Dukascopy Historical Data

| Data Type | Description | |-----------|-------------| | Tick Data | Individual trades or price updates, captured with millisecond timestamps | | Bar Data | Aggregated OHLCV data for any custom timeframe | | Volume Data | Trading volume at bid and ask prices | | Order History | Historical order records for account analysis |

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🔹 – Data includes actual traded spreads and volume from their liquidity pool, not synthetic approximations. dukascopy historical data

Dukascopy provides several ways to access their data, ranging from manual downloads to automated API feeds. 1. Dukascopy Historical Data Feed (Web Widget)

Dukascopy has distinguished itself in a competitive market by fostering a , giving clients and researchers full insight into its historical price feeds. This data is crucial for confidence in the backtesting process, and the broker's philosophy is to provide a historical data stream that is ideal for strategy development and testing, with comprehensive access to tick-by-tick quotations. | Data Type | Description | |-----------|-------------| |

Dukascopy’s proprietary platform, JForex, allows users to download, store, and test strategies directly against their historical tick data library. 3. API Access/Data Feeds

Dukascopy's commitment to transparency is reflected in its provision of historical price data. The bank states that "transparency of historical prices is an essential element for strategy development," and it provides a consistent historical data stream for strategy development and back-testing with full access to tick-by-tick quotations. This approach is designed to ensure high reliability for backtesting and to eliminate any possibility of price manipulation by the broker. Dukascopy provides several ways to access their data,

Historical records for many instruments go back over a decade, allowing for extensive backtesting across different market regimes. Why Choose Dukascopy for Historical Data?

Tickstory is a well-known tool specifically optimized for MetaTrader 4. It automates the entire pipeline: downloading Dukascopy ticks, converting them into MT4 .fxt (tester) and .hst (history) files, and launching MT4 with launcher patches to bypass standard broker data overwrites. 3. JForex API (Native)

However, the key to successful and reliable backtesting is not just having data—it's understanding its nuances. By being aware of the potential limitations, such as data gaps, periodic updates, and differences between providers, you can build a robust research process. Use the access method that best fits your workflow, whether it's a simple CSV download, the powerful JForex API, or a Python library. Treat backtesting as a serious analytical endeavor, always verify your data, and you'll be well-equipped to use Dukascopy historical data to its fullest potential.

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