#!/usr/bin/env python3
# coding: utf-8
"""Historical OHLC backfill engine for the dccd daemon.
.. currentmodule:: dccd.daemon.backfill
Two strategies handle the fundamental difference between exchanges:
- :class:`OHLCBackfill` — exchanges whose REST endpoint accepts arbitrary
``start``/``end`` timestamps (Binance, Bybit, OKX, Coinbase). A rolling
window of ``max_candles × span`` seconds is used to stay within each
API's page-size limit.
- :class:`KrakenBackfill` — Kraken's OHLC endpoint only returns the last
~720 candles regardless of ``since``. This strategy falls back to the
public ``/Trades`` endpoint, which supports full pagination from any
timestamp, and resamples the raw trades into OHLC candles with pandas.
Both strategies share the same retry/progress/save loop defined in
:class:`_BackfillBase`, so all operational logic (resume from last saved
point, exponential backoff on transient errors, tqdm progress bar) lives
in one place.
Public entry points
-------------------
.. autosummary::
:toctree: generated/
make_job -- build the right strategy instance for an (exchange, pair)
run_backfill -- run all backfill jobs from a :class:`~dccd.daemon.config.CollectorConfig`
"""
from __future__ import annotations
import time as time_mod
from abc import ABC, abstractmethod
from datetime import datetime, timezone
from typing import TYPE_CHECKING
import polars as pl
from tqdm import tqdm
from dccd.tools.date_time import date_to_TS
if TYPE_CHECKING:
from dccd.daemon.config import CollectorConfig
from dccd.histo_dl.exchange import ImportDataCryptoCurrencies
__all__ = ['OHLCBackfill', 'KrakenBackfill', 'make_job', 'run_backfill']
# ---------------------------------------------------------------------------
# Per-exchange API constraints (not user-configurable — API-level limits)
# ---------------------------------------------------------------------------
_EXCHANGE_DEFAULTS: dict[str, dict] = {
'binance': {'max_candles': 990, 'sleep': 0.12},
'bybit': {'max_candles': 990, 'sleep': 0.15},
'kraken': {'sleep': 1.0},
'okx': {'max_candles': 300, 'sleep': 0.20},
'coinbase': {'max_candles': 300, 'sleep': 0.25},
}
# Kraken trades window: 6 h balances request count vs. memory footprint.
_KRAKEN_TRADE_WINDOW: int = 6 * 3600
_MAX_RETRIES: int = 3
DEFAULT_START: str = '2020-01-01 00:00:00'
_KRAKEN_EXCHANGE = 'kraken'
# ---------------------------------------------------------------------------
# Base class
# ---------------------------------------------------------------------------
class _BackfillBase(ABC):
"""Common retry / progress / save loop for all backfill strategies.
Subclasses implement :meth:`_fetch_window` (fetch one window and
populate ``self.obj.df``) and may override :meth:`window_size`,
:meth:`_advance`, and :meth:`_dry_run_summary`.
Parameters
----------
obj : ImportDataCryptoCurrencies
Instantiated exchange object.
sleep : float
Minimum seconds to wait between API calls.
form : str
Output format (accepted for backward compatibility; storage is
always Parquet via :class:`~dccd.storage.DataStore`).
max_retries : int, optional
Maximum fetch attempts per window before skipping it. Default 3.
retry_delay : float, optional
Base delay in seconds between retries. Actual delay is
``retry_delay * 2 ** (attempt - 1)`` (exponential back-off).
Default 2.0.
"""
def __init__(
self,
obj: ImportDataCryptoCurrencies,
sleep: float,
form: str,
max_retries: int = 3,
retry_delay: float = 2.0,
) -> None:
self.obj = obj
self.sleep = sleep
self.form = form
self.max_retries = max_retries
self.retry_delay = retry_delay
cls_name = type(obj).__name__[4:] # strip leading 'From'
self.label = f'{cls_name:8s} {obj.crypto}/{obj.fiat}'
# ------------------------------------------------------------------
# Abstract interface
@property
@abstractmethod
def window_size(self) -> int:
"""Seconds covered by a single API request window."""
@abstractmethod
def _fetch_window(self, current: int, end: int) -> int:
"""Fetch one window and populate ``self.obj.df``.
Parameters
----------
current : int
Window start (Unix timestamp, inclusive).
end : int
Window end (Unix timestamp, exclusive).
Returns
-------
int
Number of candles fetched (0 means nothing to save).
"""
# ------------------------------------------------------------------
# Optional overrides
def _advance(self, current: int, end: int) -> int:
"""Return the start timestamp for the next window."""
return end
def _dry_run_summary(self, n_windows: int) -> str:
"""One-line dry-run description shown instead of fetching."""
sleep_total = n_windows * (self.sleep + 0.5) / 60
return (
f'DRY RUN — {n_windows} windows, '
f'~{sleep_total:.0f} min at {self.sleep}s/req'
)
# ------------------------------------------------------------------
# Main loop
def run(
self,
start_str: str,
dry_run: bool = False,
position: int = 0,
) -> None:
"""Run the full backfill for this (exchange, pair).
Parameters
----------
start_str : str
Earliest date to backfill (``'YYYY-MM-DD HH:MM:SS'``).
Interpreted in the timezone stored on the exchange object.
dry_run : bool, optional
Print an estimate without making any API calls.
position : int, optional
tqdm bar position (use distinct values for parallel runs).
"""
now_ts = int(time_mod.time())
user_start = int(date_to_TS(start_str, tz=self.obj.tz))
if dry_run:
total = max(1, (now_ts - user_start) // self.window_size + 1)
tqdm.write(f'[{self.label}] {self._dry_run_summary(total)}')
return
intervals = self.obj._store.missing_intervals(user_start, now_ts)
if not intervals:
tqdm.write(f'[{self.label}] already up to date')
return
def _iso(ts: int) -> str:
return datetime.fromtimestamp(ts, tz=timezone.utc).strftime('%Y-%m-%d')
total = sum(max(1, (e - s) // self.window_size + 1) for s, e in intervals)
end_date_str = _iso(intervals[-1][1])
bar = tqdm(
total=total,
desc=f'{self.label} {_iso(intervals[0][0])} → {end_date_str}',
unit='',
position=position, leave=True, dynamic_ncols=True,
)
n_candles = 0
skipped = 0
for ivl_start, ivl_end in intervals:
current = ivl_start
while current < ivl_end:
end = min(current + self.window_size, ivl_end)
last_exc: Exception | None = None
n = 0
for attempt in range(1, self.max_retries + 1):
try:
n = self._fetch_window(current, end)
break
except Exception as exc:
last_exc = exc
if attempt < self.max_retries:
delay = self.retry_delay * (2 ** (attempt - 1))
tqdm.write(
f'[{self.label}] attempt {attempt}/{self.max_retries} '
f'failed: {exc} — retrying in {delay:.1f}s'
)
time_mod.sleep(delay)
else:
tqdm.write(
f'[{self.label}] window {current} failed after '
f'{self.max_retries} attempts: {last_exc} — skipping'
)
skipped += 1
current += self.window_size
bar.update(1)
time_mod.sleep(self.sleep)
continue
if n > 0:
self.obj.save()
n_candles += n
current_date = _iso(current)
current = self._advance(current, end)
bar.update(1)
bar.set_description(f'{self.label} {current_date} → {end_date_str}')
bar.set_postfix(candles=n_candles, skipped=skipped)
time_mod.sleep(self.sleep)
bar.close()
tqdm.write(
f'[{self.label}] done — {n_candles:,} candles'
+ (f', {skipped} windows skipped' if skipped else '')
)
# ---------------------------------------------------------------------------
# OHLC endpoint strategy (Binance, Bybit, OKX, Coinbase)
# ---------------------------------------------------------------------------
[docs]
class OHLCBackfill(_BackfillBase):
"""Backfill strategy for exchanges with a paginated OHLC endpoint.
Parameters
----------
obj : ImportDataCryptoCurrencies
Instantiated exchange object.
max_candles : int
Maximum candles per API request (exchange-specific).
sleep : float
Seconds to wait between requests.
form : str
Output format (accepted for backward compatibility).
max_retries : int, optional
See :class:`_BackfillBase`. Default 3.
retry_delay : float, optional
See :class:`_BackfillBase`. Default 2.0.
"""
def __init__(
self,
obj: ImportDataCryptoCurrencies,
max_candles: int,
sleep: float,
form: str,
max_retries: int = 3,
retry_delay: float = 2.0,
) -> None:
super().__init__(obj, sleep, form, max_retries=max_retries, retry_delay=retry_delay)
self.max_candles = max_candles
@property
def window_size(self) -> int:
return self.max_candles * self.obj.span
def _fetch_window(self, current: int, end: int) -> int:
self.obj.import_data(start=current, end=end)
if self.obj.df is None or len(self.obj.df) == 0:
return 0
self._warn_if_suspicious(current, end)
return len(self.obj.df)
def _advance(self, current: int, end: int) -> int:
return self.obj.end + self.obj.span
def _warn_if_suspicious(self, current: int, now_ts: int) -> None:
df = self.obj.df
ts_min = df['TS'].min()
tolerance = self.obj.span * 5
if ts_min > current + tolerance:
tqdm.write(
f'[{self.label}] WARNING: data starts at {ts_min} but window '
f'began at {current} (gap={ts_min - current}s > {tolerance}s)'
)
if df['TS'].max() > now_ts + 60:
tqdm.write(f'[{self.label}] WARNING: future timestamps detected')
# ---------------------------------------------------------------------------
# Kraken trades strategy
# ---------------------------------------------------------------------------
[docs]
class KrakenBackfill(_BackfillBase):
"""Backfill strategy for Kraken using the ``/Trades`` endpoint.
Kraken's OHLC endpoint only returns the last ~720 candles regardless
of ``since``. This strategy paginates ``/Trades`` with nanosecond
cursors, then resamples the raw trades into OHLC candles.
Parameters
----------
obj : ImportDataCryptoCurrencies
Instantiated :class:`~dccd.histo_dl.kraken.FromKraken` object.
sleep : float
Seconds to wait between paginated API calls.
form : str
Output format (accepted for backward compatibility).
"""
@property
def window_size(self) -> int:
return _KRAKEN_TRADE_WINDOW
def _fetch_window(self, current: int, end: int) -> int:
trades = self._paginate_trades(current, end)
candles = self._trades_to_ohlc(trades, current, end)
if candles:
self.obj.start, self.obj.end = current, end
self.obj._sort_data(candles)
return len(candles)
def _advance(self, current: int, end: int) -> int:
return end
def _dry_run_summary(self, n_windows: int) -> str:
hours = _KRAKEN_TRADE_WINDOW // 3600
eta_min = n_windows * 15 * self.sleep / 60
return (
f'DRY RUN (trades→OHLC) — {n_windows} windows '
f'({hours}h each), ~{eta_min:.0f} min'
)
# ------------------------------------------------------------------
# Private helpers
def _paginate_trades(self, start_ts: int, end_ts: int) -> list[dict]:
"""Paginate Kraken ``/Trades`` and return raw trade dicts.
Uses nanosecond ``since`` cursors. Retries with exponential
backoff on ``EGeneral:Too many requests`` errors.
Parameters
----------
start_ts, end_ts : int
Window boundaries in Unix seconds.
Returns
-------
list of dict
Each dict has keys ``timestamp`` (float), ``price`` (float),
``amount`` (float).
"""
trades: list[dict] = []
since_ns = int(start_ts * 1e9)
while True:
body = self._call_with_backoff(since_ns)
result = body['result']
raw = result.get(self.obj.pair, [])
last_ns = int(result.get('last', since_ns))
for t in raw:
ts = float(t[2])
if ts > end_ts:
return trades
trades.append({
'timestamp': ts,
'price': float(t[0]),
'amount': float(t[1]),
})
if not raw or last_ns >= int(end_ts * 1e9):
break
since_ns = last_ns
time_mod.sleep(self.sleep)
return trades
def _call_with_backoff(self, since_ns: int) -> dict:
"""Call Kraken ``/Trades`` with exponential backoff on rate limits."""
backoff = self.sleep
for _attempt in range(6):
r = self.obj._fetch(
'https://api.kraken.com/0/public/Trades',
{'pair': self.obj.pair, 'since': since_ns, 'count': 1000},
)
body = r.json()
errors = body.get('error', [])
if errors and any('Too many requests' in e for e in errors):
tqdm.write(
f'[{self.label}] rate limited — backing off {backoff:.0f}s'
)
time_mod.sleep(backoff)
backoff = min(backoff * 2, 60)
continue
if errors:
raise RuntimeError(f'Kraken Trades API error: {errors}')
return body
raise RuntimeError(
f'Kraken rate limit not resolved after retries (since={since_ns})'
)
def _trades_to_ohlc(
self, trades: list[dict], start_ts: int, end_ts: int,
) -> list[dict]:
"""Resample raw trades into 1-min (or ``span``-s) OHLC candles.
Empty buckets (no trades) are dropped; :meth:`_sort_data` will
forward-fill them when building the uniform minute grid.
Parameters
----------
trades : list of dict
Output of :meth:`_paginate_trades`.
start_ts, end_ts : int
Window boundaries (inclusive start, exclusive end).
Returns
-------
list of dict
Each dict contains ``date``, ``open``, ``high``, ``low``,
``close``, ``volume``, ``quoteVolume``, ``weightedAverage``.
"""
if not trades:
return []
span = self.obj.span
df = (
pl.DataFrame(trades)
.with_columns(pl.from_epoch('timestamp', time_unit='s').alias('ts'))
.sort('ts')
)
result = (
df.group_by_dynamic('ts', every=f'{span}s', closed='left', start_by='window')
.agg(
pl.col('price').first().alias('open'),
pl.col('price').max().alias('high'),
pl.col('price').min().alias('low'),
pl.col('price').last().alias('close'),
pl.col('amount').sum().alias('volume'),
(pl.col('price') * pl.col('amount')).sum().alias('quoteVolume'),
)
.filter(
(pl.col('ts').dt.epoch(time_unit='s') >= start_ts)
& (pl.col('ts').dt.epoch(time_unit='s') < end_ts)
)
.with_columns(pl.col('ts').dt.epoch(time_unit='s').alias('ts_sec'))
)
return [{
'date': row['ts_sec'],
'open': float(row['open']),
'high': float(row['high']),
'low': float(row['low']),
'close': float(row['close']),
'volume': float(row['volume']),
'quoteVolume': float(row['quoteVolume']),
'weightedAverage': (
float(row['quoteVolume'] / row['volume'])
if row['volume'] > 0 else float(row['close'])
),
} for row in result.iter_rows(named=True)]
# ---------------------------------------------------------------------------
# Factory and entry point
# ---------------------------------------------------------------------------
[docs]
def make_job(
exchange: str,
crypto: str,
fiat: str,
span: int,
path: str,
tz: str,
form: str,
max_retries: int = 3,
retry_delay: float = 2.0,
) -> _BackfillBase:
"""Build the appropriate backfill strategy for an (exchange, pair).
Parameters
----------
exchange : str
Exchange name (e.g. ``'binance'``).
crypto, fiat : str
Asset symbols (e.g. ``'BTC'``, ``'USDT'``).
span : int
Candle interval in seconds.
path : str
Root data directory.
tz : str
Timezone for date parsing and file labelling.
form : str
Output format (accepted for backward compatibility).
Returns
-------
_BackfillBase
:class:`KrakenBackfill` for Kraken, :class:`OHLCBackfill` otherwise.
Raises
------
ValueError
If *exchange* is not in the supported defaults table.
"""
if exchange not in _EXCHANGE_DEFAULTS:
raise ValueError(
f"Unsupported exchange {exchange!r}. "
f"Supported: {sorted(_EXCHANGE_DEFAULTS)}"
)
defaults = _EXCHANGE_DEFAULTS[exchange]
sleep = defaults['sleep']
from dccd.daemon.scheduler import _HISTO_CLASSES
cls = _HISTO_CLASSES.get(exchange)
if cls is None:
raise ValueError(f"No exchange class registered for {exchange!r}")
obj = cls(path, crypto, span, fiat, form=form, tz=tz)
if exchange == _KRAKEN_EXCHANGE:
return KrakenBackfill(obj, sleep=sleep, form=form,
max_retries=max_retries, retry_delay=retry_delay)
return OHLCBackfill(
obj,
max_candles=defaults['max_candles'],
sleep=sleep,
form=form,
max_retries=max_retries,
retry_delay=retry_delay,
)
[docs]
def run_backfill(
cfg: CollectorConfig,
exchange: str | None = None,
pairs: list[str] | None = None,
start: str = DEFAULT_START,
parallel: bool = False,
dry_run: bool = False,
) -> None:
"""Run historical backfill for all matching jobs in *cfg*.
Parameters
----------
cfg : CollectorConfig
Loaded daemon configuration.
exchange : str or None, optional
Filter to a single exchange. ``None`` runs all.
pairs : list of str or None, optional
Filter to specific pairs in ``'CRYPTO/FIAT'`` format. ``None``
runs all pairs defined in the config.
start : str, optional
Earliest date to backfill. Default is ``'2020-01-01 00:00:00'``.
parallel : bool, optional
Run all jobs in parallel threads.
dry_run : bool, optional
Print estimates without making any API calls.
"""
from concurrent.futures import ThreadPoolExecutor, as_completed
path = cfg.settings.data_path
tz = cfg.settings.timezone
jobs: list[tuple[_BackfillBase, int]] = []
seen_paths: set[str] = set()
for histo_job in cfg.histo_jobs:
if exchange and histo_job.exchange != exchange:
continue
wanted_pairs = pairs or histo_job.pairs
for pair in histo_job.pairs:
if pair not in wanted_pairs:
continue
crypto, fiat = pair.split('/', 1)
job = make_job(
histo_job.exchange, crypto, fiat, histo_job.span,
path, tz, histo_job.format,
max_retries=histo_job.max_retries,
retry_delay=histo_job.retry_delay,
)
if job.obj.full_path in seen_paths:
tqdm.write(
f'[{histo_job.exchange} {pair}] skipped — resolves to the '
f'same data path as a previous job '
f'({job.obj.pair})'
)
continue
seen_paths.add(job.obj.full_path)
jobs.append((job, len(jobs)))
if not jobs:
tqdm.write('No matching jobs — check --exchange / --pairs filters.')
return
if parallel:
with ThreadPoolExecutor(max_workers=len(jobs)) as executor:
futures = {
executor.submit(job.run, start, dry_run, pos): job
for job, pos in jobs
}
for future in as_completed(futures):
job = futures[future]
try:
future.result()
except Exception as exc:
tqdm.write(f'[{job.label}] FATAL: {exc}')
else:
for job, _ in jobs:
job.run(start, dry_run=dry_run, position=0)
