This notebook explores the relative volatity of different countries, above and below a given dollar volume threshold.
from quantrocket import get_prices
from quantrocket.account import download_exchange_rates
from quantrocket.master import get_securities_reindexed_like
import pandas as pd
import io
Get exchange rates (for converting non-USD stocks to USD dollar volume):
f = io.StringIO()
download_exchange_rates(f, latest=True, base_currencies="USD")
rates = pd.read_csv(f)
rates = rates.set_index("QuoteCurrency").Rate.to_dict()
def get_std(db, start_date, min_dollar_volume=0, max_dollar_volume=None, universes=None):
"""
Returns the average standard dev of monthly, daily, overnight, and intraday returns from
the start date until now.
"""
prices = get_prices(
db,
start_date=start_date,
universes=universes,
fields=["Close","Open","Volume"])
closes = prices.loc["Close"]
opens = prices.loc["Open"]
securities = get_securities_reindexed_like(closes, fields=["Currency","Multiplier","PriceMagnifier"])
currencies = securities.loc["Currency"]
multipliers = securities.loc["Multiplier"]
magnifiers = securities.loc["PriceMagnifier"]
volumes = prices.loc["Volume"]
currencies = prices.loc["Currency"]
multipliers = prices.loc["Multiplier"].reindex(closes.index, method="ffill")
magnifiers = prices.loc["PriceMagnifier"].reindex(closes.index, method="ffill")
exchange_rates = currencies.applymap(lambda x: rates[x])
exchange_rates = exchange_rates.reindex(closes.index, method="ffill")
closes = closes * multipliers.fillna(1) / magnifiers.fillna(1)
opens = opens * multipliers.fillna(1) / magnifiers.fillna(1)
dollar_volumes = closes.div(exchange_rates) * volumes
avg_dollar_volumes = dollar_volumes.mean()
sufficient_dollar_volumes = avg_dollar_volumes >= min_dollar_volume
if max_dollar_volume:
sufficient_dollar_volumes = sufficient_dollar_volumes & (avg_dollar_volumes <= max_dollar_volume)
monthly_returns = (closes - closes.shift(22)) / closes.shift(22)
monthly_returns = monthly_returns
monthly_returns = monthly_returns.where((monthly_returns > -0.9) & (monthly_returns < 9), 0)
monthly_std = monthly_returns.std()[sufficient_dollar_volumes].mean()
cc_returns = closes.pct_change()
# Ignore outliers
cc_returns = cc_returns.where((cc_returns > -0.5) & (cc_returns < 1))
cc_std = cc_returns.std()[sufficient_dollar_volumes].mean()
co_returns = (opens - closes.shift()) / closes.shift()
co_returns = co_returns.where((co_returns > -0.5) & (co_returns < 1))
co_std = co_returns.std()[sufficient_dollar_volumes].mean()
oc_returns = (closes - opens) / opens
oc_returns = oc_returns.where((oc_returns > -0.5) & (oc_returns < 1))
oc_std = oc_returns.std()[sufficient_dollar_volumes].mean()
return {
"monthly": monthly_std,
"daily": cc_std,
"intraday (open-to-close)": oc_std,
"overnight (close-to-open)": co_std,
}
def get_daily_std_by_year(db, years=[2015,2016,2017,2018], min_dollar_volume=0, max_dollar_volume=None, universes=None):
"""
Returns the average daily standard deviation for each of the years.
"""
all_years = {}
for year in years:
print(year)
prices = get_prices(
db,
start_date="{0}-01-01".format(year),
end_date="{0}-12-31".format(year),
universes=universes,
fields=["Close","Open","Volume"])
closes = prices.loc["Close"]
opens = prices.loc["Open"]
volumes = prices.loc["Volume"]
securities = get_securities_reindexed_like(closes, fields=["Currency","Multiplier","PriceMagnifier"])
currencies = securities.loc["Currency"]
multipliers = securities.loc["Multiplier"]
magnifiers = securities.loc["PriceMagnifier"]
exchange_rates = currencies.applymap(lambda x: rates[x])
exchange_rates = exchange_rates.reindex(closes.index, method="ffill")
closes = closes * multipliers.fillna(1) / magnifiers.fillna(1)
opens = opens * multipliers.fillna(1) / magnifiers.fillna(1)
dollar_volumes = closes.div(exchange_rates) * volumes
avg_dollar_volumes = dollar_volumes.mean()
sufficient_dollar_volumes = avg_dollar_volumes >= min_dollar_volume
if max_dollar_volume:
sufficient_dollar_volumes = sufficient_dollar_volumes & (avg_dollar_volumes <= max_dollar_volume)
cc_returns = closes.pct_change()
# Ignore outliers
cc_returns = cc_returns.where((cc_returns > -0.5) & (cc_returns < 1))
cc_std = cc_returns.std()[sufficient_dollar_volumes].mean()
all_years[year] = cc_std
return all_years
Countries for which numbers should be run:
countries = (
# North America
("usa-stk-1d", "United States"),
("canada-stk-1d", "Canada"),
("mexico-stk-1d", "Mexico"),
# Asia
("australia-stk-1d", "Australia"),
("japan-stk-1d", "Japan"),
("hongkong-stk-1d", "Hong Kong"),
("singapore-stk-1d", "Singapore"),
# Europe
("spain-stk-1d", "Spain"),
("netherlands-stk-1d", "Netherlands"),
("uk-stk-1d", "United Kingdom"),
("germany-stk-1d", "Germany"),
("france-stk-1d", "France"),
("belgium-stk-1d", "Belgium"),
("swiss-stk-1d", "Switzerland"),
("austria-stk-1d", "Austria"),
("sweden-stk-1d", "Sweden"),
("italy-stk-1d", "Italy"),
)
Run numbers for each country:
volatilities = {}
start_date = "2018-01-01"
for db, country in countries:
print(country)
volatilities[country] = get_std(db, start_date)
volatilities = pd.DataFrame(volatilities).T * 100
United States Canada Mexico Australia Japan Hong Kong Singapore Spain Netherlands United Kingdom Germany France Belgium Switzerland Austria Sweden Italy
import seaborn as sns
import matplotlib.pyplot as plt
sns.set(palette=sns.color_palette('deep', n_colors=3))
ax = volatilities.drop("monthly", axis=1).sort_values("daily", ascending=False).plot(kind="bar", title="Volatility of returns (all stocks)", figsize=(16,6))
ax.set_ylabel("Standard deviation (%)")
Text(0,0.5,'Standard deviation (%)')
Repeat with a minimum dollar volume threshold of $1M USD:
volatilities = {}
start_date = "2018-01-01"
for db, country in countries:
print(country)
volatilities[country] = get_std(db, start_date, min_dollar_volume=1000000)
volatilities = pd.DataFrame(volatilities).T * 100
United States Canada Mexico Australia Japan Hong Kong Singapore Spain Netherlands United Kingdom Germany France Belgium Switzerland Austria Sweden Italy
ax = volatilities.drop("monthly", axis=1).sort_values("daily", ascending=False).plot(kind="bar", title="Volatility of returns for stocks with daily average dollar volume above $1M USD", figsize=(16,6))
ax.set_ylabel("Standard deviation (%)")
Text(0,0.5,'Standard deviation (%)')
Repeat with a maximum dollar volume threshold of $1M USD:
volatilities = {}
start_date = "2018-01-01"
for db, country in countries:
print(country)
volatilities[country] = get_std(db, start_date, max_dollar_volume=1000000)
volatilities = pd.DataFrame(volatilities).T * 100
United States Canada Mexico Australia Japan Hong Kong Singapore Spain Netherlands United Kingdom Germany France Belgium Switzerland Austria Sweden Italy
ax = volatilities.drop("monthly", axis=1).sort_values("daily", ascending=False).plot(kind="bar", title="Volatility of returns for stocks with daily average dollar volume below $1M USD", figsize=(16,6))
ax.set_ylabel("Standard deviation (%)")
Text(0,0.5,'Standard deviation (%)')
volatilities = {}
for db, country in countries:
print(country)
volatilities[country] = get_daily_std_by_year(db, min_dollar_volume=1000000)
volatilities = pd.DataFrame(volatilities).T * 100
United States 2015 2016 2017 2018 Canada 2015 2016 2017 2018 Mexico 2015 2016 2017 2018 Australia 2015 2016 2017 2018 Japan 2015 2016 2017 2018 Hong Kong 2015 2016 2017 2018 Singapore 2015 2016 2017 2018 Spain 2015 2016 2017 2018 Netherlands 2015 2016 2017 2018 United Kingdom 2015 2016 2017 2018 Germany 2015 2016 2017 2018 France 2015 2016 2017 2018 Belgium 2015 2016 2017 2018 Switzerland 2015 2016 2017 2018 Austria 2015 2016 2017 2018 Sweden 2015 2016 2017 2018 Italy 2015 2016 2017 2018
import seaborn as sns
import matplotlib.pyplot as plt
sns.set(palette=sns.color_palette('deep', n_colors=4))
ax = volatilities.sort_values(2018, ascending=False).plot(kind="bar", title="Volatility of daily returns for stocks with daily average dollar volume above $1M USD", figsize=(16,6))
ax.set_ylabel("Standard deviation (%)")
Text(0,0.5,'Standard deviation (%)')
# Get top 3 most volatile by year
volatilities.apply(lambda x: pd.Series(x.sort_values(ascending=False).head(3).index, index=["1st","2nd","3rd"]))
| 2015 | 2016 | 2017 | 2018 | |
|---|---|---|---|---|
| 1st | Hong Kong | Japan | Hong Kong | Hong Kong |
| 2nd | Japan | Canada | Canada | Canada |
| 3rd | Canada | Hong Kong | Japan | Japan |