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These scripts achieve these goals:

• Normally a stoploss order gets trigerred based on the last trade price. These scripts will create a stoposs order in DAS Trader such that it gets trigerred based on the Ask or the Bid (although it can be modified to use the last trade price for the trigger).
• The scripts will break a big order into multiple smaller orders of the desired size. For example, if we have 30k shares and we want to exit the full position once the stop is trigerred and we want to execute the order in the chunks of 4k shares, then the script will automatically do this. Some brokers some times take a long time to process a large order, so breaking into smaller chunks solves this problem.
• When you add to an existing position, then the stoploss order does not need to be updated as it reads the position size when the stop is trigered and not when it is created.

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In the initialization code, first find out the exchanges for the symbols and accordingly sort them into two buckets:

class SchwabStreamingClient:

    #=====================================================================================
    def __init__(self):
        # All one time initialization should be done here
        self.client = schwab.auth.client_from_token_file(
                api_key=client_id,

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This is what I do when I do manual auth:

    def _create_token_meta_file(self):
        # Create a file to save the timestamp for the last time the refresh token was generated
        meta_path = pathlib.Path(self._token_path).with_suffix(".meta.json")
        # First delete the meta file if it exists
        if os.path.exists(meta_path):
            os.remove(meta_path)
        # Now create the meta file with the current timestamp

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import math
import pandas


# ======================================================================================================================
def convert_data_for_timeframe(price_data, target_timeframe):
    """
    Convert 1-min price data to a new timeframe.
    Reference Links
       * https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.resample.html

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# This script is used to manually authenticate the user with the Schwab API.
# It deletes the token file at the beginning. It also creates a meta file with the current timestamp.

import datetime
import json
import os
import pathlib
import pytz
import sys

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# This script is used to manually authenticate the user with the Schwab API.
# Delete the token file before running this script.

import schwab

from environment.instance_settings import schwabSettings

client_id = schwabSettings.SCHWAB_APP_ID
client_secret = schwabSettings.SCHWAB_APP_SECRET

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import datetime
import json
import os
import pytz
import sys

import schwab

from environment.instance_settings import schwabSettings

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import json
import logging
import os
import sys

from schwab import auth
from schwab.orders.common import *
from schwab.orders.generic import OrderBuilder

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import json
import logging
import os
import sys

from schwab import auth
from schwab.orders.common import *
from schwab.orders.generic import OrderBuilder

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import json
import requests

account_hash = 'dummy'
access_token = 'dummy'


def place_order(order) -> str:
    url = f"https://api.schwabapi.com/trader/v1/accounts/{account_hash}/orders"
    headers = {