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Started new tutorial. Added a bunch of python packages such as pytorch

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Caleb Burke
2026-06-22 17:13:57 -07:00
parent c965a11c7d
commit 669271901d
7 changed files with 1984 additions and 51 deletions
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.gitignore
+3
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@@ -1 +1,4 @@
.env
__pycache__/
*.pyc
*.pyo
pyproject.toml
+22 -2
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@@ -1,13 +1,33 @@
[project]
name = "roadmap"
name = "study"
version = "0.1.0"
description = "Add your description here"
readme = "README.md"
requires-python = ">=3.14"
dependencies = [
"dotenv>=0.9.9",
"aiohttp>=3.14.1",
"altair>=6.2.1",
"binance>=0.3.110",
"cryptography>=49.0.0",
"jupyter>=1.1.1",
"matplotlib>=3.10.9",
"numpy>=2.4.6",
"polars>=1.41.2",
"python-dotenv>=1.2.2",
"seaborn>=0.13.2",
"torch>=2.12.1",
"tqdm>=4.68.3",
]
[[tool.uv.index]]
name = "pytorch-cu132"
url = "https://download.pytorch.org/whl/cu132"
explicit = true
[tool.uv.sources]
torch = [
{ index = "pytorch-cu132", marker = "sys_platform == 'linux' or sys_platform == 'win32'" },
]
torchvision = [
{ index = "pytorch-cu132", marker = "sys_platform == 'linux' or sys_platform == 'win32'" },
]
textbooks/reading/20260622012450_Introduction to Probability, Statistics, and Random Processes/ch1/example-problems.ipynb
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@@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"execution_count": 1,
"id": "c58309b2",
"metadata": {},
"outputs": [],
@@ -299,7 +299,7 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 2,
"id": "e589ecc1",
"metadata": {},
"outputs": [
@@ -312,13 +312,6 @@
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"2.25091334378745\n"
]
}
],
"source": [
@@ -769,7 +762,7 @@
],
"metadata": {
"kernelspec": {
"display_name": "roadmap (3.14.5)",
"display_name": "study (3.14.5)",
"language": "python",
"name": "python3"
},
tutorials/20260622164035_Let's Build a Quant Trading Strategy, MemLabs/README.md
+3
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@@ -0,0 +1,3 @@
# Youtube Playlist - Let's Build a Quant Trading Strategy
<https://www.youtube.com/playlist?list=PLdsqLas3-Kg3fM8VgykXBJ2gRIFBpKHjY>
tutorials/20260622164035_Let's Build a Quant Trading Strategy, MemLabs/part1.ipynb
+79
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@@ -0,0 +1,79 @@
{
"cells": [
{
"cell_type": "code",
"execution_count": 7,
"id": "0ba75c87",
"metadata": {},
"outputs": [],
"source": [
"# Data and analysis libraries\n",
"import polars as pl # Fast dataframes for financial data\n",
"import numpy as np # Numerical computing library\n",
"from datetime import datetime, timedelta # Date and time operations\n",
"import random\n",
"\n",
"# Machine learning libraries \n",
"import torch # PyTorch framework\n",
"import torch.nn as nn # Neural network modules\n",
"import torch.optim as optim # Optimization algorithms\n",
"import research # Model building and training utilities\n",
"\n",
"# Visualization and \n",
"import altair as alt # Interactive visualization library\n",
"\n",
"# data sources\n",
"import binance # Binance market data utilities"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "ee4d8884",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CUDA Available: True\n",
"CUDA Version: 13.2\n"
]
}
],
"source": [
"print('CUDA Available:', torch.cuda.is_available())\n",
"print('CUDA Version:', torch.version.cuda)"
]
},
{
"cell_type": "markdown",
"id": "e0f64bf2",
"metadata": {},
"source": [
"# Part 1: ML Model in PyTorch"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "study (3.14.5)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.14.5"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
tutorials/20260622164035_Let's Build a Quant Trading Strategy, MemLabs/research.py
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uv.lock
Generated
+762 -39
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@@ -4,7 +4,103 @@ requires-python = ">=3.14"
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