fahricansecer/iddaai-be
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

gg

Browse Source
This commit is contained in:
Fahri Can
2026-05-10 10:37:45 +03:00
parent 4f7090e2d9
commit c525b12dfd
32 changed files with 2374 additions and 209 deletions
Download Patch File Download Diff File Expand all files Collapse all files
ai-engine/config/config_loader.py
+73 -4
View File
@@ -1,17 +1,19 @@
import os
import json
import yaml
from typing import Dict, Any, Optional
class EnsembleConfig:
_instance: Optional['EnsembleConfig'] = None
_config: Dict[str, Any] = {}
def __new__(cls):
if cls._instance is None:
cls._instance = super(EnsembleConfig, cls).__new__(cls)
cls._instance._load_config()
return cls._instance
def _load_config(self):
"""Load configuration from YAML file."""
config_path = os.path.join(os.path.dirname(__file__), 'ensemble_config.yaml')
@@ -22,12 +24,12 @@ class EnsembleConfig:
except Exception as e:
print(f"❌ Failed to load ensemble config: {e}")
self._config = {}
def get(self, key: str, default: Any = None) -> Any:
"""Get configuration value by key (supports dot notation for nested keys)."""
keys = key.split('.')
value = self._config
try:
for k in keys:
value = value[k]
@@ -35,12 +37,79 @@ class EnsembleConfig:
except (KeyError, TypeError):
return default
# Singleton accessor
def get_config() -> EnsembleConfig:
return EnsembleConfig()
# ── Market Thresholds Loader ────────────────────────────────────────────
_market_thresholds_cache: Optional[Dict[str, Any]] = None
def load_market_thresholds() -> Dict[str, Any]:
"""
Load market thresholds from JSON config file.
Returns the full config dict with 'markets' and 'defaults' keys.
Caches after first load for performance.
"""
global _market_thresholds_cache
if _market_thresholds_cache is not None:
return _market_thresholds_cache
config_path = os.path.join(os.path.dirname(__file__), 'market_thresholds.json')
try:
with open(config_path, 'r', encoding='utf-8') as f:
data = json.load(f)
_market_thresholds_cache = data
print(f"✅ Market thresholds loaded: {len(data.get('markets', {}))} markets (v={data.get('_meta', {}).get('version', '?')})")
return data
except Exception as e:
print(f"❌ Failed to load market thresholds: {e} — using built-in defaults")
_market_thresholds_cache = {"markets": {}, "defaults": {
"calibration": 0.55,
"min_conf": 55.0,
"min_play_score": 68.0,
"min_edge": 0.02,
"odds_band_min_sample": 0.0,
"odds_band_min_edge": 0.0,
}}
return _market_thresholds_cache
def build_threshold_dict(field: str) -> Dict[str, float]:
"""
Build a flat {market: value} dict for a specific threshold field.
Usage:
calibration_map = build_threshold_dict("calibration")
# → {"MS": 0.62, "DC": 0.82, ...}
"""
data = load_market_thresholds()
markets = data.get("markets", {})
result: Dict[str, float] = {}
for market, cfg in markets.items():
if field in cfg:
result[market] = float(cfg[field])
return result
def get_threshold_default(field: str) -> float:
"""Get the default fallback value for a threshold field."""
data = load_market_thresholds()
defaults = data.get("defaults", {})
return float(defaults.get(field, 0.0))
if __name__ == "__main__":
# Test
cfg = get_config()
print(f"Weights: {cfg.get('engine_weights')}")
print(f"Team Weight: {cfg.get('engine_weights.team')}")
print()
print("--- Market Thresholds ---")
for field in ["calibration", "min_conf", "min_play_score", "min_edge"]:
d = build_threshold_dict(field)
print(f"{field}: {d}")
print(f"Default calibration: {get_threshold_default('calibration')}")
ai-engine/config/market_thresholds.json
+115
View File
@@ -0,0 +1,115 @@
{
"_meta": {
"version": "v34",
"description": "Market-specific thresholds for the betting engine pipeline — V34 odds-aware gate fix",
"rule": "max_reachable (100 × calibration) MUST be > min_conf + 8",
"updated_at": "2026-05-10",
"changelog": "V34: Reduced min_edge to realistic levels for odds-aware V25 model. Model output ≈ market-implied, so large EV edges are mathematically impossible."
},
"markets": {
"MS": {
"calibration": 0.62,
"min_conf": 20.0,
"min_play_score": 28.0,
"min_edge": 0.005,
"odds_band_min_sample": 8.0,
"odds_band_min_edge": 0.005
},
"DC": {
"calibration": 0.82,
"min_conf": 40.0,
"min_play_score": 50.0,
"min_edge": 0.003,
"odds_band_min_sample": 8.0,
"odds_band_min_edge": 0.005
},
"OU15": {
"calibration": 0.84,
"min_conf": 45.0,
"min_play_score": 50.0,
"min_edge": 0.003,
"odds_band_min_sample": 8.0,
"odds_band_min_edge": 0.005
},
"OU25": {
"calibration": 0.68,
"min_conf": 30.0,
"min_play_score": 40.0,
"min_edge": 0.005,
"odds_band_min_sample": 8.0,
"odds_band_min_edge": 0.005
},
"OU35": {
"calibration": 0.60,
"min_conf": 20.0,
"min_play_score": 30.0,
"min_edge": 0.008,
"odds_band_min_sample": 8.0,
"odds_band_min_edge": 0.008
},
"BTTS": {
"calibration": 0.65,
"min_conf": 30.0,
"min_play_score": 40.0,
"min_edge": 0.005,
"odds_band_min_sample": 8.0,
"odds_band_min_edge": 0.005
},
"HT": {
"calibration": 0.58,
"min_conf": 20.0,
"min_play_score": 28.0,
"min_edge": 0.01,
"odds_band_min_sample": 8.0,
"odds_band_min_edge": 0.008
},
"HT_OU05": {
"calibration": 0.68,
"min_conf": 35.0,
"min_play_score": 42.0,
"min_edge": 0.005,
"odds_band_min_sample": 8.0,
"odds_band_min_edge": 0.005
},
"HT_OU15": {
"calibration": 0.60,
"min_conf": 25.0,
"min_play_score": 32.0,
"min_edge": 0.008,
"odds_band_min_sample": 8.0,
"odds_band_min_edge": 0.008
},
"OE": {
"calibration": 0.62,
"min_conf": 35.0,
"min_play_score": 32.0,
"min_edge": 0.005
},
"CARDS": {
"calibration": 0.58,
"min_conf": 30.0,
"min_play_score": 35.0,
"min_edge": 0.008
},
"HCAP": {
"calibration": 0.56,
"min_conf": 25.0,
"min_play_score": 30.0,
"min_edge": 0.015
},
"HTFT": {
"calibration": 0.45,
"min_conf": 10.0,
"min_play_score": 18.0,
"min_edge": 0.02
}
},
"defaults": {
"calibration": 0.55,
"min_conf": 55.0,
"min_play_score": 60.0,
"min_edge": 0.008,
"odds_band_min_sample": 0.0,
"odds_band_min_edge": 0.0
}
}