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# Byte-compiled / optimized / DLL files
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3.13

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# kraken-stoploss
## Linux documentation stnadards
All commands are written in a way that is predictable
There are two basic ways to change a way a program behaves. Options, and arguments
Options are preceded by a single dash when a single letter, and a double dash when a word. e.g.
ls -l
git pull --all
Arguments are not proceded by dashes, and represent fundimental actions that the program can perfrom
git checkout <branchname>
Arguments that are wrapped in brackets have a special meanings. The type of bracket denotes
what the meaning is. The word in the bracket is the type of argument, not what you actually
want to pass. It's a descriptive of what you should put in there, and the actual string you
use is variable.
Arrow brackets are used to denote mandatory arguments. You must always provide these options.
git checkout <branchname>
Becomes
git checkout influx
Arguments in square brackets are used to denote optional arguments. They, by their nature, are
optional, but can effect the way the program behaves
ls -l [directory]
Becomes:
ls -l /bin
## bash idiosyncrosies
In Unix (Linux is a Unix-like, not all Unixes are Linuxes) directories are nested using forward
slashes, unlike Windows, which uses backslashes.
A directory path might look like the following
/bin/bash
Also, in bash, backslashes are an escape character. This is used to prevent certain symbols from
being interpreted by the command interpeter, or shell.
Try the following
echo $PATH
echo \$PATH
This should provide some insight in to how these are different. $PATH is what is called an
environment variable, and all variables in bash are proceded by the $ character.
Because backslash is a special character
## BEFORE YOU WRITE ANY CODE DURING A SESSION
git pull --all
## To add a new feature
The `-b` option in `git checkout` tells git that you want to create a new branch.
git checkout -b <branchname>
## After you are done developing
make sure you are on the branch you want, i.e. the one with your new features (see to add a new feature)
git checkout <branchname>
Add the changed files
git add .
Create a commit
git commit -m "<commit message>"
Push your changes up into github or whatever git website you're using
git push origin <branchname>
e.g.
git push origin influx
You can alternatively use the following, to sync all changes,
git push --all
If this fails, try just pushing the new branch you're working on. It's always right to
create a new branch for new code when you are working with others. This allows the maintainer
of the project to properly merge code into the correct repository.
## NEVER PUSH TO `main` or `master`
In order to maintain a clean project without any merge conflicts, we must make sure to never push
to the `main` or `master` branches. These branches are special and are meant to contain the most
recent working version of the code. Changes to these branches are *always* done via merge request

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#!/bin/bash
ln -s pre-commit ./.git/hooks/pre-commit
chmod u+x pre-commit
python3 -m venv .venv
source .venv

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#!/bin/bash
current_branch="$(git branch --show-current)"
for protected_branch in "main" "stable"; do
if [[ "$protected_branch" == "$current_branch" ]]; then
echo "ERROR: local branch $current_branch is protected"
exit 1
fi
done
exit 0

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[project]
name = "kraken-stoploss"
version = "0.1.0"
description = "Add your description here"
readme = "README.md"
requires-python = ">=3.13"
dependencies = [
"influxdb3-python>=0.14.0",
"pandas>=2.3.1",
"websockets>=15.0.1",
]

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websockets
datetime
SQLAlchemy

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#!/usr/bin/env python
import asyncio
import websockets
import json
from datetime import datetime
import os
from influxdb_client_3 import InfluxDBClient3, InfluxDBError, WriteOptions
from influxdb_client_3 import write_client_options, Point
from influxdb_client_3
import pandas as pd
from IPython.display import display, HTML
from collections import deque
#TZ=timezone(offset=tzinfo.utcoffset())
BATCH_SIZE = 100
FLUSH_INTERVAL = 10_000
JITTER_INTERVAL = 2_000
RETRY_INTERVAL = 5_000
MAX_RETRIES = 5
MAX_RETRY_DELAY = 30_000
EXPONENTIAL_BASE = 2
WRITE_OPTIONS = {
"batch_size" = BATCH_SIZE,
"flush_interval" = FLUSH_INTERVAL,
"jitter_interval" = JITTER_INTERVAL,
"retry_interval" = RETRY_INTERVAL,
"max_retries" = MAX_RETRIES
}
TOKEN = os.environ('INFLUXDB3_AUTH_TOKEN')
HOST = os.environ('INFLUXDB3_TICKER_HOST')
ORG = os.environ('INFLUXDB3_TICKER_ORG')
DATABASE = os.environ('INFLUXDB3_TICKER_DATABASE')
PAIRS = os.environ('INFLUXDB3_CURRENCY_PAIRS').split(',')
request_body = {
"method": "",
"params": {
}
}
class BatchingCallback(object):
"""BatchingCallback - callback class for batching"""
def success(self, conf, data: str):
print(f"Written batch: {conf}")
def error(self, conf, data: str, exception: InfluxDBError):
print(f"Cannot write batch: {conf}, data: {data} due: {exception}")
def retry(self, conf, data:str, exception: InfluxDBError):
print(f"Retryable error occurs for batch: {conf}, data: {data} retry: {exception}")
class InfluxDBWriter:
"""
Manages buffering and batch writing data points to InfluxDB.
"""
def __init__(self, host, token, org, database, enable_gzip, write_options):
# Initialize Callback
self.callback = BatchingCallback()
# initialize write options
# 10_000 = 10ms
self.write_options = WriteOptions(batch_size=BATCH_SIZE,
flush_interval=FLUSH_INTERVAL,
jitter_interval=JITTER_INTERVAL,
retry_interval=RETRY_INTERVAL,
max_retries=MAX_RETRIES,
max_retry_delay=MAX_RETRY_DELAY,
exponetial_base=EXPONENTIAL_BASE
)
# write_client_options sets our client options for retries
# and passes the WriteOptions object
self.wco = write_client_options(success_callback=callback.success,
error_callback=self.callback.error,
retry_callback=self.callback.retry,
WriteOptions=self.write_options
)
# Initialize the influx client
self.client = InfluxDBClient3(
token=token,
host=host,
org=org,
database=database,
enable_gzip=enable_gzip,
write_client_options=self.wco
)
# Use SYNCHRONOUS write option for explicit control over flushing
# For higher throughput, consider ASYNCHRONOUS or BATCHING options provided by InfluxDB client.
# Here, we implement our own batching logic on top of SYNCHRONOUS.
self.buffer = deque()
self.batch_size = BATCH_SIZE
self.flush_interval = FLUSH_INTERVAL
self.last_flush_time = datetime.now()
self._flusher_task = None
print(f"InfluxDBWriter initialized for bucket: {self.bucket}")
async def write_point(self, point: Point):
"""Adds a single InfluxDB Point to the buffer and triggers flush if needed."""
self.buffer.append(point)
if len(self.buffer) >= self.batch_size:
print(f"Buffer size {len(self.buffer)} >= {self.batch_size}. Triggering immediate flush.")
await self._flush_buffer()
async def _flush_buffer(self):
"""Flushes the current buffer to InfluxDB."""
if not self.buffer:
return
points_to_write = list(self.buffer)
self.buffer.clear()
self.last_flush_time = datetime.now()
try:
print(f"Flushing {len(points_to_write)} points to InfluxDB...")
# The write_api.write method is blocking, so run it in a thread pool executor
# to avoid blocking the asyncio event loop.
await asyncio.to_thread(self.write_api.write, bucket=self.bucket, record=points_to_write)
print(f"Successfully flushed {len(points_to_write)} points.")
except Exception as e:
print(f"Error writing to InfluxDB: {e}")
# Optionally, re-add points to buffer or log for retry
for p in points_to_write:
self.buffer.appendleft(p) # Add back to front for retry
print(f"Re-added {len(points_to_write)} points to buffer due to error.")
async def _periodic_flusher(self):
"""Periodically flushes the buffer based on the flush interval."""
while True:
await asyncio.sleep(self.flush_interval)
if self.buffer and (datetime.now() - self.last_flush_time >= timedelta(seconds=self.flush_interval)):
print(f"Periodic flush triggered. Buffer size: {len(self.buffer)}")
await self._flush_buffer()
def start_flusher(self):
"""Starts the background periodic flusher task."""
if not self._flusher_task:
self._flusher_task = asyncio.create_task(self._periodic_flusher())
print("InfluxDB periodic flusher started.")
async def stop_flusher(self):
"""Stops the background periodic flusher task and flushes any remaining data."""
if self._flusher_task:
self._flusher_task.cancel()
try:
await self._flusher_task
except asyncio.CancelledError:
print("InfluxDB periodic flusher stopped.")
print("Flushing remaining points before shutdown...")
await self._flush_buffer()
self.client.close()
print("InfluxDB client closed.")
def get_ticker(symbol, request_body):
"""Get Kraken websocket initialization"""
request_body.update({"params": {
"channel": "ticker",
"symbol": symbol
}})
request_body.update({"method":"subscribe"})
return json.dumps(request_body)
async def ws_client(client):
"""
Ticker websocket client
"""
print("WebSocket Client Initialization started")
url = "wss://ws.kraken.com/v2"
async with websockets.connect(url) as ws:
await ws.send(get_ticker(["BTC/USD"], request_body))
while True:
msg = await ws.recv()
# upon receiving json message,
# parse out data that we want, then
# write to the database
data = json.loads(msg)
for datum in data["data"]:
new_data = {
symbol = datum["symbol"],
bid = datum["bid"],
bid_qty = datum["bid_qty"],
ask = datum["ask"],
ask_qty = datum["ask_qty"],
daily_high = datum["high"],
daily_low = datum["low"],
change = datum["change"],
change_pct = datum["change_pct"],
}
# add data to buffer
# asynchronously commit data to database
print(f"{datetime.now()} - {msg}")
async def write_data(client, data):
try:
# try to write the data
client.write(data, data_frame_measurement_name=None,
data_frame_tag_columns=None)
except Exception as e:
print(f"Error writing data point: {e}")
# non-blocking sleep to wait for data to be written
await asyncio.sleep(2)
while __name__ == "__main__":
# Initialize Callback
callback = BatchingCallback()
# initialize write options
# 10_000 = 10ms
write_options = WriteOptions(batch_size=100,
flush_interval=10_000,
jitter_interval=2_000,
retry_interval=5_000,
max_retries=5,
max_retry_delay=30_000,
exponetial_base=2
)
# write_client_options sets our client options for retries
# and passes the WriteOptions object
wco = write_client_options(success_callback=callback.success,
error_callback=callback.error,
retry_callback=callback.retry,
WriteOptions=write_options
)
# Initialize the influx client
client = InfluxDBClient3(
token=token,
host=host,
org=org,
database=database,
enable_gzip=True,
write_client_options=wco
)
now = pd.Timestamp.now(tz="UTC").floor("ms")
asyncio.run(ws_client(client))

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