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FTSE 100 (UK100) Historical Data CSV Free Download

UK100 concentrates its volatility in the London open, which makes session-filtered EAs behave very differently than on US indices. This dataset includes the Brexit-period volatility of 2019-2020, the 2022 gilt shock and the recovery into 2026 — with intraday detail down to M30/H1 for session studies.

434,144

OHLC candles across 9 timeframes.

M1 → MN1

Nine separate CSV files in one ZIP.

Jul 2019 – Oct 2026

Includes the 2020 crash and 2022 bear market.

CSV

Timestamp, Open, High, Low, Close, TickVolume, Spread.

6.66 MB

ZIP size, plain uncompressed CSV inside.

UK100m

MT5 export symbol — rename to match your broker.

UK100 Historical Data — Timeframe Breakdown

FileRowsFromTo
UK100m_PERIOD_M1_OHLC.csv100,0002026-06-292026-10-09
UK100m_PERIOD_M5_OHLC.csv100,0002025-04-252026-10-09
UK100m_PERIOD_M15_OHLC.csv100,0002022-05-042026-10-09
UK100m_PERIOD_M30_OHLC.csv80,1272019-07-162026-10-09
UK100m_PERIOD_H1_OHLC.csv40,4492019-07-162026-10-09
UK100m_PERIOD_H4_OHLC.csv10,9752019-07-162026-10-09
UK100m_PERIOD_D1_OHLC.csv2,1272019-07-162026-10-09
UK100m_PERIOD_W1_OHLC.csv3782019-07-142026-10-04
UK100m_PERIOD_MN1_OHLC.csv882019-07-012026-10-01

M1, M5 and M15 contain the most recent 100,000 candles (the MetaTrader 5 terminal window). M30 and above carry the full broker history since July 2019.

CSV Columns Explained

ColumnMeaningBacktesting Note
TimestampBroker candle open time (server time).Verify the server offset before aligning with another data source.
Open, High, Low, CloseUS500 candle prices.Validate High ≥ max(Open, Close) and Low ≤ min(Open, Close).
TickVolumeBroker tick activity inside the candle.Not centralized volume — use it as an activity proxy.
SpreadRecorded broker spread.Keep it: index CFDs widen at the cash open and rollover.

How to Use the UK100 Data

  1. Download the ZIP and extract the nine CSV files.
  2. For MT5: import into the Strategy Tester or rebuild the US500 symbol history; match the exact symbol name your broker uses (US500, SPX500, SP500, USA500).
  3. For Python: load with pandas, parse the Timestamp column, sort ascending and build features (returns, range, session flags).
  4. Run a data-quality pass first: duplicate timestamps, OHLC violations and session gaps are the three checks that catch most bad imports.
  5. Include spread and the overnight financing behavior when you interpret results — index CFDs are not futures.
import pandas as pd

df = pd.read_csv("UK100m_PERIOD_M30_OHLC.csv")
df["Timestamp"] = pd.to_datetime(df["Timestamp"])
df = df.sort_values("Timestamp").set_index("Timestamp")

df["range"] = df["High"] - df["Low"]
df["body"]  = (df["Close"] - df["Open"]).abs()
df["session"] = pd.cut(df.index.hour, bins=[-1, 7, 13, 21, 24],
                       labels=["Asia", "London", "NewYork", "LateNY"])

print(df.tail())
print(df[["range", "body", "TickVolume", "Spread"]].describe())

Also Known As

UK100 is listed under several broker names: FTSE100, UK100Cash, FTSE 100 Index CFD. The dataset works with any of them — rename the symbol to match your MT5 Market Watch exactly.

UK100 Historical Data — FAQ

Does the FTSE 100 data include the Brexit volatility?

Yes — the daily and M30-H1 series start in July 2019, covering the October 2019 Brexit deal rally, the 2020 crash and the 2022 gilt-market shock.

What timezone are the timestamps in?

All timestamps are the broker server time from the MT5 export (typically GMT+2/GMT+3 with DST). When comparing with London local time, subtract the server offset first.

Can I use UK100 data for a session-based EA?

That is the ideal use. Filter the M30/H1 candles by hour to isolate the London open (08:00-10:00 London) and test whether your entry logic actually works in that window versus the US session.

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