Data ~repack~ — Dukascopy Historical

Because JForex is so slow, many developers write scripts to scrape the data directly from Dukascopy's servers using their public API links.

This public link is valid for 7 days and shares a thread, including any personal information you added. This link or copies made by others cannot be deleted. If you share with third parties, their policies apply. Can’t copy the link right now. Try again later. dukascopy historical data

import pandas as pd # Convert tick list to Pandas DataFrame df = pd.DataFrame(ticks, columns=['Timestamp', 'Ask', 'Bid', 'AskVolume', 'BidVolume']) df['Timestamp'] = pd.to_datetime(df['Timestamp'], unit='ms') df.set_index('Timestamp', inplace=True) # Resample to 5-Minute Mid-Price Candles ohlc = df['Bid'].resample('5Min').ohlc() Use code with caution. Importing Data into MetaTrader 4 and 5

Most reliable, includes volume. Cons: Manual download is slow for large ranges (5+ years). You must babysit the download. | Feature | Dukascopy | ForexTickData | TickStory

Use Tickstory or QuantDataManager to export the data as .hst (for charts) and .fxt (for the Strategy Tester).

Access to tick-level data (every bid/ask quote). If you share with third parties, their policies apply

Algo traders can optimize parameters (stop-loss, take-profit, entry rules) over a decade of data, avoiding curve-fitting by using out-of-sample testing on later years.

Hours range from 00 to 23 in Coordinated Universal Time (UTC). Raw Binary Decoding

Over 1,000 instruments , including major and minor Forex pairs, precious metals, cryptocurrencies, stocks, and bonds.

: Community tools like duka (Python) or dukascopy-node enable bulk, multi-threaded automated downloads.