0	352965200.0	3906000.0	55018000.0	1725400.0	50603500.0	93075567.0	63140600.0	48294400.0	29888800.0	21819500.0	355000.0	11130000.0	13148600.0	3.829730e+09
2013-01-04	12999800.0	594333600.0	3809300.0	37484000.0	3170800.0	36378900.0	110954331.0	72715400.0	52521100.0	52496800.0	21687300.0	101000.0	10110000.0	7464200.0	3.424290e+09
2013-01-07	12610950.0	484156400.0	3632100.0	98200000.0	2066100.0	30790700.0	66476239.0	83781800.0	37110400.0	61073200.0	14008300.0	83000.0	6630000.0	9429900.0	3.304970e+09
2013-01-08	13822250.0	458707200.0	3080900.0	60214000.0	1182400.0	33218100.0	67295297.0	45871300.0	44703100.0	46642400.0	17408900.0	49000.0	19260000.0	8112900.0	3.601600e+09

Write the Data to the Library¶

Currently ArcticDB cannot store multi-level column dataframes directly, so we use a symbol for each price field.

In [27]:

def field_name_to_sym(field_name: str) -> str:
    return f"hist/price_{field_name.replace(' ', '')}"

def field_name_to_sym(field_name: str) -> str: return f"hist/price_{field_name.replace(' ', '')}"

In [28]:

for l in hist.columns.levels[0]:
    lib.write(field_name_to_sym(l), hist[l])

for l in hist.columns.levels[0]: lib.write(field_name_to_sym(l), hist[l])

In [29]:

# read back and check that the data round-trips precisely
for l in hist.columns.levels[0]:
    hist_check_db = lib.read(field_name_to_sym(l)).data
    if not hist[l].equals(hist_check_db):
        print(f"Field '{l}' does not round-trip")

# read back and check that the data round-trips precisely for l in hist.columns.levels[0]: hist_check_db = lib.read(field_name_to_sym(l)).data if not hist[l].equals(hist_check_db): print(f"Field '{l}' does not round-trip")

Update with Recent Data¶

Here we consume data for the recent past and slightly overlap the time window with the original data.

In a real workflow this technique can be used to apply restatements to the price dataset.

In [30]:

update_start = datetime(2022, 7, 1) 
update_end = datetime(2023, 12, 31)
update_hist = yf.download(list(symbols.keys()), interval=freq, start=update_start, end=update_end)

update_start = datetime(2022, 7, 1) update_end = datetime(2023, 12, 31) update_hist = yf.download(list(symbols.keys()), interval=freq, start=update_start, end=update_end)

[*********************100%%**********************]  15 of 15 completed

In [31]:

for l in update_hist.columns.levels[0]:
    lib.update(field_name_to_sym(l), update_hist[l])

for l in update_hist.columns.levels[0]: lib.update(field_name_to_sym(l), update_hist[l])

In [32]:

# these are the symbols we have created
lib.list_symbols()

# these are the symbols we have created lib.list_symbols()

Out[32]:

['hist/price_High',
 'hist/price_Low',
 'hist/price_Volume',
 'hist/price_Close',
 'hist/price_AdjClose',
 'hist/price_Open']

In [33]:

# each symbol contains data for one price field, with the stock tickers as columns and dates as the index
lib.head(field_name_to_sym('Close')).data

# each symbol contains data for one price field, with the stock tickers as columns and dates as the index lib.head(field_name_to_sym('Close')).data

Out[33]:

	005930.KS	AAPL	ADBE	AMZN	ASML	CSCO	GOOG	META	MSFT	NVDA	ORCL	TCEHY	TSLA	TSM	^GSPC
Date
2013-01-02	31520.0	19.608213	38.340000	12.8655	66.779999	20.340000	18.013729	28.000000	27.620001	3.1800	34.689999	6.720	2.357333	18.100000	1462.420044
2013-01-03	30860.0	19.360714	37.750000	12.9240	65.379997	20.450001	18.024191	27.770000	27.250000	3.1825	34.310001	6.660	2.318000	18.090000	1459.369995
2013-01-04	30500.0	18.821428	38.130001	12.9575	64.709999	20.480000	18.380356	28.760000	26.740000	3.2875	34.610001	6.694	2.293333	17.959999	1466.469971
2013-01-07	30400.0	18.710714	37.939999	13.4230	63.660000	20.290001	18.300158	29.420000	26.690001	3.1925	34.430000	6.600	2.289333	17.700001	1461.890015
2013-01-08	30000.0	18.761070	38.139999	13.3190	63.139999	20.309999	18.264042	29.059999	26.549999	3.1225	34.439999	6.570	2.245333	17.540001	1457.150024

Read and Process the Whole Price Dataset¶

• Read the data, using the Adj Close field as our primary price source
• Remove missing data by forward filling. A simple but unsophisticated method
• Calculate daily returns from the prices
• Write the returns back to ArcticDB as another symbol

In [36]:

hist_adj_close = lib.read(field_name_to_sym('Adj Close')).data

hist_adj_close = lib.read(field_name_to_sym('Adj Close')).data

In [37]:

# ffill to remove nans (missing data)
hist_adj_close_clean = hist_adj_close.ffill()
# the following line will return True if there are any nans
hist_adj_close_clean.isnull().any().any()

# ffill to remove nans (missing data) hist_adj_close_clean = hist_adj_close.ffill() # the following line will return True if there are any nans hist_adj_close_clean.isnull().any().any()

Out[37]:

False

In [38]:

hist_daily_returns = hist_adj_close_clean.pct_change(1).iloc[1:]
hist_daily_returns.iloc[:5, :5]

hist_daily_returns = hist_adj_close_clean.pct_change(1).iloc[1:] hist_daily_returns.iloc[:5, :5]

Out[38]:

	005930.KS	AAPL	ADBE	AMZN	ASML
Date
2013-01-03	-0.020939	-0.012622	-0.015389	0.004547	-0.020964
2013-01-04	-0.011666	-0.027854	0.010066	0.002592	-0.010248
2013-01-07	-0.003278	-0.005882	-0.004983	0.035925	-0.016226
2013-01-08	-0.013158	0.002691	0.005272	-0.007748	-0.008168
2013-01-09	0.000000	-0.015629	0.013634	-0.000113	0.005702

In [40]:

returns_sym = 'hist/returns_AdjClose_clean'
lib.write(returns_sym, hist_daily_returns)

returns_sym = 'hist/returns_AdjClose_clean' lib.write(returns_sym, hist_daily_returns)

Out[40]:

VersionedItem(symbol='hist/returns_AdjClose_clean', library='demo', data=n/a, version=1, metadata=None, host='LMDB(path=/users/isys/nclarke/jupyter/arctic/demos/arcticdb_equity)')

End of the Data Processing - Make a Snapshot¶

The snapshot is optional but it can be useful to record the state of all the data and the end of the daily update process.

In [41]:

snapshot_name = f"eod:{hist_daily_returns.iloc[-1].name.date()}"
print(snapshot_name)
if snapshot_name in lib.list_snapshots():
    lib.delete_snapshot(snapshot_name)
lib.snapshot(snapshot_name, metadata="EOD audit point")

snapshot_name = f"eod:{hist_daily_returns.iloc[-1].name.date()}" print(snapshot_name) if snapshot_name in lib.list_snapshots(): lib.delete_snapshot(snapshot_name) lib.snapshot(snapshot_name, metadata="EOD audit point")

eod:2023-11-17

Visualise the Returns¶

In [42]:

plot_start = datetime(2021, 1, 1)
plot_end = datetime(2023, 12, 31)
returns_plot = lib.read(returns_sym, date_range=(plot_start, plot_end)).data
daily_cum_returns = ((1 + returns_plot).cumprod() - 1)
daily_cum_returns.rename(columns=symbols).plot(figsize=(20, 10), grid=True, linewidth=0.9, title="Daily Cumulative Stock Returns")

plot_start = datetime(2021, 1, 1) plot_end = datetime(2023, 12, 31) returns_plot = lib.read(returns_sym, date_range=(plot_start, plot_end)).data daily_cum_returns = ((1 + returns_plot).cumprod() - 1) daily_cum_returns.rename(columns=symbols).plot(figsize=(20, 10), grid=True, linewidth=0.9, title="Daily Cumulative Stock Returns")

Out[42]:

<Axes: title={'center': 'Daily Cumulative Stock Returns'}, xlabel='Date'>

Analysis: Compute Rolling Betas vs the S&P 500¶

Notice the big change in betas starting in Q2 2020 which rolls out of the betas 130 days later (the size of the rolling window).

Possibly due to the large market moves at the start of the Covid era?

In [43]:

index_ticker = "^GSPC"
roll_days = 130
beta_start = datetime(2018, 1, 1)
beta_end = datetime(2022, 12, 31)
beta_returns = lib.read(returns_sym, date_range=(beta_start, beta_end)).data
index_returns = beta_returns[index_ticker]
stock_returns = beta_returns.drop(columns=index_ticker)

index_ticker = "^GSPC" roll_days = 130 beta_start = datetime(2018, 1, 1) beta_end = datetime(2022, 12, 31) beta_returns = lib.read(returns_sym, date_range=(beta_start, beta_end)).data index_returns = beta_returns[index_ticker] stock_returns = beta_returns.drop(columns=index_ticker)

In [44]:

rolling_cov = stock_returns.rolling(roll_days).cov(index_returns).iloc[roll_days-1:]
rolling_index_var = index_returns.rolling(roll_days).var().iloc[roll_days-1:]
rolling_beta = rolling_cov.divide(rolling_index_var, axis='index').rename(columns=symbols)

rolling_cov = stock_returns.rolling(roll_days).cov(index_returns).iloc[roll_days-1:] rolling_index_var = index_returns.rolling(roll_days).var().iloc[roll_days-1:] rolling_beta = rolling_cov.divide(rolling_index_var, axis='index').rename(columns=symbols)

In [45]:

ax = rolling_beta.plot(figsize=(20, 10), grid=True, linewidth=0.9, title=f"Rolling {roll_days}-day beta")
ax.legend(loc='upper left')

ax = rolling_beta.plot(figsize=(20, 10), grid=True, linewidth=0.9, title=f"Rolling {roll_days}-day beta") ax.legend(loc='upper left')

Out[45]:

<matplotlib.legend.Legend at 0x7ef5d42b1fd0>

Conclusions¶

• We have demonstrated a simple data pipeline for capture and analysis
• Although simple, this pattern scales well to much larger datasets
• In a real system, the data collection and storage would be separated from the analysis
• Throughout the ArcticDB usage is simple, clean and clear
• Researchers and Data Scientists like the simplicity - it allows them to focus on the data and their research

For more information about equity beta see https://en.wikipedia.org/wiki/Beta_(finance)