Price Data: OHLCV & Timeframes

Understanding Price Data

All algorithmic strategies start with price data. Understanding how price data is structured—and its limitations—is foundational.

What is OHLCV?

• Open: The first trade price of the period
• High: The highest price traded during the period
• Low: The lowest price traded during the period
• Close: The last trade price of the period (most important for technical analysis)
• Volume: Total number of shares traded during the period

Each of these is a separate data point. Your algorithm will consume OHLCV bars and generate trading signals from them.

import pandas as pd

# Loading price data
df = pd.read_csv('AAPL_daily.csv', parse_dates=['Date'])
# df columns: Date, Open, High, Low, Close, Adjusted Close, Volume

print(df.head())
#            Date    Open    High     Low  Close  Adj Close    Volume
# 2024-01-01  191.00  195.50  190.75  194.45  194.23  5234000

Adjusted vs. Unadjusted Prices

Stocks undergo corporate actions: dividend payments and stock splits. If you don't adjust for these, your backtests will show massive false losses.

• Stock split (2-for-1): One share becomes two. Unadjusted price would show a 50% "loss" that didn't actually happen.
• Dividend ($1.00): Price drops by $1.00 on ex-dividend date. Unadjusted data shows a decline that should have been a total return.

Timeframes: The Tradeoff

Lower timeframes = more signals but also more false signals (noise). Higher timeframes = cleaner signals but fewer opportunities.