Hello kalmar,

Very useful indeed, thank you!

I took the liberty of extending your script to make it create a complete Assets.csv file, grouped by asset type, for all Oanda instruments, including the correct RollLong and RollShort values converted to the account currency. PIPCost is also calculated in account currency.

One thing to still be aware of is that Oanda appears to sometimes triple or quadruple the rollover rates even when it is not a Wednesday or Friday. I guess that could be taken care of by a script which runs a few times per week and compares the rates per instrument (manually or automatically).

Code
from collections import defaultdict
import pandas as pd

import oandapyV20
import oandapyV20.endpoints.accounts as accounts
import oandapyV20.endpoints.pricing as pricing

ACCOUNT_ID = 'XXX-XXX-XXXXXXX-XXX'
TOKEN = 'xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx-xxxxxxxxxxxxxxxxxxxxx'
ACCOUNT_CURRENCY = 'EUR'
OUTPUT_FILENAME = 'AssetsOanda.csv'

client = oandapyV20.API(access_token=TOKEN)

# Retrieve all instruments for account
r = accounts.AccountInstruments(accountID=ACCOUNT_ID)
rv = client.request(r)
data = pd.json_normalize(pd.DataFrame(rv)['instruments'])

# Retrieve pricing data for all instruments
instrument_list = list(data['name'])
r = pricing.PricingInfo(accountID=ACCOUNT_ID, params={'instruments': ','.join(map(str, instrument_list))})
rv = client.request(r)
prices = pd.json_normalize(pd.DataFrame(rv)['prices']).set_index('instrument').rename(columns={'instrument': 'Name'})
prices[['closeoutAsk', 'closeoutBid']] = prices[['closeoutAsk', 'closeoutBid']].astype(float)
prices['Price'] = prices[['closeoutAsk', 'closeoutBid']].mean(axis=1)
prices['Spread'] = prices['closeoutAsk'] - prices['closeoutBid']

# Rename columns, set instrument column as index, join with prices, and convert data types as necessary
data = data.rename(columns={'name': 'Name',
                            'minimumTradeSize': 'LotAmount'}). \
            set_index('Name'). \
            join(prices[['Price', 'Spread']])
data.index = data.index.str.replace('_','/')               # Replace '_' with '/' in line with Zorro convention
convert_columns = ['LotAmount', 'marginRate', 'financing.longRate', 'financing.shortRate']
data[convert_columns] = data[convert_columns].astype(float)
data['currency'] = data.index.map(lambda c: c[-3:])

# Add Index as type, mainly for cosmetic reasons (where name ends in a number or in 'Index')
data.loc[(data['displayName'].str[-1].str.isnumeric()) | (data['displayName'].str[-5:]=='Index'),'type'] = 'INDEX'

# Store currency rates in dict, add inverse and necessary cross currency rates to be able to convert all
# assets to account currency (also add XAG as currency for XAU/XAG)
conversion_rate = data[data['type']=='CURRENCY']['Price'].to_dict()     # All OANDA currency rates
conversion_rate.update({pair[-3:]+"/"+pair[:3]: 1/rate
                        for pair, rate in conversion_rate.items()})     # Inverse of existing currencies
conversion_rate[f"{ACCOUNT_CURRENCY}/{ACCOUNT_CURRENCY}"] = 1
assert 'USD/'+ACCOUNT_CURRENCY in conversion_rate, "Cannot calculate account currency rates"
conversion_rate['XAG/USD'] = data.loc['XAG/USD']['Price']
# Add missing currencies as cross currency via USD
conversion_rate.update({currency+'/'+ACCOUNT_CURRENCY: conversion_rate[currency+'/USD'] * conversion_rate['USD/'+ACCOUNT_CURRENCY]
                        for currency in
                            (currency for currency in data['currency']
                             if currency+'/'+ACCOUNT_CURRENCY not in conversion_rate)})

# Add additional columns, calculate PipCost based on conversion rates
data['PIP'] = 10.**data['pipLocation']
data['PIPCost'] = data['PIP'] * (data['currency']+'/'+ACCOUNT_CURRENCY).map(conversion_rate)
data['Leverage'] = (1/data['marginRate']).astype(int)
data['MarginCost'], data['Commission'] = 0, 0
data['Symbol'] = data.index

# Adjust for weekend rate inflation on Wednesdays/Fridays if necessary
# data[['financing.longRate', 'financing.shortRate']] = data[['financing.longRate', 'financing.shortRate']] / 4

# Calculate RollLong and RollShort in account currency, for the quantities used by Zorro
financing_pos_size = defaultdict(lambda: 1, {'CURRENCY': 10000})    # Everything but CURRENCY defaults to 1
data['RollLong'] = data['financing.longRate']/365 * \
                   data['Price'] * \
                   data['type'].map(financing_pos_size) * \
                  (data['currency']+'/'+ACCOUNT_CURRENCY).map(conversion_rate)
data['RollShort'] = data['financing.shortRate']/365 * \
                    data['Price'] * \
                    data['type'].map(financing_pos_size) * \
                   (data['currency']+'/'+ACCOUNT_CURRENCY).map(conversion_rate)

EXPORT_COLUMNS = ['Price', 'Spread', 'RollLong', 'RollShort',
                  'PIP', 'PIPCost', 'MarginCost', 'Leverage',
                  'LotAmount', 'Commission', 'Symbol']
DISPLAY_COLUMNS = ['type', 'displayName', 'Price', 'Spread',
                   'financing.longRate', 'financing.shortRate',
                   'RollLong', 'RollShort',
                   'PIP', 'PIPCost', 'MarginCost', 'Leverage',
                   'LotAmount', 'Commission']
data.sort_values(['type', 'Name'], inplace=True)

# Write CSV with group headers
data[EXPORT_COLUMNS].head(0).to_csv(OUTPUT_FILENAME)
max((open(OUTPUT_FILENAME, 'a').write(f"### {data_type}\n"),
          data.loc[data_group, EXPORT_COLUMNS].to_csv(OUTPUT_FILENAME, float_format='%g', header=False, mode='a'))
    for data_type, data_group in data.groupby('type').groups.items())

print(data[DISPLAY_COLUMNS].to_string())