""" Live Betting Signal Scanner Downloads today's fixtures from football-data.co.uk and flags value bets using the CLV strategy (B365 vs sharp reference odds). Sharp reference priority: BFE (Betfair Exchange) > Avg market odds """ import pandas as pd import urllib.request from datetime import datetime # --------------------------------------------------------------- # CONFIG # --------------------------------------------------------------- BANKROLL = 1000.0 # <-- set your current bankroll here KELLY_MULT = 0.25 # quarter Kelly EDGE_THRESHOLD = 0.05 # 5% minimum edge MAX_BET_PCT = 0.10 # cap any single bet at 10% of bankroll FIXTURES_URL = 'https://www.football-data.co.uk/fixtures.csv' # Consistently profitable league+outcome combinations (backtested 2012-2026) LEAGUES = [ 'E0','E1','E2','E3','EC','SP1','SP2','I1','I2','D1','D2', 'F1','F2','N1','B1','P1','T1','G1','SC0','SC1','SC2','SC3', ] OUTCOMES = [('Home','B365H'),('Draw','B365D'),('Away','B365A')] LEAGUE_NAMES = { 'E0':'EPL','E1':'Championship','E2':'League One','E3':'League Two','EC':'Conference', 'SP1':'La Liga','SP2':'Segunda','I1':'Serie A','I2':'Serie B', 'D1':'Bundesliga','D2':'2.Bundesliga','F1':'Ligue 1','F2':'Ligue 2', 'N1':'Eredivisie','B1':'Belgian Pro League','P1':'Primeira Liga', 'T1':'Super Lig','G1':'Greek SL', 'SC0':'Scottish Prem','SC1':'Scottish Champ','SC2':'Scottish L1','SC3':'Scottish L2', } # --------------------------------------------------------------- def remove_margin(h, d, a): over = 1/h + 1/d + 1/a return 1/h/over, 1/d/over, 1/a/over def kelly_stake(edge, net_odds, bankroll): f = edge / net_odds f = f * KELLY_MULT f = min(f, MAX_BET_PCT) return round(bankroll * f, 2) # --------------------------------------------------------------- # Download fixtures # --------------------------------------------------------------- print(f"Downloading fixtures... ({datetime.now().strftime('%Y-%m-%d %H:%M')})") urllib.request.urlretrieve(FIXTURES_URL, 'E:/soccer/fixtures_live.csv') df = pd.read_csv('E:/soccer/fixtures_live.csv', encoding='utf-8-sig') df['Date'] = pd.to_datetime(df['Date'], dayfirst=True, errors='coerce') df = df.dropna(subset=['Date']) print(f"Loaded {len(df)} fixtures across {df['Div'].nunique()} leagues") print(f"Date range: {df['Date'].min().date()} to {df['Date'].max().date()}\n") # --------------------------------------------------------------- # Scan for signals # --------------------------------------------------------------- signals = [] for _, row in df.iterrows(): div = row.get('Div') if div not in LEAGUES: continue # Sharp reference: prefer BFE (Betfair Exchange), fall back to Avg ref_h = ref_d = ref_a = ref_source = None for h_col, d_col, a_col in [('BFEH','BFED','BFEA'), ('AvgH','AvgD','AvgA')]: try: rh = float(row[h_col]); rd = float(row[d_col]); ra = float(row[a_col]) if all(x > 1.0 for x in [rh, rd, ra]): ref_h, ref_d, ref_a = rh, rd, ra ref_source = h_col.replace('H','') break except (ValueError, KeyError, TypeError): continue if ref_h is None: continue tp_h, tp_d, tp_a = remove_margin(ref_h, ref_d, ref_a) fair_map = {'B365H': 1/tp_h, 'B365D': 1/tp_d, 'B365A': 1/tp_a} prob_map = {'B365H': tp_h, 'B365D': tp_d, 'B365A': tp_a } for outcome, b365_col in OUTCOMES: try: b365_odds = float(row[b365_col]) except (ValueError, KeyError, TypeError): continue if b365_odds <= 1.0: continue ps_fair = fair_map[b365_col] ps_prob = prob_map[b365_col] edge = (b365_odds - ps_fair) / ps_fair if edge < EDGE_THRESHOLD: continue ev = b365_odds * ps_prob - 1 net_odds = b365_odds - 1 if ev <= 0: continue kelly_f_raw = ev / net_odds stake = kelly_stake(ev, net_odds, BANKROLL) signals.append({ 'Date': row['Date'].strftime('%a %d %b'), 'Time': str(row.get('Time', '')).strip(), 'League': LEAGUE_NAMES.get(div, div), 'Home': row['HomeTeam'], 'Away': row['AwayTeam'], 'Bet': outcome, 'B365': round(b365_odds, 2), 'PS_fair': round(ps_fair, 2), 'Edge%': round(edge*100, 1), 'Kelly_raw%': round(kelly_f_raw*100, 1), 'Stake £': stake, 'Ref': ref_source, }) signals_df = pd.DataFrame(signals).sort_values(['Date','Time']).reset_index(drop=True) # --------------------------------------------------------------- # Print betting slip # --------------------------------------------------------------- print("=" * 95) print(f"BETTING SIGNALS — {datetime.now().strftime('%Y-%m-%d')} | Bankroll: £{BANKROLL:,.2f} | Strategy: {KELLY_MULT}x Kelly, >{EDGE_THRESHOLD*100:.0f}% edge") print("=" * 95) if signals_df.empty: print(" No qualifying bets found.") else: total_stake = signals_df['Stake £'].sum() print(f"\n {len(signals_df)} qualifying bets | Total stake: £{total_stake:,.2f} ({total_stake/BANKROLL*100:.1f}% of bankroll)\n") print(f" {'Date':<12} {'Time':<7} {'League':<20} {'Match':<35} {'Bet':<6} {'B365':>6} {'Ref Fair':>9} {'Edge':>6} {'Kelly':>6} {'Stake £':>8} {'Ref':<5}") print(" " + "-"*100) for _, s in signals_df.iterrows(): match = f"{s['Home']} vs {s['Away']}" if len(match) > 34: match = match[:31] + "..." print(f" {s['Date']:<12} {s['Time']:<7} {s['League']:<20} {match:<35} {s['Bet']:<6} " f"{s['B365']:>6.2f} {s['PS_fair']:>9.2f} {s['Edge%']:>5.1f}% {s['Kelly_raw%']:>5.1f}% " f"£{s['Stake £']:>7.2f} {s['Ref']:<5}") print() print("=" * 95) print(f" Total outlay: £{total_stake:,.2f}") print(f" If all bets win (unlikely): £{sum((s['B365']-1)*s['Stake £'] for _,s in signals_df.iterrows()):+,.2f}") print(f" If all bets lose (worst case): -£{total_stake:,.2f}") print("=" * 95) # --------------------------------------------------------------- # Also show upcoming fixtures with no signal (close to threshold) # --------------------------------------------------------------- near_misses = [] for _, row in df.iterrows(): div = row.get('Div') if div not in LEAGUE_CONFIG: continue outcome, b365_col, ps_col = LEAGUE_CONFIG[div] try: b365_odds = float(row[b365_col]) except: continue ref_h = ref_d = ref_a = None for h_col,d_col,a_col in [('BFEH','BFED','BFEA'),('AvgH','AvgD','AvgA')]: try: rh=float(row[h_col]); rd=float(row[d_col]); ra=float(row[a_col]) if all(x>1.0 for x in [rh,rd,ra]): ref_h,ref_d,ref_a = rh,rd,ra; break except: continue if ref_h is None or b365_odds<=1.0: continue tp_h,tp_d,tp_a = remove_margin(ref_h,ref_d,ref_a) ps_fair = {'B365H':1/tp_h,'B365D':1/tp_d,'B365A':1/tp_a}[b365_col] edge = (b365_odds - ps_fair) / ps_fair if 0.02 <= edge < EDGE_THRESHOLD: near_misses.append({ 'Date': row['Date'].strftime('%a %d %b'), 'Time': str(row.get('Time','')).strip(), 'League': LEAGUE_NAMES.get(div,div), 'Match': f"{row['HomeTeam']} vs {row['AwayTeam']}", 'Bet': outcome, 'B365': round(b365_odds,2), 'PS_fair': round(ps_fair,2), 'Edge%': round(edge*100,1), }) if near_misses: nm_df = pd.DataFrame(near_misses).sort_values('Edge%', ascending=False) print(f"\n NEAR MISSES (2-5% edge, watching brief):") print(f" {'Date':<12} {'Time':<7} {'League':<20} {'Match':<35} {'Bet':<6} {'B365':>6} {'PS Fair':>8} {'Edge':>6}") print(" " + "-"*93) for _, s in nm_df.iterrows(): match = s['Match'] if len(s['Match'])<=34 else s['Match'][:31]+"..." print(f" {s['Date']:<12} {s['Time']:<7} {s['League']:<20} {match:<35} {s['Bet']:<6} " f"{s['B365']:>6.2f} {s['PS_fair']:>8.2f} {s['Edge%']:>5.1f}%")