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from __future__ import annotations
from collections import deque
from dataclasses import dataclass, field
import math
class OrderBook:
def __init__(self):
self.bids: dict[float,float] = {}
self.asks: dict[float,float] = {}
self.valid = False
self.last_ts = None
def apply(self, kind: str, bids, asks, ts: int):
if kind == 'ob_snapshot':
self.bids.clear(); self.asks.clear(); self.valid = True
elif not self.valid:
return 0
changed = 0
for side, updates in ((self.bids, bids or []), (self.asks, asks or [])):
for p, q in updates:
p=float(p); q=float(q); changed += 1
if q == 0: side.pop(p, None)
else: side[p]=q
self.last_ts = ts
return changed
def top(self, n=10):
bids = sorted(self.bids.items(), key=lambda x:x[0], reverse=True)[:n]
asks = sorted(self.asks.items(), key=lambda x:x[0])[:n]
return bids, asks
def metrics(self, n=10):
if not self.valid or not self.bids or not self.asks:
return {'book_valid':0,'mid':math.nan,'spread_bp':math.nan,'bid_depth':math.nan,'ask_depth':math.nan,'imbalance':math.nan}
bids, asks = self.top(n)
bb, ba = bids[0][0], asks[0][0]
mid=(bb+ba)/2
spread_bp=(ba-bb)/mid*10000 if mid else math.nan
bd=sum(q for _,q in bids); ad=sum(q for _,q in asks)
imb=(bd-ad)/(bd+ad) if (bd+ad)>0 else 0.0
return {'book_valid':1,'mid':mid,'spread_bp':spread_bp,'bid_depth':bd,'ask_depth':ad,'imbalance':imb}
class MarketState:
def __init__(self, primary_category='spot', primary_symbol='BTCUSDC', topn=10):
self.primary=(primary_category,primary_symbol)
self.topn=topn
self.books={}
self.trades=deque() # (ts, price, quote, key)
self.churn=deque() # (ts, changed)
self.last_primary_price=math.nan
self.prev_imbalance=0.0
def book(self,key):
if key not in self.books: self.books[key]=OrderBook()
return self.books[key]
def apply(self,r:dict):
ts=int(r.get('exch_ts_ms') or r.get('recv_wall_ms') or 0)
key=(r.get('category'),r.get('symbol'))
k=r.get('kind')
if k in ('ob_snapshot','ob_delta'):
c=self.book(key).apply(k,r.get('bids'),r.get('asks'),ts)
if c: self.churn.append((ts,c))
elif k=='trade':
try:
p=float(r.get('price')); q=float(r.get('quote_value') or float(r.get('price'))*float(r.get('size')))
except Exception: return
self.trades.append((ts,p,q,key))
if key==self.primary: self.last_primary_price=p
def _purge(self, now, ms):
cutoff=now-ms
while self.trades and self.trades[0][0] < cutoff-5000: self.trades.popleft()
while self.churn and self.churn[0][0] < cutoff-5000: self.churn.popleft()
def sensors(self, now:int, window_ms:int):
self._purge(now,window_ms)
lo=now-window_ms
primary=[x for x in self.trades if lo < x[0] <= now and x[3]==self.primary]
alltr=[x for x in self.trades if lo < x[0] <= now]
prices=[x[1] for x in primary]
if prices:
hi=max(prices); low=min(prices); ref=prices[0] or 1.0
range_bp=(hi-low)/ref*10000
travel=0.0
for a,b in zip(prices,prices[1:]): travel += abs(b-a)
travel_bp=travel/ref*10000
price_last=prices[-1]
else:
range_bp=travel_bp=0.0; price_last=self.last_primary_price
sec=max(window_ms/1000.0,1e-9)
tr_rate=len(alltr)/sec
quote=sum(x[2] for x in alltr)/sec
churn=sum(c for ts,c in self.churn if lo < ts <= now)/sec
bm=self.book(self.primary).metrics(self.topn)
imb=bm['imbalance']
if math.isnan(imb): imb_delta=0.0
else: imb_delta=abs(imb-self.prev_imbalance)
return {
'price_last':price_last,
'price_range_bp':range_bp,
'tick_travel_bp':travel_bp,
'trade_rate_s':tr_rate,
'quote_volume_s':quote,
'book_churn_s':churn,
'book_valid':bm['book_valid'],
'mid':bm['mid'],'spread_bp':bm['spread_bp'],'bid_depth':bm['bid_depth'],'ask_depth':bm['ask_depth'],
'imbalance':imb if not math.isnan(imb) else 0.0,
'imbalance_delta':imb_delta,
}
def commit_tick(self):
bm=self.book(self.primary).metrics(self.topn)
if not math.isnan(bm['imbalance']): self.prev_imbalance=bm['imbalance']