534 lines
19 KiB
Python
534 lines
19 KiB
Python
from __future__ import annotations
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import asyncio
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from dataclasses import dataclass
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from typing import Any, Dict, List, Optional, Tuple
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from aiohttp import web
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import numpy as np
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import pandas as pd
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from statsmodels.tsa.stattools import adfuller, coint # type: ignore
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from statsmodels.tsa.vector_ar.vecm import coint_johansen # type: ignore
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from cvttpy_tools.app import App
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from cvttpy_tools.base import NamedObject
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from cvttpy_tools.config import Config, CvttAppConfig
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from cvttpy_tools.logger import Log
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from cvttpy_tools.timeutils import NanoPerSec, SecPerHour, current_nanoseconds
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from cvttpy_tools.web.rest_client import RESTSender
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from cvttpy_tools.web.rest_service import RestService
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from cvttpy_trading.trading.exchange_config import ExchangeAccounts
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from cvttpy_trading.trading.instrument import ExchangeInstrument
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from cvttpy_trading.trading.mkt_data.md_summary import MdTradesAggregate, MdSummary
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from pairs_trading.apps.pair_selector.renderer import HtmlRenderer
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@dataclass
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class InstrumentQuality(NamedObject):
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instrument_: ExchangeInstrument
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record_count_: int
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latest_tstamp_: Optional[pd.Timestamp]
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status_: str
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reason_: str
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@dataclass
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class PairStats(NamedObject):
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pair_name_: str
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instrument_a_: ExchangeInstrument
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instrument_b_: ExchangeInstrument
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pvalue_eg_: Optional[float]
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pvalue_adf_: Optional[float]
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pvalue_j_: Optional[float]
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trace_stat_j_: Optional[float]
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rank_eg_: int = 0
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rank_adf_: int = 0
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rank_j_: int = 0
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composite_rank_: int = 0
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def as_dict(self) -> Dict[str, Any]:
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return {
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"instrument_a": self.instrument_a_.instrument_id(),
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"instrument_b": self.instrument_b_.instrument_id(),
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"pvalue_eg": self.pvalue_eg_,
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"pvalue_adf": self.pvalue_adf_,
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"pvalue_j": self.pvalue_j_,
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"trace_stat_j": self.trace_stat_j_,
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"rank_eg": self.rank_eg_,
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"rank_adf": self.rank_adf_,
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"rank_j": self.rank_j_,
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"composite_rank": self.composite_rank_,
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}
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class DataFetcher(NamedObject):
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sender_: RESTSender
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interval_sec_: int
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history_depth_sec_: int
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def __init__(
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self,
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base_url: str,
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interval_sec: int,
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history_depth_sec: int,
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) -> None:
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self.sender_ = RESTSender(base_url=base_url)
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self.interval_sec_ = interval_sec
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self.history_depth_sec_ = history_depth_sec
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def fetch(
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self, exch_acct: str, inst: ExchangeInstrument
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) -> List[MdTradesAggregate]:
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rqst_data = {
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"exch_acct": exch_acct,
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"instrument_id": inst.instrument_id(),
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"interval_sec": self.interval_sec_,
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"history_depth_sec": self.history_depth_sec_,
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}
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response = self.sender_.send_post(endpoint="md_summary", post_body=rqst_data)
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if response.status_code not in (200, 201):
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Log.error(
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f"{self.fname()}: error {response.status_code} for {inst.details_short()}: {response.text}"
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)
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return []
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mdsums: List[MdSummary] = MdSummary.from_REST_response(response=response)
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return [
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mdsum.create_md_trades_aggregate(
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exch_acct=exch_acct, exch_inst=inst, interval_sec=self.interval_sec_
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)
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for mdsum in mdsums
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]
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class QualityChecker(NamedObject):
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interval_sec_: int
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def __init__(self, interval_sec: int) -> None:
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self.interval_sec_ = interval_sec
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def evaluate(
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self, inst: ExchangeInstrument, aggr: List[MdTradesAggregate]
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) -> InstrumentQuality:
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if len(aggr) == 0:
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return InstrumentQuality(
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instrument_=inst,
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record_count_=0,
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latest_tstamp_=None,
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status_="FAIL",
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reason_="no records",
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)
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aggr_sorted = sorted(aggr, key=lambda a: a.aggr_time_ns_)
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latest_ts = pd.to_datetime(aggr_sorted[-1].aggr_time_ns_, unit="ns", utc=True)
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now_ts = pd.Timestamp.utcnow()
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recency_cutoff = now_ts - pd.Timedelta(seconds=2 * self.interval_sec_)
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if latest_ts <= recency_cutoff:
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return InstrumentQuality(
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instrument_=inst,
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record_count_=len(aggr_sorted),
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latest_tstamp_=latest_ts,
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status_="FAIL",
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reason_=f"stale: latest {latest_ts} <= cutoff {recency_cutoff}",
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)
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gaps_ok, reason = self._check_gaps(aggr_sorted)
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status = "PASS" if gaps_ok else "FAIL"
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return InstrumentQuality(
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instrument_=inst,
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record_count_=len(aggr_sorted),
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latest_tstamp_=latest_ts,
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status_=status,
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reason_=reason,
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)
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def _check_gaps(self, aggr: List[MdTradesAggregate]) -> Tuple[bool, str]:
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NUM_TRADES_THRESHOLD = 50
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if len(aggr) < 2:
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return True, "ok"
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interval_ns = self.interval_sec_ * NanoPerSec
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for idx in range(1, len(aggr)):
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prev = aggr[idx - 1]
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curr = aggr[idx]
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delta = curr.aggr_time_ns_ - prev.aggr_time_ns_
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missing_intervals = int(delta // interval_ns) - 1
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if missing_intervals <= 0:
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continue
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prev_nt = prev.num_trades_
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next_nt = curr.num_trades_
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estimate = self._approximate_num_trades(prev_nt, next_nt)
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if estimate > NUM_TRADES_THRESHOLD:
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return False, (
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f"gap of {missing_intervals} interval(s), est num_trades={estimate} > {NUM_TRADES_THRESHOLD}"
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)
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return True, "ok"
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@staticmethod
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def _approximate_num_trades(prev_nt: int, next_nt: int) -> float:
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if prev_nt is None and next_nt is None:
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return 0.0
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if prev_nt is None:
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return float(next_nt)
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if next_nt is None:
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return float(prev_nt)
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return (prev_nt + next_nt) / 2.0
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class PairAnalyzer(NamedObject):
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price_field_: str
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interval_sec_: int
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def __init__(self, price_field: str, interval_sec: int) -> None:
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self.price_field_ = price_field
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self.interval_sec_ = interval_sec
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def analyze(
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self, series: Dict[ExchangeInstrument, pd.DataFrame]
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) -> Dict[str, PairStats]:
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instruments = list(series.keys())
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results: Dict[str, PairStats] = {}
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for i in range(len(instruments)):
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for j in range(i + 1, len(instruments)):
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inst_a, inst_b, pair_name = self._normalized_pair(
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instruments[i], instruments[j]
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)
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df_a = series[inst_a][["tstamp", "price"]].rename(
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columns={"price": "price_a"}
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)
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df_b = series[inst_b][["tstamp", "price"]].rename(
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columns={"price": "price_b"}
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)
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merged = pd.merge(df_a, df_b, on="tstamp", how="inner").sort_values(
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"tstamp"
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)
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stats = self._compute_stats(inst_a, inst_b, pair_name, merged)
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if stats:
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results[pair_name] = stats
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return self._rank(results)
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def _compute_stats(
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self,
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inst_a: ExchangeInstrument,
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inst_b: ExchangeInstrument,
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pair_name: str,
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merged: pd.DataFrame,
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) -> Optional[PairStats]:
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if len(merged) < 2:
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return None
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px_a = merged["price_a"].astype(float)
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px_b = merged["price_b"].astype(float)
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std_a = float(px_a.std())
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std_b = float(px_b.std())
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if std_a == 0 or std_b == 0:
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return None
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z_a = (px_a - float(px_a.mean())) / std_a
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z_b = (px_b - float(px_b.mean())) / std_b
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p_eg: Optional[float]
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p_adf: Optional[float]
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p_j: Optional[float]
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trace_stat: Optional[float]
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try:
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p_eg = float(coint(z_a, z_b)[1])
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except Exception as exc:
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Log.warning(
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f"{self.fname()}: EG failed for {inst_a.details_short()}/{inst_b.details_short()}: {exc}"
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)
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p_eg = None
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try:
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spread = z_a - z_b
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p_adf = float(adfuller(spread, maxlag=1, regression="c")[1])
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except Exception as exc:
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Log.warning(
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f"{self.fname()}: ADF failed for {inst_a.details_short()}/{inst_b.details_short()}: {exc}"
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)
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p_adf = None
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try:
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data = np.column_stack([z_a, z_b])
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res = coint_johansen(data, det_order=0, k_ar_diff=1)
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trace_stat = float(res.lr1[0])
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cv10, cv5, cv1 = res.cvt[0]
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if trace_stat > cv1:
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p_j = 0.01
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elif trace_stat > cv5:
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p_j = 0.05
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elif trace_stat > cv10:
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p_j = 0.10
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else:
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p_j = 1.0
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except Exception as exc:
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Log.warning(
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f"{self.fname()}: Johansen failed for {inst_a.details_short()}/{inst_b.details_short()}: {exc}"
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)
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p_j = None
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trace_stat = None
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return PairStats(
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pair_name_=pair_name,
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instrument_a_=inst_a,
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instrument_b_=inst_b,
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pvalue_eg_=p_eg,
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pvalue_adf_=p_adf,
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pvalue_j_=p_j,
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trace_stat_j_=trace_stat,
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)
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def _rank(self, results: Dict[str, PairStats]) -> Dict[str, PairStats]:
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ranked = list(results.values())
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self._assign_ranks(ranked, key=lambda r: r.pvalue_eg_, attr="rank_eg_")
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self._assign_ranks(ranked, key=lambda r: r.pvalue_adf_, attr="rank_adf_")
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self._assign_ranks(ranked, key=lambda r: r.pvalue_j_, attr="rank_j_")
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for res in ranked:
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res.composite_rank_ = res.rank_eg_ + res.rank_adf_ + res.rank_j_
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ranked.sort(key=lambda r: r.composite_rank_)
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return {res.pair_name_: res for res in ranked}
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@staticmethod
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def _normalized_pair(
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inst_a: ExchangeInstrument, inst_b: ExchangeInstrument
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) -> Tuple[ExchangeInstrument, ExchangeInstrument, str]:
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inst_a_id = PairAnalyzer._pair_label(inst_a.instrument_id())
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inst_b_id = PairAnalyzer._pair_label(inst_b.instrument_id())
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if inst_a_id <= inst_b_id:
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return inst_a, inst_b, f"{inst_a_id}<->{inst_b_id}"
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return inst_b, inst_a, f"{inst_b_id}<->{inst_a_id}"
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@staticmethod
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def _pair_label(instrument_id: str) -> str:
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if instrument_id.startswith("PAIR-"):
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return instrument_id[len("PAIR-") :]
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return instrument_id
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@staticmethod
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def _assign_ranks(results: List[PairStats], key, attr: str) -> None:
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values = [key(r) for r in results]
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sorted_vals = sorted([v for v in values if v is not None])
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for res in results:
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val = key(res)
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if val is None:
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setattr(res, attr, len(sorted_vals) + 1)
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continue
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rank = 1 + sum(1 for v in sorted_vals if v < val)
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setattr(res, attr, rank)
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class PairSelectionEngine(NamedObject):
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config_: object
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instruments_: List[ExchangeInstrument]
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price_field_: str
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fetcher_: DataFetcher
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quality_: QualityChecker
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analyzer_: PairAnalyzer
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interval_sec_: int
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history_depth_sec_: int
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data_quality_cache_: List[InstrumentQuality]
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pair_results_cache_: Dict[str, PairStats]
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def __init__(
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self,
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config: Config,
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instruments: List[ExchangeInstrument],
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price_field: str,
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) -> None:
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self.config_ = config
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self.instruments_ = instruments
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self.price_field_ = price_field
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interval_sec = int(config.get_value("interval_sec", 0))
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history_depth_sec = int(config.get_value("history_depth_hours", 0)) * SecPerHour
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base_url = config.get_value("cvtt_base_url", None)
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assert interval_sec > 0, "interval_sec must be > 0"
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assert history_depth_sec > 0, "history_depth_sec must be > 0"
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assert base_url, "cvtt_base_url must be set"
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self.fetcher_ = DataFetcher(
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base_url=base_url,
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interval_sec=interval_sec,
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history_depth_sec=history_depth_sec,
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)
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self.quality_ = QualityChecker(interval_sec=interval_sec)
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self.analyzer_ = PairAnalyzer(
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price_field=price_field, interval_sec=interval_sec
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)
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self.interval_sec_ = interval_sec
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self.history_depth_sec_ = history_depth_sec
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self.data_quality_cache_ = []
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self.pair_results_cache_ = {}
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async def run_once(self) -> None:
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quality_results: List[InstrumentQuality] = []
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price_series: Dict[ExchangeInstrument, pd.DataFrame] = {}
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for inst in self.instruments_:
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exch_acct = inst.user_data_.get("exch_acct") or inst.exchange_id_
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aggr = self.fetcher_.fetch(exch_acct=exch_acct, inst=inst)
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q = self.quality_.evaluate(inst, aggr)
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quality_results.append(q)
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if q.status_ != "PASS":
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continue
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df = self._to_dataframe(aggr, inst)
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if len(df) > 0:
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price_series[inst] = df
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self.data_quality_cache_ = quality_results
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self.pair_results_cache_ = self.analyzer_.analyze(price_series)
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def _to_dataframe(
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self, aggr: List[MdTradesAggregate], inst: ExchangeInstrument
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) -> pd.DataFrame:
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rows: List[Dict[str, Any]] = []
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for item in aggr:
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rows.append(
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{
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"tstamp": pd.to_datetime(item.aggr_time_ns_, unit="ns", utc=True),
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"price": self._extract_price(item, inst),
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"num_trades": item.num_trades_,
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}
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)
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df = pd.DataFrame(rows)
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return df.sort_values("tstamp").reset_index(drop=True)
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def _extract_price(
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self, aggr: MdTradesAggregate, inst: ExchangeInstrument
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) -> float:
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price_field = self.price_field_
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# MdTradesAggregate inherits hist bar with fields open_, high_, low_, close_, vwap_
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field_map = {
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"open": aggr.open_,
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"high": aggr.high_,
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"low": aggr.low_,
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"close": aggr.close_,
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"vwap": aggr.vwap_,
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}
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raw = field_map.get(price_field, aggr.close_)
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return inst.get_price(raw)
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def sleep_seconds_until_next_cycle(self) -> float:
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now_ns = current_nanoseconds()
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interval_ns = self.interval_sec_ * NanoPerSec
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next_boundary = (now_ns // interval_ns + 1) * interval_ns
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return max(0.0, (next_boundary - now_ns) / NanoPerSec)
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def quality_dicts(self) -> List[Dict[str, Any]]:
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res: List[Dict[str, Any]] = []
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for q in self.data_quality_cache_:
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res.append(
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{
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"instrument": q.instrument_.instrument_id(),
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"record_count": q.record_count_,
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"latest_tstamp": (
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q.latest_tstamp_.isoformat() if q.latest_tstamp_ else None
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),
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"status": q.status_,
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"reason": q.reason_,
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}
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)
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return res
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def pair_dicts(self) -> Dict[str, Dict[str, Any]]:
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return {
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pair_name: stats.as_dict()
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for pair_name, stats in self.pair_results_cache_.items()
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}
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class PairSelector(NamedObject):
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instruments_: List[ExchangeInstrument]
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engine_: PairSelectionEngine
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rest_service_: RestService
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def __init__(self) -> None:
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App.instance().add_cmdline_arg("--oneshot", action="store_true", default=False)
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App.instance().add_call(App.Stage.Config, self._on_config())
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App.instance().add_call(App.Stage.Run, self.run())
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async def _on_config(self) -> None:
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cfg = CvttAppConfig.instance()
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self.instruments_ = self._load_instruments(cfg)
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price_field = cfg.get_value("model/stat_model_price", "close")
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self.engine_ = PairSelectionEngine(
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config=cfg,
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instruments=self.instruments_,
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price_field=price_field,
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)
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self.rest_service_ = RestService(config_key="/api/REST")
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self.rest_service_.add_handler("GET", "/data_quality", self._on_data_quality)
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self.rest_service_.add_handler(
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"GET", "/pair_selection", self._on_pair_selection
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)
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def _load_instruments(self, cfg: CvttAppConfig) -> List[ExchangeInstrument]:
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instruments_cfg = cfg.get_value("instruments", [])
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instruments: List[ExchangeInstrument] = []
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assert len(instruments_cfg) >= 2, "at least two instruments required"
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for item in instruments_cfg:
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|
if isinstance(item, str):
|
|
parts = item.split(":", 1)
|
|
if len(parts) != 2:
|
|
raise ValueError(f"invalid instrument format: {item}")
|
|
exch_acct, instrument_id = parts
|
|
elif isinstance(item, dict):
|
|
exch_acct = item.get("exch_acct", "")
|
|
instrument_id = item.get("instrument_id", "")
|
|
if not exch_acct or not instrument_id:
|
|
raise ValueError(f"invalid instrument config: {item}")
|
|
else:
|
|
raise ValueError(f"unsupported instrument entry: {item}")
|
|
|
|
exch_inst = ExchangeAccounts.instance().get_exchange_instrument(
|
|
exch_acct=exch_acct, instrument_id=instrument_id
|
|
)
|
|
assert (
|
|
exch_inst is not None
|
|
), f"no ExchangeInstrument for {exch_acct}:{instrument_id}"
|
|
exch_inst.user_data_["exch_acct"] = exch_acct
|
|
instruments.append(exch_inst)
|
|
return instruments
|
|
|
|
async def run(self) -> None:
|
|
oneshot = App.instance().get_argument("oneshot", False)
|
|
while True:
|
|
await self.engine_.run_once()
|
|
if oneshot:
|
|
break
|
|
sleep_for = self.engine_.sleep_seconds_until_next_cycle()
|
|
await asyncio.sleep(sleep_for)
|
|
|
|
async def _on_data_quality(self, request: web.Request) -> web.Response:
|
|
fmt = request.query.get("format", "html").lower()
|
|
quality = self.engine_.quality_dicts()
|
|
if fmt == "json":
|
|
return web.json_response(quality)
|
|
return web.Response(
|
|
text=HtmlRenderer.render_data_quality(quality), content_type="text/html"
|
|
)
|
|
|
|
async def _on_pair_selection(self, request: web.Request) -> web.Response:
|
|
fmt = request.query.get("format", "html").lower()
|
|
pairs = self.engine_.pair_dicts()
|
|
if fmt == "json":
|
|
return web.json_response(pairs)
|
|
return web.Response(
|
|
text=HtmlRenderer.render_pairs(pairs), content_type="text/html"
|
|
)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
App()
|
|
CvttAppConfig()
|
|
PairSelector()
|
|
App.instance().run()
|