tradedesk-dukascopy 0.5.0

Dukascopy tick downloader and candle/tick exporter for backtesting workflows.

tradedesk-dukascopy

Dukascopy tick downloader and candle exporter for use in backtesting your trading strategies.

This tool downloads raw tick data from Dukascopy, converts it into clean, deterministic CSV candle files, and writes a metadata sidecar describing exactly how the data was produced.

It is designed to be run once per dataset, not repeatedly during backtests.

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Quick start

Install:

pip install tradedesk-dukascopy

Export 5-minute candles for EURUSD:

tradedesk-dc-export --symbols EURUSD \
  --from 2025-01-01 --to 2025-01-31 \
  --resample 5min \
  --out data \
  --cache-dir ./cache \
  --price-divisor 1000 \
  --workers 1

This produces:

data/
  EURUSD_5MIN_bid.csv
  EURUSD_5MIN_bid.csv.meta.json
  EURUSD_5MIN_ask.csv
  EURUSD_5MIN_ask.csv.meta.json

You can now point your backtest engine at the bid or ask CSV directly, depending on which price side you want to replay.

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Price scaling (--price-divisor)

Dukascopy tick prices are stored as integers or scaled values depending on the instrument.

This tool applies price scaling once, at export time, using --price-divisor.

Examples:

Instrument	Typical divisor
EURUSD	1000
USDJPY	100000
Indices	1 or 10

If unsure, use probe mode:

tradedesk-dc-export --symbols GBPSEK \
  --from 2025-07-01 --to 2025-07-01 \
  --probe

Probe mode prints sample ticks at different divisors without writing files.

GBPSEK: detected tick price format = int
GBPSEK @ 2025-07-01T00:00:00+00:00 (int): first 10 ticks
first tick raw: 2025-07-01T00:00:00.326000+00:00 bid_i 1297675 ask_i 1298619 vol 1.149999976158142
  divisor      1: bid 1297675.000000 ask 1298619.000000
  divisor     10: bid 129767.500000 ask 129861.900000
  divisor    100: bid 12976.750000 ask 12986.190000
  divisor   1000: bid 1297.675000 ask 1298.619000
  divisor  10000: bid 129.767500 ask 129.861900
  divisor 100000: bid 12.976750 ask 12.986190
using --price-divisor 1.0:
2025-07-01T00:00:00.326000+00:00 bid 1297675.0 ask 1298619.0 bid_vol 1.149999976158142
2025-07-01T00:00:01.128000+00:00 bid 1297800.0 ask 1298661.0 bid_vol 0.9200000166893005
2025-07-01T00:00:01.329000+00:00 bid 1297796.0 ask 1298621.0 bid_vol 0.9200000166893005
2025-07-01T00:00:03.335000+00:00 bid 1297796.0 ask 1298591.0 bid_vol 0.9200000166893005
2025-07-01T00:00:03.737000+00:00 bid 1297842.0 ask 1298695.0 bid_vol 1.149999976158142
2025-07-01T00:00:05.340000+00:00 bid 1297850.0 ask 1298655.0 bid_vol 0.9200000166893005
2025-07-01T00:00:06.542000+00:00 bid 1297862.0 ask 1298709.0 bid_vol 0.9200000166893005
2025-07-01T00:00:08.546000+00:00 bid 1297874.0 ask 1298709.0 bid_vol 0.9200000166893005
2025-07-01T00:00:10.556000+00:00 bid 1297877.0 ask 1298724.0 bid_vol 0.9200000166893005
2025-07-01T00:00:12.562000+00:00 bid 1297839.0 ask 1298684.0 bid_vol 1.149999976158142

Repairing an existing cache

If you already populated --cache-dir with the wrong price scale, the package ships a repair command:

tradedesk-dc-normalize --cache-dir ./cache --dry-run
tradedesk-dc-normalize --cache-dir ./cache --symbols EURUSD USDJPY

tradedesk-dc-normalize rewrites cached daily candle files in place when it detects prices that are clearly outside the expected range for a symbol. This covers both caches written with the default --price-divisor 1.0 for int32 tick feeds and older cache files affected by a bad inferred divisor.

The normalizer only updates the cached daily candle files under --cache-dir. If you already wrote range-level CSVs with --out, rerun your export command after normalizing so those output files are regenerated from the corrected cache.

Rescaling a cache that drifted off its own dominant scale

tradedesk-dc-normalize brings each day's prices into a hardcoded natural-units band (e.g. USDJPY 50–500). That is the wrong target when the downstream consumer expects prices at the symbol's existing scaled-cache convention (for instance the bulk of an FX/JPY cache exported with --price-divisor 10, leaving USDJPY at ~15 700 rather than ~157.0).

Use tradedesk-dc-rescale for that case. It finds the symbol's dominant cache scale (median of per-day medians) and snaps every off-scale day back onto it by a power-of-ten factor:

tradedesk-dc-rescale --cache-dir ./cache --dry-run
tradedesk-dc-rescale --cache-dir ./cache --symbols USDJPY

Days whose median cannot be reconciled to a power of ten of the dominant scale are reported as unfixable; delete and re-export those with the matching --price-divisor.

Write-time scale-discontinuity sentry

tradedesk-dc-export automatically refuses to commit a freshly-resampled daily CSV whose median close diverges by more than 3× from the medians of its neighbours already on disk. The bi5 hour files for that day are kept so the day can be retried with the matching --price-divisor. See tradedesk_dukascopy.scale_sentry for the failure mode this catches — typically a cache stitched together from multiple tradedesk-dc-export runs that used different --price-divisor values.

Data-quality audit scripts

The repository also ships three maintainer-oriented audit scripts under scripts/ for checking whether an existing local candle cache still looks healthy after exporter changes or upstream Dukascopy drift.

scripts/dukascopy_audit.py is a read-only local audit. It inspects the cached 1-minute bid/ask candles for each instrument and emits JSON covering:

• session-gap counts and longest intraday gap
• DST-transition day bar-count anomalies
• spread sanity percentiles
• stale-price runs

Example:

python scripts/dukascopy_audit.py \
  --cache ./cache \
  --instruments EURUSD GBPUSD USDJPY XAUUSD \
  --year-start 2024 \
  --year-end 2025 \
  --out /tmp/dukascopy_audit.json

scripts/dukascopy_cross_provider.py is a cross-provider check. It compares the local Dukascopy daily close series against ECB/Frankfurter reference rates for FX and Yahoo Finance reference closes for indices, metals, and commodity proxies.

Example:

python scripts/dukascopy_cross_provider.py \
  --cache ./cache \
  --instruments EURUSD GBPUSD USDJPY XAUUSD USA500IDXUSD \
  --start 2024-01-01 \
  --end 2025-12-31 \
  --out /tmp/dukascopy_cross_provider.json

scripts/audit_fx_scale.py is a focused FX scale-corruption audit. For each DD_{bid,ask}.csv.zst under <cache_dir>/<SYMBOL>, it flags day files whose median close falls outside an explicit FX-rate envelope (e.g. [0.30, 2.00] for NZDUSD-style 4-decimal FX), so caches that were exported with the wrong --price-divisor show up immediately. It reports per-year and day-of-week histograms, or with --print-dates emits one ISO date per line for shell pipelines.

Example:

python scripts/audit_fx_scale.py NZDUSD --cache-dir ./cache --min 0.30 --max 2.00
python scripts/audit_fx_scale.py NZDUSD --cache-dir ./cache --print-dates

These scripts are intended for maintainers validating cached data quality, not for the normal export path. dukascopy_cross_provider.py performs live HTTP requests to external reference feeds, so it requires internet access in addition to a populated local cache.

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Intended workflow

This tool is intended to be used as a data preparation step, not as part of your backtest runtime loop:

1. Download and export historical data once
2. Commit or archive the output CSV + metadata if applicable
3. Run fast, deterministic backtests against local files

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Output files and --cache-dir

When run, the tool will fetch new or missing raw data files from Dukascopy for the instrument(s) and periods that you specify. These are always compressed, hourly files. Once fetched, the files are converted to CSV format tick files and aggregated into daily files. When all 24 hour periods are available and the daily CSV file is written to the cache, the raw native files are discarded.

Dukascopy downloads are notoriously slow and unreliable due to rate limiting and limited resources available for their service. This tool has multiple strategies to address and work around those limitations, including retaining the raw files until a full daily file of CSV data can be written. Re-running the same tradedesk-dc-export is both safe and efficient - it will only attempt to fill in gaps and will finish very quickly where downloads or conversions are already cached.

For this to work well though, you should treat the cache directory as a permanent, not a transient store of local market data that can be added to over time. Best practice is to always specify a --cache-dir that points to your common market data trove wherever you use the tool from.

Concurrency and Dukascopy reliability

Each symbol export uses up to two downloader threads internally. --workers controls how many symbols are exported concurrently, so the total request concurrency can grow quickly.

Dukascopy becomes unreliable when too many requests are in flight. If you want to stay near the safest limit of two concurrent download threads, keep --workers 1. Re-running the same command is idempotent and is the intended way to fill cache gaps caused by failed hours.

Resampled CSV using --out

If you resample to an --out location, the tool writes separate bid and ask OHLCV CSV files with UTC timestamps that include an explicit +00:00 offset:

timestamp,open,high,low,close,volume
2025-01-01 00:00:00+00:00,1.10342,1.10361,1.10311,1.10355,1234.0

• Timestamps are always UTC
• Prices are floats after applying the price divisor
• Volume is derived from tick volume

Metadata sidecar (.meta.json)

Every output CSV is accompanied by a metadata file describing how it was generated:

{
  "data_type": "candles",
  "generated_at": "2026-03-06T16:58:50.397630Z",
  "params": {
    "date_from": "2026-01-05",
    "date_to": "2026-01-06",
    "price_side": "bid",
    "resample": "15MIN"
  },
  "price_divisor": 10.0,
  "schema_version": "1",
  "source": "dukascopy",
  "symbol": "GBPUSD",
  "timestamp_format": "iso8601_utc"
}

This ensures datasets are self-describing and reproducible, even months later.

--resample requires --out. If you run the tool without --resample, it will populate the --cache-dir with the cached source data and daily candles but it will not emit the final range-level output CSVs in --out.

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Requirements

• Python 3.11+
• Internet access to Dukascopy datafeed

Credentials and Release Automation

Normal exporter usage, local development, and CI do not require repository secrets or broker credentials.

Maintainers running .github/workflows/prepare-release.yml need these repository secrets configured:

• RELEASE_APP_ID
• RELEASE_APP_PRIVATE_KEY

The release workflow uses those secrets to mint a GitHub App token for checkout, version bumping, pushing the release commit, and creating the GitHub release. .github/workflows/publish.yml uses PyPI trusted publishing via GitHub OIDC (id-token: write), so no PyPI API token secret is expected in this repository.

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License

Licensed under the Apache License, Version 2.0. See: https://www.apache.org/licenses/LICENSE-2.0

Copyright 2026 Radius Red Ltd.

Contributing

See CONTRIBUTING.md for guidelines on contributing to tradedesk-dukascopy.