rajvermacas

Analyze the uploaded tradebook using FIFO matching and provide the following metrics only for realized/closed trades.

Disable 1. git worktrees 2. sub agent driven development 3. test driven development

Keep the architecture lean, concise and simple.
Always keep a check on the number of lines in the file. If the file exceeds the limit of 800 lines, break it down into multiple files.
Always keep function within 100 lines of code and it should follow single responsibility principle

Here are the essential tmux commands to get you started:

Starting and Managing Sessions

	//@version=5
	indicator("Fixed Horizontal Support Levels", shorttitle="Fixed Supports", overlay=true)

	// ==================== INPUT SETTINGS ====================
	len_left = input.int(100, "Line Length Left", minval=10, maxval=500, group="Line Settings")
	len_right = input.int(100, "Line Length Right", minval=10, maxval=500, group="Line Settings")

	// -3% Level
	color3 = input.color(color.new(color.red, 0), " -3% Line Color", group=" -3% Level")
	thick3 = input.int(2, " -3% Line Thickness", minval=1, maxval=5, group=" -3% Level")

	//@version=5
	indicator("ADR Percentage", shorttitle="ADR%", overlay=false)

	len = input.int(20, title="Length", minval=1)
	tf = input.timeframe("1D", title="Timeframe")
	threshold = input.float(5.0, title="Threshold %", minval=0.0)

	adrPct = request.security(syminfo.tickerid, tf, 100 * (ta.sma(high / low, len) - 1))

	plot(adrPct, title="ADR%", color=color.blue, linewidth=2)

	Complete Summary: Deploying a Scikit-Learn Model on Databricks Model Serving
	What We Accomplished
	Successfully trained, registered, and deployed a scikit-learn Linear Regression model as a REST API endpoint on Databricks Model Serving.

	Step-by-Step Process
	1. Initial Setup & Understanding
	Question: Can scikit-learn models trained on Databricks be served using Model Serving?
	Answer: Yes! Databricks supports custom models packaged in MLflow format, including scikit-learn.
	2. Reviewed Existing Notebook (PocML)
	Cell 1: Basic model training

	javascript:(function(){
	if (!window._capturingLogs) {
	window._capturingLogs = true;
	window._logs = [];
	const originalLog = console.log;
	console.log = function(...args) {
	window._logs.push(args.map(a => {
	try { return JSON.parse(JSON.stringify(a)); } catch(e) { return String(a); }
	}));
	originalLog.apply(console, args);

	import os
	from openai import OpenAI

	# Setup client
	client = OpenAI(
	api_key=os.getenv("AZURE_OPENAI_API_KEY"),
	base_url="https://YOUR-RESOURCE-NAME.openai.azure.com/openai/v1/"
	)

	# Send a message to o3

	# filepath="C:\Users\<username>\.wslconfig" - remove this line

	[wsl2]
	memory=16GB