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from __future__ import annotations
from collections import defaultdict
from pathlib import Path
import numpy as np
import pandas as pd
from .archive_stream import iter_archive_rows, record_timestamp, ArchiveInfo
def _ob_snapshot_stats(row: dict) -> dict:
bids = row.get('bids') or []
asks = row.get('asks') or []
mid = float(row.get('mid') or 0.0)
best_bid = float(row.get('bestBid') or (bids[0][0] if bids else 0.0))
best_ask = float(row.get('bestAsk') or (asks[0][0] if asks else 0.0))
bid_qty = float(sum(float(x[1]) for x in bids if len(x)>=2))
ask_qty = float(sum(float(x[1]) for x in asks if len(x)>=2))
denom = bid_qty + ask_qty
imbalance = (bid_qty-ask_qty)/denom if denom>0 else np.nan
bq1 = float(bids[0][1]) if bids else 0.0
aq1 = float(asks[0][1]) if asks else 0.0
micro = np.nan
if best_bid and best_ask and bq1+aq1>0:
micro = (best_ask*bq1 + best_bid*aq1)/(bq1+aq1)
out = {
'spread_bps': float(row.get('spreadBps')) if row.get('spreadBps') is not None else ((best_ask-best_bid)/mid*10000 if mid else np.nan),
'bid_qty_l50': bid_qty, 'ask_qty_l50': ask_qty, 'imbalance_l50': imbalance,
'micro_dev_bps': ((micro/mid)-1)*10000 if mid>0 and np.isfinite(micro) else np.nan,
}
for band in (5,10,25,50):
if mid<=0:
out[f'bid_qty_{band}bps']=out[f'ask_qty_{band}bps']=out[f'imbalance_{band}bps']=np.nan
continue
lo,hi=mid*(1-band/10000),mid*(1+band/10000)
b=sum(float(q) for p,q in bids if float(p)>=lo)
a=sum(float(q) for p,q in asks if float(p)<=hi)
d=b+a
out[f'bid_qty_{band}bps']=b; out[f'ask_qty_{band}bps']=a; out[f'imbalance_{band}bps']=(b-a)/d if d>0 else np.nan
return out
def consume_row(agg: dict[int,dict], row: dict):
ch=row.get('_channel')
ts=record_timestamp(row)
if ts is None: return
minute=(int(ts)//60000)*60000
a=agg.setdefault(minute,{})
if ch in ('spot_trades','perp_trades'):
p='spot' if ch=='spot_trades' else 'perp'
side=str(row.get('side') or '').lower(); size=float(row.get('size') or 0); price=float(row.get('price') or 0)
a[f'{p}_trade_count']=a.get(f'{p}_trade_count',0)+1
a[f'{p}_volume']=a.get(f'{p}_volume',0.0)+size
a[f'{p}_turnover']=a.get(f'{p}_turnover',0.0)+size*price
if side=='buy': a[f'{p}_buy_volume']=a.get(f'{p}_buy_volume',0.0)+size
elif side=='sell': a[f'{p}_sell_volume']=a.get(f'{p}_sell_volume',0.0)+size
elif ch in ('spot_orderbook','perp_orderbook'):
p='spot_ob' if ch=='spot_orderbook' else 'perp_ob'; st=_ob_snapshot_stats(row)
n=a.get(f'{p}_samples',0)+1; a[f'{p}_samples']=n
for k,v in st.items():
if not np.isfinite(v): continue
a[f'{p}_{k}_sum']=a.get(f'{p}_{k}_sum',0.0)+float(v)
a[f'{p}_{k}_last']=float(v)
a[f'{p}_{k}_min']=min(a.get(f'{p}_{k}_min',float(v)),float(v))
a[f'{p}_{k}_max']=max(a.get(f'{p}_{k}_max',float(v)),float(v))
elif ch=='liquidations':
side=str(row.get('positionSide') or '').lower(); size=float(row.get('size') or 0)
a['liq_count']=a.get('liq_count',0)+1; a['liq_volume']=a.get('liq_volume',0.0)+size
if side=='buy': a['liq_buy_volume']=a.get('liq_buy_volume',0.0)+size
elif side=='sell': a['liq_sell_volume']=a.get('liq_sell_volume',0.0)+size
def finalize_minutes(agg: dict[int,dict], minutes: list[int] | None=None) -> pd.DataFrame:
keys=sorted(minutes if minutes is not None else agg.keys())
out=[]
for minute in keys:
if minute not in agg: continue
row={'minute_ms':minute,**agg[minute]}
for p in ('spot_ob','perp_ob'):
n=row.get(f'{p}_samples',0)
if n:
for k in list(row):
if k.startswith(p+'_') and k.endswith('_sum'):
row[k[:-4]+'_mean']=row[k]/n
out.append(row)
return pd.DataFrame(out).sort_values('minute_ms').reset_index(drop=True) if out else pd.DataFrame(columns=['minute_ms'])
def stream_archives_to_daily_micro(infos: list[ArchiveInfo], out_dir: Path, allow_dense: bool=False, progress_every: int=1_000_000) -> dict:
"""One sequential RAW pass. Keeps only a small minute dictionary and writes daily gzip partitions."""
out_dir.mkdir(parents=True,exist_ok=True)
agg: dict[int,dict]={}; rows=0; parts=[]; max_seen=None
current_day=None
def flush_days(before_day: int | None, force=False):
nonlocal agg
days=sorted(set(m//86_400_000 for m in agg))
flush=[d for d in days if force or (before_day is not None and d<before_day)]
for d in flush:
mins=[m for m in agg if m//86_400_000==d]
df=finalize_minutes(agg,mins)
day=pd.to_datetime(d*86_400_000,unit='ms',utc=True).strftime('%Y-%m-%d')
path=out_dir/f'{day}.csv.gz'
# If a day was touched by multiple flushes, merge compact minute partitions only.
if path.exists():
old=pd.read_csv(path)
df=pd.concat([old,df],ignore_index=True).groupby('minute_ms',as_index=False).last().sort_values('minute_ms')
df.to_csv(path,index=False,compression='gzip')
parts.append(str(path))
for m in mins: agg.pop(m,None)
for ai,info in enumerate(infos,1):
print(f" [RAW {ai}/{len(infos)}] {info.path.name}")
if info.format=='dense_truth_v1' and not allow_dense:
raise RuntimeError('Wskazano archiwum dense truth recordera. Do ML research uzyj data/raw/archives collectora; dense recorder sluzy do strojenia adaptive sampling.')
for row in iter_archive_rows(info.path,allow_dense=allow_dense):
rows+=1
ts=record_timestamp(row)
if ts is not None:
max_seen=ts if max_seen is None else max(max_seen,ts)
consume_row(agg,row)
if progress_every and rows%progress_every==0: print(f" {rows:,} raw records")
# Keep current UTC day AND the previous day in RAM. This tolerates small timestamp
# reordering around midnight without ever holding the whole corpus.
flush_days((info.end_ms//86_400_000)-1)
flush_days(None,force=True)
return {'raw_records':rows,'partitions':sorted(set(parts)),'minutes':sum(len(pd.read_csv(p,usecols=['minute_ms'])) for p in sorted(set(parts)))}