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feat: split portfolio and opportunity decision models

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Tacit Lab
2026-04-20 16:13:57 +08:00
parent 4312b16288
commit 1da08415f1
10 changed files with 326 additions and 195 deletions
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78
src/coinhunter/cli.py
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@@ -28,6 +28,7 @@ from .services import (
account_service,
market_service,
opportunity_service,
portfolio_service,
trade_service,
)
@@ -346,26 +347,26 @@ Fields:
TUI Output:
RECOMMENDATIONS count=3
1. BTCUSDT action=add score=0.7500
· trend, momentum, and breakout are aligned
trend=1.0 momentum=0.02 breakout=0.85 volume_confirmation=1.2 volatility=0.01 concentration=0.3
· market signal is strong and position still has room
trend=1.0 momentum=0.02 breakout=0.85 volume_confirmation=1.2 volatility=0.01 position_weight=0.3
2. ETHUSDT action=hold score=0.6000
· trend remains constructive
trend=1.0 momentum=0.01 breakout=0.5 volume_confirmation=1.0 volatility=0.02 concentration=0.2
· market structure remains supportive for holding
trend=1.0 momentum=0.01 breakout=0.5 volume_confirmation=1.0 volatility=0.02 position_weight=0.2
3. SOLUSDT action=trim score=-0.2000
· position concentration is high
trend=-1.0 momentum=-0.01 breakout=0.3 volume_confirmation=0.8 volatility=0.03 concentration=0.5
· position weight is above the portfolio risk budget
trend=-1.0 momentum=-0.01 breakout=0.3 volume_confirmation=0.8 volatility=0.03 position_weight=0.5
JSON Output:
{
"recommendations": [
{"symbol": "BTCUSDT", "action": "add", "score": 0.75, "reasons": ["trend, momentum, and breakout are aligned"],
"metrics": {"trend": 1.0, "momentum": 0.02, "breakout": 0.85, "volume_confirmation": 1.2, "volatility": 0.01, "concentration": 0.3}}
{"symbol": "BTCUSDT", "action": "add", "score": 0.75, "reasons": ["market signal is strong and position still has room"],
"metrics": {"trend": 1.0, "momentum": 0.02, "breakout": 0.85, "volume_confirmation": 1.2, "volatility": 0.01, "position_weight": 0.3}}
]
}
Fields:
symbol – trading pair (e.g. "BTCUSDT")
action – enum: "add" | "hold" | "trim" | "exit" | "observe"
score – composite score (float, can be negative)
action – enum: "add" | "hold" | "trim" | "exit" | "review"
score – shared market signal score (float, can be negative)
reasons – list of human-readable explanations
metrics – scoring breakdown
trend – -1.0 (down) | 0.0 (neutral) | 1.0 (up)
@@ -373,20 +374,20 @@ Fields:
breakout – proximity to recent high, 0-1 (float)
volume_confirmation – volume ratio vs average (float, >1 = above average)
volatility – price range relative to current (float)
concentration – position weight in portfolio (float, 0-1)
position_weight – position weight in portfolio (float, 0-1)
""",
"json": """\
JSON Output:
{
"recommendations": [
{"symbol": "BTCUSDT", "action": "add", "score": 0.75, "reasons": ["trend, momentum, and breakout are aligned"],
"metrics": {"trend": 1.0, "momentum": 0.02, "breakout": 0.85, "volume_confirmation": 1.2, "volatility": 0.01, "concentration": 0.3}}
{"symbol": "BTCUSDT", "action": "add", "score": 0.75, "reasons": ["market signal is strong and position still has room"],
"metrics": {"trend": 1.0, "momentum": 0.02, "breakout": 0.85, "volume_confirmation": 1.2, "volatility": 0.01, "position_weight": 0.3}}
]
}
Fields:
symbol – trading pair (e.g. "BTCUSDT")
action – enum: "add" | "hold" | "trim" | "exit" | "observe"
score – composite score (float, can be negative)
action – enum: "add" | "hold" | "trim" | "exit" | "review"
score – shared market signal score (float, can be negative)
reasons – list of human-readable explanations
metrics – scoring breakdown
trend – -1.0 (down) | 0.0 (neutral) | 1.0 (up)
@@ -394,52 +395,57 @@ Fields:
breakout – proximity to recent high, 0-1 (float)
volume_confirmation – volume ratio vs average (float, >1 = above average)
volatility – price range relative to current (float)
concentration – position weight in portfolio (float, 0-1)
position_weight – position weight in portfolio (float, 0-1)
""",
},
"opportunity": {
"tui": """\
TUI Output:
RECOMMENDATIONS count=5
1. ETHUSDT action=add score=0.8200
· trend, momentum, and breakout are aligned
1. ETHUSDT action=enter score=0.8200
· trend, momentum, and breakout are aligned for a fresh entry
· base asset ETH passed liquidity and tradability filters
trend=1.0 momentum=0.03 breakout=0.9 volume_confirmation=1.5 volatility=0.02 concentration=0.0
2. BTCUSDT action=hold score=0.6000
· trend remains constructive
trend=1.0 momentum=0.03 breakout=0.9 volume_confirmation=1.5 volatility=0.02 signal_score=0.82 position_weight=0.0
2. BTCUSDT action=watch score=0.6000
· market structure is constructive but still needs confirmation
· base asset BTC passed liquidity and tradability filters
trend=1.0 momentum=0.01 breakout=0.6 volume_confirmation=1.1 volatility=0.01 concentration=0.3
· symbol is already held, so the opportunity score is discounted for overlap
trend=1.0 momentum=0.01 breakout=0.6 volume_confirmation=1.1 volatility=0.01 signal_score=0.78 position_weight=0.3
JSON Output:
{
"recommendations": [
{"symbol": "ETHUSDT", "action": "add", "score": 0.82,
"reasons": ["trend, momentum, and breakout are aligned", "base asset ETH passed liquidity and tradability filters"],
"metrics": {"trend": 1.0, "momentum": 0.03, "breakout": 0.9, "volume_confirmation": 1.5, "volatility": 0.02, "concentration": 0.0}}
{"symbol": "ETHUSDT", "action": "enter", "score": 0.82,
"reasons": ["trend, momentum, and breakout are aligned for a fresh entry", "base asset ETH passed liquidity and tradability filters"],
"metrics": {"trend": 1.0, "momentum": 0.03, "breakout": 0.9, "volume_confirmation": 1.5, "volatility": 0.02, "signal_score": 0.82, "position_weight": 0.0}}
]
}
Fields:
symbol – trading pair (e.g. "ETHUSDT")
action – enum: "add" | "hold" | "trim" | "exit" | "observe"
score – composite score (float, can be negative)
action – enum: "enter" | "watch" | "skip"
score – opportunity score after overlap/risk discounts
reasons – list of human-readable explanations (includes liquidity filter note for scan)
metrics – scoring breakdown (same as portfolio)
metrics – scoring breakdown
signal_score – raw shared market signal score before overlap discount
position_weight – current portfolio overlap in the same symbol
""",
"json": """\
JSON Output:
{
"recommendations": [
{"symbol": "ETHUSDT", "action": "add", "score": 0.82,
"reasons": ["trend, momentum, and breakout are aligned", "base asset ETH passed liquidity and tradability filters"],
"metrics": {"trend": 1.0, "momentum": 0.03, "breakout": 0.9, "volume_confirmation": 1.5, "volatility": 0.02, "concentration": 0.0}}
{"symbol": "ETHUSDT", "action": "enter", "score": 0.82,
"reasons": ["trend, momentum, and breakout are aligned for a fresh entry", "base asset ETH passed liquidity and tradability filters"],
"metrics": {"trend": 1.0, "momentum": 0.03, "breakout": 0.9, "volume_confirmation": 1.5, "volatility": 0.02, "signal_score": 0.82, "position_weight": 0.0}}
]
}
Fields:
symbol – trading pair (e.g. "ETHUSDT")
action – enum: "add" | "hold" | "trim" | "exit" | "observe"
score – composite score (float, can be negative)
action – enum: "enter" | "watch" | "skip"
score – opportunity score after overlap/risk discounts
reasons – list of human-readable explanations (includes liquidity filter note for scan)
metrics – scoring breakdown (same as portfolio)
metrics – scoring breakdown
signal_score – raw shared market signal score before overlap discount
position_weight – current portfolio overlap in the same symbol
""",
},
"upgrade": {
@@ -771,7 +777,7 @@ def build_parser() -> argparse.ArgumentParser:
portfolio_parser = subparsers.add_parser(
"portfolio", aliases=["pf", "p"], help="Score current holdings",
description="Score your current spot holdings and generate add/hold/trim/exit recommendations.",
description="Review current spot holdings and generate add/hold/trim/exit recommendations.",
)
_add_global_flags(portfolio_parser)
@@ -1051,7 +1057,7 @@ def main(argv: list[str] | None = None) -> int:
if args.command == "portfolio":
spot_client = _load_spot_client(config)
with with_spinner("Analyzing portfolio...", enabled=not args.agent):
result = opportunity_service.analyze_portfolio(config, spot_client=spot_client)
result = portfolio_service.analyze_portfolio(config, spot_client=spot_client)
print_output(result, agent=args.agent)
return 0

27
src/coinhunter/config.py
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@@ -38,20 +38,29 @@ spot_enabled = true
dry_run_default = false
dust_usdt_threshold = 10.0
[opportunity]
min_quote_volume = 1000000.0
top_n = 10
scan_limit = 50
ignore_dust = true
lookback_intervals = ["1h", "4h", "1d"]
[opportunity.weights]
[signal]
lookback_interval = "1h"
trend = 1.0
momentum = 1.0
breakout = 0.8
volume = 0.7
volatility_penalty = 0.5
position_concentration_penalty = 0.6
[opportunity]
min_quote_volume = 1000000.0
top_n = 10
scan_limit = 50
ignore_dust = true
entry_threshold = 1.5
watch_threshold = 0.6
overlap_penalty = 0.6
[portfolio]
add_threshold = 1.5
hold_threshold = 0.6
trim_threshold = 0.2
exit_threshold = -0.2
max_position_weight = 0.6
"""
DEFAULT_ENV = "BINANCE_API_KEY=\nBINANCE_API_SECRET=\n"

8
src/coinhunter/runtime.py
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@@ -334,7 +334,13 @@ def _render_tui(payload: Any) -> None:
score = r.get("score", 0)
action = r.get("action", "")
action_color = (
_GREEN if action == "add" else _YELLOW if action == "hold" else _RED if action == "exit" else _CYAN
_GREEN
if action in {"add", "enter"}
else _YELLOW
if action in {"hold", "watch", "review"}
else _RED
if action in {"exit", "skip", "trim"}
else _CYAN
)
print(
f" {i}. {_BOLD}{r.get('symbol', '')}{_RESET} action={_color(action, action_color)} score={score:.4f}"

156
src/coinhunter/services/opportunity_service.py
View File

@@ -1,14 +1,14 @@
"""Opportunity analysis services."""
"""Opportunity scanning services."""
from __future__ import annotations
from dataclasses import asdict, dataclass
from statistics import mean
from typing import Any
from ..audit import audit_event
from .account_service import get_positions
from .market_service import base_asset, get_scan_universe, normalize_symbol
from .signal_service import get_signal_interval, get_signal_weights, score_market_signal
@dataclass
@@ -20,132 +20,25 @@ class OpportunityRecommendation:
metrics: dict[str, float]
def _safe_pct(new: float, old: float) -> float:
if old == 0:
return 0.0
return (new - old) / old
def _score_candidate(
closes: list[float], volumes: list[float], ticker: dict[str, Any], weights: dict[str, float], concentration: float
) -> tuple[float, dict[str, float]]:
if len(closes) < 2 or not volumes:
return 0.0, {
"trend": 0.0,
"momentum": 0.0,
"breakout": 0.0,
"volume_confirmation": 1.0,
"volatility": 0.0,
"concentration": round(concentration, 4),
}
current = closes[-1]
sma_short = mean(closes[-5:]) if len(closes) >= 5 else current
sma_long = mean(closes[-20:]) if len(closes) >= 20 else mean(closes)
trend = 1.0 if current >= sma_short >= sma_long else -1.0 if current < sma_short < sma_long else 0.0
momentum = (
_safe_pct(closes[-1], closes[-2]) * 0.5
+ (_safe_pct(closes[-1], closes[-5]) * 0.3 if len(closes) >= 5 else 0.0)
+ float(ticker.get("price_change_pct", 0.0)) / 100.0 * 0.2
)
recent_high = max(closes[-20:]) if len(closes) >= 20 else max(closes)
breakout = 1.0 - max((recent_high - current) / recent_high, 0.0)
avg_volume = mean(volumes[:-1]) if len(volumes) > 1 else volumes[-1]
volume_confirmation = volumes[-1] / avg_volume if avg_volume else 1.0
volume_score = min(max(volume_confirmation - 1.0, -1.0), 2.0)
volatility = (max(closes[-10:]) - min(closes[-10:])) / current if len(closes) >= 10 and current else 0.0
score = (
weights.get("trend", 1.0) * trend
+ weights.get("momentum", 1.0) * momentum
+ weights.get("breakout", 0.8) * breakout
+ weights.get("volume", 0.7) * volume_score
- weights.get("volatility_penalty", 0.5) * volatility
- weights.get("position_concentration_penalty", 0.6) * concentration
)
metrics = {
"trend": round(trend, 4),
"momentum": round(momentum, 4),
"breakout": round(breakout, 4),
"volume_confirmation": round(volume_confirmation, 4),
"volatility": round(volatility, 4),
"concentration": round(concentration, 4),
def _opportunity_thresholds(config: dict[str, Any]) -> dict[str, float]:
opportunity_config = config.get("opportunity", {})
return {
"entry_threshold": float(opportunity_config.get("entry_threshold", 1.5)),
"watch_threshold": float(opportunity_config.get("watch_threshold", 0.6)),
"overlap_penalty": float(opportunity_config.get("overlap_penalty", 0.6)),
}
return score, metrics
def _action_for(score: float, concentration: float) -> tuple[str, list[str]]:
def _action_for_opportunity(score: float, thresholds: dict[str, float]) -> tuple[str, list[str]]:
reasons: list[str] = []
if concentration >= 0.5 and score < 0.4:
reasons.append("position concentration is high")
return "trim", reasons
if score >= 1.5:
reasons.append("trend, momentum, and breakout are aligned")
return "add", reasons
if score >= 0.6:
reasons.append("trend remains constructive")
return "hold", reasons
if score <= -0.2:
reasons.append("momentum and structure have weakened")
return "exit", reasons
reasons.append("signal is mixed and needs confirmation")
return "observe", reasons
def analyze_portfolio(config: dict[str, Any], *, spot_client: Any) -> dict[str, Any]:
quote = str(config.get("market", {}).get("default_quote", "USDT")).upper()
weights = config.get("opportunity", {}).get("weights", {})
positions = get_positions(config, spot_client=spot_client)["positions"]
positions = [item for item in positions if item["symbol"] != quote]
total_notional = sum(item["notional_usdt"] for item in positions) or 1.0
recommendations = []
for position in positions:
symbol = normalize_symbol(position["symbol"])
klines = spot_client.klines(symbol=symbol, interval="1h", limit=24)
closes = [float(item[4]) for item in klines]
volumes = [float(item[5]) for item in klines]
tickers = spot_client.ticker_stats([symbol], window="1d")
ticker = tickers[0] if tickers else {"priceChangePercent": "0"}
concentration = position["notional_usdt"] / total_notional
score, metrics = _score_candidate(
closes,
volumes,
{
"price_change_pct": float(ticker.get("priceChangePercent") or 0.0),
},
weights,
concentration,
)
action, reasons = _action_for(score, concentration)
recommendations.append(
asdict(
OpportunityRecommendation(
symbol=symbol,
action=action,
score=round(score, 4),
reasons=reasons,
metrics=metrics,
)
)
)
payload = {"recommendations": sorted(recommendations, key=lambda item: item["score"], reverse=True)}
audit_event(
"opportunity_portfolio_generated",
{
"market_type": "spot",
"symbol": None,
"side": None,
"qty": None,
"quote_amount": None,
"order_type": None,
"dry_run": True,
"request_payload": {"mode": "portfolio"},
"response_payload": payload,
"status": "generated",
"error": None,
},
)
return payload
if score >= thresholds["entry_threshold"]:
reasons.append("trend, momentum, and breakout are aligned for a fresh entry")
return "enter", reasons
if score >= thresholds["watch_threshold"]:
reasons.append("market structure is constructive but still needs confirmation")
return "watch", reasons
reasons.append("edge is too weak for a new entry")
return "skip", reasons
def scan_opportunities(
@@ -155,7 +48,9 @@ def scan_opportunities(
symbols: list[str] | None = None,
) -> dict[str, Any]:
opportunity_config = config.get("opportunity", {})
weights = opportunity_config.get("weights", {})
signal_weights = get_signal_weights(config)
interval = get_signal_interval(config)
thresholds = _opportunity_thresholds(config)
scan_limit = int(opportunity_config.get("scan_limit", 50))
top_n = int(opportunity_config.get("top_n", 10))
quote = str(config.get("market", {}).get("default_quote", "USDT")).upper()
@@ -167,14 +62,19 @@ def scan_opportunities(
recommendations = []
for ticker in universe:
symbol = normalize_symbol(ticker["symbol"])
klines = spot_client.klines(symbol=symbol, interval="1h", limit=24)
klines = spot_client.klines(symbol=symbol, interval=interval, limit=24)
closes = [float(item[4]) for item in klines]
volumes = [float(item[5]) for item in klines]
concentration = concentration_map.get(symbol, 0.0) / total_held
score, metrics = _score_candidate(closes, volumes, ticker, weights, concentration)
action, reasons = _action_for(score, concentration)
signal_score, metrics = score_market_signal(closes, volumes, ticker, signal_weights)
score = signal_score - thresholds["overlap_penalty"] * concentration
action, reasons = _action_for_opportunity(score, thresholds)
metrics["signal_score"] = round(signal_score, 4)
metrics["position_weight"] = round(concentration, 4)
if symbol.endswith(quote):
reasons.append(f"base asset {base_asset(symbol, quote)} passed liquidity and tradability filters")
if concentration > 0:
reasons.append("symbol is already held, so the opportunity score is discounted for overlap")
recommendations.append(
asdict(
OpportunityRecommendation(

109
src/coinhunter/services/portfolio_service.py Normal file
View File

@@ -0,0 +1,109 @@
"""Portfolio analysis and position management signals."""
from __future__ import annotations
from dataclasses import asdict, dataclass
from typing import Any
from ..audit import audit_event
from .account_service import get_positions
from .market_service import normalize_symbol
from .signal_service import get_signal_interval, get_signal_weights, score_market_signal
@dataclass
class PortfolioRecommendation:
symbol: str
action: str
score: float
reasons: list[str]
metrics: dict[str, float]
def _portfolio_thresholds(config: dict[str, Any]) -> dict[str, float]:
portfolio_config = config.get("portfolio", {})
return {
"add_threshold": float(portfolio_config.get("add_threshold", 1.5)),
"hold_threshold": float(portfolio_config.get("hold_threshold", 0.6)),
"trim_threshold": float(portfolio_config.get("trim_threshold", 0.2)),
"exit_threshold": float(portfolio_config.get("exit_threshold", -0.2)),
"max_position_weight": float(portfolio_config.get("max_position_weight", 0.6)),
}
def _action_for_position(score: float, concentration: float, thresholds: dict[str, float]) -> tuple[str, list[str]]:
reasons: list[str] = []
max_weight = thresholds["max_position_weight"]
if concentration >= max_weight and score < thresholds["hold_threshold"]:
reasons.append("position weight is above the portfolio risk budget")
return "trim", reasons
if score >= thresholds["add_threshold"] and concentration < max_weight:
reasons.append("market signal is strong and position still has room")
return "add", reasons
if score >= thresholds["hold_threshold"]:
reasons.append("market structure remains supportive for holding")
return "hold", reasons
if score <= thresholds["exit_threshold"]:
reasons.append("market signal has weakened enough to justify an exit review")
return "exit", reasons
if score <= thresholds["trim_threshold"]:
reasons.append("edge has faded and the position should be reduced")
return "trim", reasons
reasons.append("signal is mixed and the position needs review")
return "review", reasons
def analyze_portfolio(config: dict[str, Any], *, spot_client: Any) -> dict[str, Any]:
quote = str(config.get("market", {}).get("default_quote", "USDT")).upper()
signal_weights = get_signal_weights(config)
interval = get_signal_interval(config)
thresholds = _portfolio_thresholds(config)
positions = get_positions(config, spot_client=spot_client)["positions"]
positions = [item for item in positions if item["symbol"] != quote]
total_notional = sum(item["notional_usdt"] for item in positions) or 1.0
recommendations = []
for position in positions:
symbol = normalize_symbol(position["symbol"])
klines = spot_client.klines(symbol=symbol, interval=interval, limit=24)
closes = [float(item[4]) for item in klines]
volumes = [float(item[5]) for item in klines]
tickers = spot_client.ticker_stats([symbol], window="1d")
ticker = tickers[0] if tickers else {"priceChangePercent": "0"}
concentration = position["notional_usdt"] / total_notional
score, metrics = score_market_signal(
closes,
volumes,
{"price_change_pct": float(ticker.get("priceChangePercent") or 0.0)},
signal_weights,
)
action, reasons = _action_for_position(score, concentration, thresholds)
metrics["position_weight"] = round(concentration, 4)
recommendations.append(
asdict(
PortfolioRecommendation(
symbol=symbol,
action=action,
score=round(score, 4),
reasons=reasons,
metrics=metrics,
)
)
)
payload = {"recommendations": sorted(recommendations, key=lambda item: item["score"], reverse=True)}
audit_event(
"opportunity_portfolio_generated",
{
"market_type": "spot",
"symbol": None,
"side": None,
"qty": None,
"quote_amount": None,
"order_type": None,
"dry_run": True,
"request_payload": {"mode": "portfolio"},
"response_payload": payload,
"status": "generated",
"error": None,
},
)
return payload

78
src/coinhunter/services/signal_service.py Normal file
View File

@@ -0,0 +1,78 @@
"""Shared market signal scoring."""
from __future__ import annotations
from statistics import mean
from typing import Any
def _safe_pct(new: float, old: float) -> float:
if old == 0:
return 0.0
return (new - old) / old
def get_signal_weights(config: dict[str, Any]) -> dict[str, float]:
signal_config = config.get("signal", {})
return {
"trend": float(signal_config.get("trend", 1.0)),
"momentum": float(signal_config.get("momentum", 1.0)),
"breakout": float(signal_config.get("breakout", 0.8)),
"volume": float(signal_config.get("volume", 0.7)),
"volatility_penalty": float(signal_config.get("volatility_penalty", 0.5)),
}
def get_signal_interval(config: dict[str, Any]) -> str:
signal_config = config.get("signal", {})
if signal_config.get("lookback_interval"):
return str(signal_config["lookback_interval"])
return "1h"
def score_market_signal(
closes: list[float],
volumes: list[float],
ticker: dict[str, Any],
weights: dict[str, float],
) -> tuple[float, dict[str, float]]:
if len(closes) < 2 or not volumes:
return 0.0, {
"trend": 0.0,
"momentum": 0.0,
"breakout": 0.0,
"volume_confirmation": 1.0,
"volatility": 0.0,
}
current = closes[-1]
sma_short = mean(closes[-5:]) if len(closes) >= 5 else current
sma_long = mean(closes[-20:]) if len(closes) >= 20 else mean(closes)
trend = 1.0 if current >= sma_short >= sma_long else -1.0 if current < sma_short < sma_long else 0.0
momentum = (
_safe_pct(closes[-1], closes[-2]) * 0.5
+ (_safe_pct(closes[-1], closes[-5]) * 0.3 if len(closes) >= 5 else 0.0)
+ float(ticker.get("price_change_pct", 0.0)) / 100.0 * 0.2
)
recent_high = max(closes[-20:]) if len(closes) >= 20 else max(closes)
breakout = 1.0 - max((recent_high - current) / recent_high, 0.0)
avg_volume = mean(volumes[:-1]) if len(volumes) > 1 else volumes[-1]
volume_confirmation = volumes[-1] / avg_volume if avg_volume else 1.0
volume_score = min(max(volume_confirmation - 1.0, -1.0), 2.0)
volatility = (max(closes[-10:]) - min(closes[-10:])) / current if len(closes) >= 10 and current else 0.0
score = (
weights.get("trend", 1.0) * trend
+ weights.get("momentum", 1.0) * momentum
+ weights.get("breakout", 0.8) * breakout
+ weights.get("volume", 0.7) * volume_score
- weights.get("volatility_penalty", 0.5) * volatility
)
metrics = {
"trend": round(trend, 4),
"momentum": round(momentum, 4),
"breakout": round(breakout, 4),
"volume_confirmation": round(volume_confirmation, 4),
"volatility": round(volatility, 4),
}
return score, metrics