Python OOP, File I/O & Dependency Management with Poetry

# Without this, Date is the DataFrame index — not a column
data = yf.download("^NSEI", start="2024-01-01")

# This promotes the index into a proper "Date" column
data.reset_index(inplace=True)   # ← the fix

data.to_csv("nifty50.csv", index=False)

# Now csv.DictReader sees: Date, Open, High, Low, Close, Volume
# And row["Date"] works correctly

import os
import yfinance as yf
import csv

file_name = "nifty50.csv"

# ── Step 1: Download only if CSV doesn't already exist ───
if os.path.exists(file_name) and os.path.getsize(file_name) > 0:
    print("CSV already exists. Skipping download.")
else:
    print("Downloading NIFTY50 data...")
    data = yf.download("^NSEI", start="2024-01-01")
    data.reset_index(inplace=True)   # moves Date from index → column
    data.to_csv("nifty50.csv", index=False)
    print("NIFTY50 CSV saved successfully")


# ── Step 2: Define the FinancialRecord class ─────────────
class FinancialRecord:
    def __init__(self, date, open_price, high, low, close):
        self.date       = date
        self.open_price = open_price
        self.high       = high
        self.low        = low
        self.close      = close

    def summary(self):
        print(
            f"Date: {self.date} | "
            f"Open: {self.open_price} | "
            f"Close: {self.close}"
        )

    def is_bullish(self):
        return self.close > self.open_price


# ── Step 3: Load CSV rows into FinancialRecord objects ───
records = []

with open(file_name, "r") as file:
    csv_reader = csv.DictReader(file)

    for row in csv_reader:
        record = FinancialRecord(
            row["Date"],
            row["Open"],
            row["High"],
            row["Low"],
            row["Close"]
        )
        records.append(record)


# ── Step 4: Print first 5 records with bullish/bearish ──
for record in records[:5]:
    record.summary()

    if record.is_bullish():
        print("Bullish Day")
    else:
        print("Bearish Day")

    print("-" * 40)

[project]
name = "ai-architect-roadmap"
version = "0.1.0"
description = "AI Architect learning roadmap projects using Python, AI, and BFSI examples"
authors = [
    {name = "Prabhu"}
]
requires-python = ">=3.11"
dependencies = [
    "pandas (>=3.0.3,<4.0.0)",
    "yfinance (>=1.3.0,<2.0.0)",
    "matplotlib (>=3.10.9,<4.0.0)"
]

[build-system]
requires = ["poetry-core>=2.0.0,<3.0.0"]
build-backend = "poetry.core.masonry.api"

AI-Architect-Roadmap/
│
├── pyproject.toml          # ← NEW: Poetry project manifest
├── poetry.lock             # ← NEW: Exact dependency versions
│
├── ai_dev/                 # conda environment (not committed to git)
│
├── Day1/
│   ├── basics.py
│   ├── day1_stock_project.py
│   ├── aapl_stock_data.csv
│   └── chart.pdf
│
└── Day2/
    ├── day2_financial_project.py   # ← NEW: OOP + CSV loading
    └── nifty50.csv                 # ← NEW: NIFTY50 historical data

# Class — the blueprint
class FinancialRecord:
    pass

# Instance — one specific object created from the blueprint
record = FinancialRecord(...)

# __init__ — constructor; called automatically on creation
def __init__(self, date, close):
    self.date  = date     # instance attribute
    self.close = close    # each instance has its own copy

# Method — a function that belongs to the class
# Always takes self as first argument
def summary(self):
    return f"{self.date}: {self.close}"

# Calling a method on an instance
print(record.summary())

# Storing many instances in a list — the standard pattern
records = []
records.append(FinancialRecord(...))

# Iterating — same as any list
for r in records:
    print(r.summary())