TacitLab
003212de99
- Remove dead scoring code (_score_candidate, _action_for, etc.) and align action decisions directly with score_opportunity_signal metrics. - Reduce action surface from trigger/setup/chase/skip to entry/watch/avoid. - Add confidence field (0..100) mapped from edge_score. - Update evaluate/optimize ground-truth mapping and tests. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
437 lines
15 KiB
Python
437 lines
15 KiB
Python
"""Signal, opportunity, and portfolio service tests."""
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from __future__ import annotations
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import unittest
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from unittest.mock import patch
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from coinhunter.services import (
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opportunity_service,
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portfolio_service,
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research_service,
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signal_service,
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)
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class FakeSpotClient:
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def account_info(self):
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return {
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"balances": [
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{"asset": "USDT", "free": "50", "locked": "0"},
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{"asset": "BTC", "free": "0.01", "locked": "0"},
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{"asset": "ETH", "free": "0.5", "locked": "0"},
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{"asset": "DOGE", "free": "1", "locked": "0"},
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]
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}
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def ticker_price(self, symbols=None):
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mapping = {
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"BTCUSDT": {"symbol": "BTCUSDT", "price": "60000"},
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"ETHUSDT": {"symbol": "ETHUSDT", "price": "3000"},
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"DOGEUSDT": {"symbol": "DOGEUSDT", "price": "0.1"},
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}
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return [mapping[symbol] for symbol in symbols]
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def ticker_stats(self, symbols=None, *, window="1d"):
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rows = {
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"BTCUSDT": {
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"symbol": "BTCUSDT",
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"lastPrice": "60000",
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"priceChangePercent": "5",
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"quoteVolume": "9000000",
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"highPrice": "60200",
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"lowPrice": "55000",
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},
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"ETHUSDT": {
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"symbol": "ETHUSDT",
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"lastPrice": "3000",
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"priceChangePercent": "3",
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"quoteVolume": "8000000",
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"highPrice": "3100",
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"lowPrice": "2800",
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},
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"SOLUSDT": {
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"symbol": "SOLUSDT",
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"lastPrice": "150",
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"priceChangePercent": "8",
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"quoteVolume": "10000000",
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"highPrice": "152",
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"lowPrice": "130",
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},
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"DOGEUSDT": {
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"symbol": "DOGEUSDT",
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"lastPrice": "0.1",
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"priceChangePercent": "1",
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"quoteVolume": "100",
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"highPrice": "0.11",
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"lowPrice": "0.09",
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},
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}
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if not symbols:
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return list(rows.values())
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return [rows[symbol] for symbol in symbols]
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def exchange_info(self):
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return {
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"symbols": [
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{"symbol": "BTCUSDT", "status": "TRADING"},
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{"symbol": "ETHUSDT", "status": "TRADING"},
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{"symbol": "SOLUSDT", "status": "TRADING"},
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{"symbol": "DOGEUSDT", "status": "TRADING"},
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]
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}
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def klines(self, symbol, interval, limit):
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curves = {
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"BTCUSDT": [50000, 52000, 54000, 56000, 58000, 59000, 60000],
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"ETHUSDT": [2600, 2650, 2700, 2800, 2900, 2950, 3000],
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"SOLUSDT": [120, 125, 130, 135, 140, 145, 150],
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"DOGEUSDT": [0.11, 0.108, 0.105, 0.103, 0.102, 0.101, 0.1],
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}[symbol]
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rows = []
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for index, close in enumerate(curves[-limit:]):
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rows.append(
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[
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index,
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close * 0.98,
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close * 1.01,
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close * 0.97,
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close,
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100 + index * 10,
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index + 1,
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close * (100 + index * 10),
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]
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)
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return rows
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class DustOverlapSpotClient(FakeSpotClient):
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def account_info(self):
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return {"balances": [{"asset": "XRP", "free": "5", "locked": "0"}]}
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def ticker_price(self, symbols=None):
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mapping = {"XRPUSDT": {"symbol": "XRPUSDT", "price": "1.5"}}
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return [mapping[symbol] for symbol in symbols]
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def ticker_stats(self, symbols=None, *, window="1d"):
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rows = {
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"XRPUSDT": {
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"symbol": "XRPUSDT",
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"lastPrice": "1.5",
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"priceChangePercent": "10",
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"quoteVolume": "5000000",
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"highPrice": "1.52",
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"lowPrice": "1.2",
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}
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}
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if not symbols:
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return list(rows.values())
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return [rows[symbol] for symbol in symbols]
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def exchange_info(self):
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return {"symbols": [{"symbol": "XRPUSDT", "status": "TRADING"}]}
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def klines(self, symbol, interval, limit):
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rows = []
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setup_curve = [
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1.4151,
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1.4858,
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1.3868,
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1.5,
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1.4009,
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1.5142,
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1.4151,
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1.5,
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1.4292,
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1.4858,
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1.4434,
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1.4717,
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1.4505,
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1.4575,
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1.4547,
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1.4604,
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1.4575,
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1.4632,
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1.4599,
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1.466,
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1.4618,
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1.4698,
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1.4745,
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1.5,
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]
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for index, close in enumerate(setup_curve[-limit:]):
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rows.append([index, close * 0.98, close * 1.01, close * 0.97, close, 100 + index * 10, index + 1, close * 100])
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return rows
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class OpportunityPatternSpotClient:
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def account_info(self):
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return {"balances": [{"asset": "USDT", "free": "100", "locked": "0"}]}
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def ticker_price(self, symbols=None):
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return []
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def ticker_stats(self, symbols=None, *, window="1d"):
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rows = {
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"SETUPUSDT": {
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"symbol": "SETUPUSDT",
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"lastPrice": "106",
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"priceChangePercent": "4",
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"quoteVolume": "10000000",
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"highPrice": "107",
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"lowPrice": "98",
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},
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"CHASEUSDT": {
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"symbol": "CHASEUSDT",
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"lastPrice": "150",
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"priceChangePercent": "18",
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"quoteVolume": "9000000",
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"highPrice": "152",
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"lowPrice": "120",
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},
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}
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if not symbols:
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return list(rows.values())
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return [rows[symbol] for symbol in symbols]
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def exchange_info(self):
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return {
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"symbols": [
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{"symbol": "SETUPUSDT", "status": "TRADING"},
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{"symbol": "CHASEUSDT", "status": "TRADING"},
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]
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}
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def klines(self, symbol, interval, limit):
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curves = {
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"SETUPUSDT": [
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100,
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105,
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98,
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106,
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99,
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107,
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100,
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106,
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101,
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105,
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102,
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104,
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102.5,
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103,
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102.8,
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103.2,
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103.0,
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103.4,
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103.1,
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103.6,
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103.3,
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103.8,
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104.2,
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106,
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],
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"CHASEUSDT": [120, 125, 130, 135, 140, 145, 150],
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}[symbol]
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rows = []
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for index, close in enumerate(curves[-limit:]):
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rows.append([index, close * 0.98, close * 1.01, close * 0.97, close, 100 + index * 20, index + 1, close * 100])
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return rows
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class OpportunityServiceTestCase(unittest.TestCase):
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def setUp(self):
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self.config = {
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"market": {"default_quote": "USDT", "universe_allowlist": [], "universe_denylist": []},
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"trading": {"dust_usdt_threshold": 10.0},
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"signal": {
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"lookback_interval": "1h",
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"trend": 1.0,
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"momentum": 1.0,
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"breakout": 0.8,
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"volume": 0.7,
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"volatility_penalty": 0.5,
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},
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"opportunity": {
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"scan_limit": 10,
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"top_n": 5,
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"min_quote_volume": 1000.0,
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"entry_threshold": 1.5,
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"watch_threshold": 0.6,
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"overlap_penalty": 0.6,
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"auto_research": False,
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"research_provider": "coingecko",
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"research_timeout_seconds": 4.0,
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"risk_limits": {
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"min_liquidity": 0.0,
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"max_overextension": 0.08,
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"max_downside_risk": 0.3,
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"max_unlock_risk": 0.75,
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"max_regulatory_risk": 0.75,
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"min_quality_for_add": 0.0,
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},
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"weights": {
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"trend": 1.0,
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"momentum": 1.0,
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"breakout": 0.8,
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"pullback": 0.4,
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"volume": 0.7,
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"liquidity": 0.3,
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"trend_alignment": 0.8,
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"fundamental": 0.8,
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"tokenomics": 0.7,
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"catalyst": 0.5,
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"adoption": 0.4,
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"smart_money": 0.3,
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"volatility_penalty": 0.5,
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"overextension_penalty": 0.7,
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"downside_penalty": 0.5,
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"unlock_penalty": 0.8,
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"regulatory_penalty": 0.4,
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"position_concentration_penalty": 0.6,
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},
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},
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"portfolio": {
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"add_threshold": 1.5,
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"hold_threshold": 0.6,
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"trim_threshold": 0.2,
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"exit_threshold": -0.2,
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"max_position_weight": 0.6,
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},
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}
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def test_portfolio_analysis_ignores_dust_and_emits_recommendations(self):
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events = []
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with patch.object(portfolio_service, "audit_event", side_effect=lambda event, payload, **kwargs: events.append(event)):
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payload = portfolio_service.analyze_portfolio(self.config, spot_client=FakeSpotClient())
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symbols = [item["symbol"] for item in payload["recommendations"]]
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self.assertNotIn("DOGEUSDT", symbols)
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self.assertEqual(symbols, ["BTCUSDT", "ETHUSDT"])
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self.assertEqual(payload["recommendations"][0]["action"], "add")
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self.assertEqual(payload["recommendations"][1]["action"], "hold")
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self.assertEqual(events, ["opportunity_portfolio_generated"])
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def test_scan_is_deterministic(self):
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with patch.object(opportunity_service, "audit_event", return_value=None):
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payload = opportunity_service.scan_opportunities(
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self.config | {"opportunity": self.config["opportunity"] | {"top_n": 2}},
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spot_client=OpportunityPatternSpotClient(),
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)
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self.assertEqual([item["symbol"] for item in payload["recommendations"]], ["SETUPUSDT", "CHASEUSDT"])
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self.assertEqual([item["action"] for item in payload["recommendations"]], ["entry", "avoid"])
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self.assertGreater(payload["recommendations"][0]["metrics"]["setup_score"], 0.6)
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self.assertGreater(payload["recommendations"][1]["metrics"]["extension_penalty"], 1.0)
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def test_scan_respects_ignore_dust_for_overlap_penalty(self):
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client = DustOverlapSpotClient()
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base_config = self.config | {
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"opportunity": self.config["opportunity"] | {
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"top_n": 1,
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"ignore_dust": True,
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"overlap_penalty": 2.0,
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}
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}
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with patch.object(opportunity_service, "audit_event", return_value=None):
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ignored = opportunity_service.scan_opportunities(base_config, spot_client=client, symbols=["XRPUSDT"])
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included = opportunity_service.scan_opportunities(
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base_config | {"opportunity": base_config["opportunity"] | {"ignore_dust": False}},
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spot_client=client,
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symbols=["XRPUSDT"],
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)
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ignored_rec = ignored["recommendations"][0]
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included_rec = included["recommendations"][0]
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self.assertEqual(ignored_rec["action"], "entry")
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self.assertEqual(ignored_rec["metrics"]["position_weight"], 0.0)
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self.assertEqual(included_rec["action"], "entry")
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self.assertEqual(included_rec["metrics"]["position_weight"], 1.0)
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self.assertLess(included_rec["score"], ignored_rec["score"])
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def test_signal_score_handles_empty_klines(self):
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score, metrics = signal_service.score_market_signal([], [], {"price_change_pct": 1.0}, {})
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self.assertEqual(score, 0.0)
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self.assertEqual(metrics["trend"], 0.0)
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def test_scan_uses_automatic_external_research(self):
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config = self.config | {
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"opportunity": self.config["opportunity"]
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| {
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"auto_research": True,
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"top_n": 2,
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}
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}
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with (
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patch.object(opportunity_service, "audit_event", return_value=None),
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patch.object(
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opportunity_service,
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"get_external_research",
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return_value={
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"SOLUSDT": {
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"fundamental": 0.9,
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"tokenomics": 0.8,
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"catalyst": 0.9,
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"adoption": 0.8,
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"smart_money": 0.7,
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"unlock_risk": 0.1,
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"regulatory_risk": 0.1,
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"research_confidence": 0.9,
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}
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},
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) as research_mock,
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):
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payload = opportunity_service.scan_opportunities(config, spot_client=FakeSpotClient())
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research_mock.assert_called_once()
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sol = next(item for item in payload["recommendations"] if item["symbol"] == "SOLUSDT")
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self.assertEqual(sol["metrics"]["fundamental"], 0.9)
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self.assertEqual(sol["metrics"]["research_confidence"], 0.9)
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def test_weak_setup_and_trigger_becomes_avoid(self):
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metrics = {
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"extension_penalty": 0.0,
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"recent_runup": 0.0,
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"breakout_pct": -0.01,
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"setup_score": 0.12,
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"trigger_score": 0.18,
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"edge_score": 0.0,
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}
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action, reasons, confidence = opportunity_service._action_for_opportunity(
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2.5,
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metrics,
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{
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"entry_threshold": 1.5,
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"watch_threshold": 0.6,
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"min_trigger_score": 0.45,
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"min_setup_score": 0.35,
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},
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)
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self.assertEqual(action, "avoid")
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self.assertIn("setup, trigger, or overall quality is too weak", reasons[0])
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self.assertEqual(confidence, 50)
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class ResearchServiceTestCase(unittest.TestCase):
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def test_coingecko_market_data_becomes_research_signals(self):
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signals = research_service._coingecko_market_to_signals(
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{
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"id": "solana",
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"symbol": "sol",
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"market_cap": 80_000_000_000,
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"fully_diluted_valuation": 95_000_000_000,
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"total_volume": 5_000_000_000,
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"market_cap_rank": 6,
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"circulating_supply": 550_000_000,
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"total_supply": 600_000_000,
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"max_supply": None,
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"price_change_percentage_7d_in_currency": 12.0,
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"price_change_percentage_30d_in_currency": 35.0,
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"price_change_percentage_200d_in_currency": 80.0,
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},
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is_trending=True,
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)
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self.assertGreater(signals["fundamental"], 0.6)
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self.assertGreater(signals["tokenomics"], 0.8)
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self.assertGreater(signals["catalyst"], 0.6)
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self.assertLess(signals["unlock_risk"], 0.2)
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