80 lines
2.0 KiB
Plaintext
80 lines
2.0 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 7,
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"id": "0ba75c87",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Data and analysis libraries\n",
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"import polars as pl # Fast dataframes for financial data\n",
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"import numpy as np # Numerical computing library\n",
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"from datetime import datetime, timedelta # Date and time operations\n",
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"import random\n",
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"\n",
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"# Machine learning libraries \n",
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"import torch # PyTorch framework\n",
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"import torch.nn as nn # Neural network modules\n",
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"import torch.optim as optim # Optimization algorithms\n",
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"import research # Model building and training utilities\n",
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"\n",
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"# Visualization and \n",
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"import altair as alt # Interactive visualization library\n",
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"\n",
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"# data sources\n",
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"import binance # Binance market data utilities"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"id": "ee4d8884",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"CUDA Available: True\n",
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"CUDA Version: 13.2\n"
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]
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}
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],
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"source": [
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"print('CUDA Available:', torch.cuda.is_available())\n",
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"print('CUDA Version:', torch.version.cuda)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "e0f64bf2",
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"metadata": {},
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"source": [
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"# Part 1: ML Model in PyTorch"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "study (3.14.5)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.14.5"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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