trade-stat-logger

Want to be able to easily log trades of any type of security and measure performance and analyze several factors, such as risk or return distribution? Then check out trade-stat-logger.

• log trades: long and short
• get a statistical summary:
  • net profit: self-explanatory
  • drawdown: vertical distance between peak and trough of net profits
  • volatility of returns: measures deviation of returns distribution
  • kurtosis: measures tails of returns distribution
  • Kelly Criterion: estimate of optimal portfolio allocation
  • And more
• visualize performance:
  • visualize distribution of returns
  • analyze movement of gains or losses after every trade or over a period of time

install

pip install trade-stat-logger

Classes and methods

SimpleLogger Methods:

SimpleLogger(datetime_support=False)

Constructs a new SimpleLogger object. Set datetime_support=True if you want to log the time at which the trade executed. Ignore future dt key word arguments if you wish to not support datetime logging.

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log(security, shares, share_price, dt=None)

Log trades with this method. Shares is the number of shares you wish to purchase, set to negative to short/sell, and share_price is the price of a share. If dt is left as None, it will log current time, else set dt to a datetime object to log a custom time.

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log_cp(security, share_price, dt=None)

Clear a position of given security at given share_price and log it. If dt is left as None, it will log current time, else set dt to a datetime object to log a custom time.

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get_position(security)

Returns a tuple in the format (# shares, position size), position size is (# shares) * (average share price). It is slightly different from the format of get_positions.

get_positions()

Returns a dict of current security holdings in form of {security: {'shares': # shares, 'position_size': position size}, another_security: {'shares': # shares, 'position_size': position size}, ...}, where the key corresponding to a security is the string passed through the log() method.

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clear_all_positions(get_price_func, closure_date)

Clears all security holdings, which is advised before calling methods that measure performance of trades. get_price_func should be a function such that get_price_func(security, closure_date) == price of security. For example:

logger = SimpleLogger()
...log trades...
def get_price(security, date):
    return api.get_price(security, date)
logger.clear_all_positions(get_price, some_date)

However, if you logged the tickers of stocks, you can use an easier alternative: clear_stock_positions(closure_date) will clear all stock positions

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get_summary_statistics()

Returns a dictionary of various statistics in the form of {statistic1_name: value, statistics2_name: value,...}.

graph_statistics(time_axis=False, time_strformat='%m/%d/%Y', show_window=True)

Graphs the distribution of trade returns, net profit at the nth trade, and puts the summary statistics in a table, and shows it in a matplotlib popup window. If you would like to have the x-axis for the net profit graph be time, set time_axis=True, and optionally add a custom time format through time_strformat='%Y/%o/%u/%r format'. If you wish to get the plot, figure, and axes, the method will return these if you set show_window=False, and this will also mean the popup window will not appear.

Examples

Use within a simple Moving Average Crossover strategy here
Use within a Time Series Forecasting with Machine Learning strategy (Jupyter NB) here

from datetime import timedelta
from random import randint
logger = SimpleLogger(datetime_support=True)

day_counter = 1
for x in range(0, 100):
    logger.log('fried chicken futures', randint(1, 100), randint(1, 100), dt=datetime.now()-timedelta(days=day_counter))
    logger.log('fried chicken futures', randint(-100, 1), randint(1, 130), dt=datetime.now()-timedelta(days=day_counter))
    day_counter += 1

# by setting show_window to false, the graphs aren't plotted, but instead returned
# axes is 2x2
plt, fig, axes = logger.graph_statistics(show_window=False)
# alter as you like, such as axes[0][0].set_xlabel('asdf')

# get current positions in securities (which we will then clear)
print(logger.get_positions())

# example of how to use clear_all_positions(),
#   replace get_price() with a real api call or any other price getter
fake_api_method = lambda security, dt: len(security) + dt.timetuple().tm_yday
get_price = lambda security, dt: fake_api_method(security, dt)
logger.clear_all_positions(get_price, datetime.today())

# shows the statistics in a graphs in a pop up window. since it calls plt.show()
# when show_window is not set to false, all code beyond this point won't be executed
logger.graph_statistics()
    

Contact me

If you like my work and have any inquiries (library support or job related), feel free to contact me at [email protected].

Future features

• more time support for SimpleLogger, including time analysis on returns
• I will add a ComplexLogger, which will be built upon SimpleLogger, and it will support initial portfolio size, ROI, and many more.
• KellyLogger, which will dynamically return Kelly Criterion scores based on trade logs