Rebalance a J.P. Morgan (Chase) investment portfolio from a CSV export of positions

chase-investment-rebalance.py

#!/usr/bin/env python3

import csv
import decimal
import itertools
import sys

def parse_desired_weights(weights_file):
    weights = { e["Category"]: decimal.Decimal(e["Desired Weight"]) for e in csv.DictReader(weights_file) }
    if sum(weights.values()) != 1:
        raise Exception("Desired weights do not sum to 100%")
    return weights

def parse_holding_categories(holding_categories_file):
    return { e["Ticker"]: e["Category"] for e in csv.DictReader(holding_categories_file) }

def aggregate_positions(positions_file, holding_categories):
    category_values = {}
    # Read until blank line separating data from footnotes
    position_lines = itertools.takewhile(lambda r: r.strip() != "", positions_file)
    for entry in csv.DictReader(position_lines):
        value = decimal.Decimal(entry["Value"].replace(",", ""))
        holding = entry["Ticker"] or entry["CUSIP"]
        if holding:
            category = holding_categories.get(holding)
            if not category:
                print("Uncategorized holding {}".format(entry["Ticker"]))
        else:
            print("Unidentified holding valued at {}".format(value))
        if not (holding and category):
            category = "Uncategorized"
        if category not in category_values:
            category_values[category] = decimal.Decimal("0.00")
        category_values[category] += value
    return category_values

def calculate_rebalancings(category_values, desired_weights):
    rebalancings = []
    total_value = sum(category_values.values())
    # Ensure that categories specifically named in desired weights are listed first
    # followed by "extra" categories in the current values
    categories = list(desired_weights.keys()) + list(set(category_values.keys()).difference(desired_weights.keys()))
    for category in categories:
        desired_value = total_value * desired_weights.get(category, 0)
        actual_value = category_values.get(category, 0)
        difference = desired_value - actual_value
        if abs(difference) >= 0.01:
            rebalancings.append((category, difference))
    return rebalancings

if len(sys.argv) < 4:
    print("Usage: {} [desired weights] [holding categories] [portfolio export]".format(sys.argv[0]))
    sys.exit(1)

with open(sys.argv[1]) as weights_file:
    desired_weights = parse_desired_weights(weights_file)

with open(sys.argv[2]) as holding_categories_file:
    holding_categories = parse_holding_categories(holding_categories_file)

with open(sys.argv[3]) as positions_file:
    category_values = aggregate_positions(positions_file, holding_categories)

rebalancings = calculate_rebalancings(category_values, desired_weights)

for rebalancing in rebalancings:
    print("{:20}: {:+0.2f}".format(*rebalancing))