gw = sum(w_list)

gl = abs(sum(l_list))

if gl == 0:

return float("inf") if gw > 0 else 0

try:

if "T" in entry_time_str:

dt = datetime.fromisoformat(entry_time_str)

elif "AM" in entry_time_str or "PM" in entry_time_str:

dt = datetime.strptime(entry_time_str, "%m/%d/%Y %I:%M:%S %p")

else:

dt = datetime.strptime(entry_time_str, "%Y-%m-%d %H:%M:%S")

mins = dt.hour * 60 + dt.minute

if instrument in ("ES","MES","NQ","MNQ","RTY","M2K","YM","MYM"):

return 570 <= mins < 960

elif instrument in ("CL","MCL"):

return 540 <= mins < 870

elif instrument in ("GC","MGC"):

return 500 <= mins < 810

except Exception:

pass

if not trades:

return

profits = [t["profit"] or 0 for t in trades]

total = sum(profits)

n = len(trades)

days = len(set(t["entryDate"] for t in trades))

w = [p for p in profits if p > 0]

l = [p for p in profits if p < 0]

wr = len(w) / n * 100

pf_val = pf(w, l)

inv_w = [-p for p in l]

inv_l = [-p for p in w]

inv_pf = pf(inv_w, inv_l)

avg = statistics.mean(profits)

avg_w = statistics.mean(w) if w else 0

avg_l = statistics.mean(l) if l else 0

first = min(t["entryDate"] for t in trades)

last = max(t["entryDate"] for t in trades)

conn = sqlite3.connect(DB)

conn.row_factory = sqlite3.Row

cur = conn.cursor()

cur.execute("""

""")

all_trades = [dict(r) for r in cur.fetchall()]

conn.close()

# Filter excluded accounts and non-portfolio instruments

all_trades = [t for t in all_trades if not is_excluded(t["strategy"]) and t["instrument"] in VALID]

print("=" * 150)

print(f"{'REVERSAL STRATEGY -- COMPLETE BREAKDOWN':^150}")

print(f"{'(' + str(len(all_trades)) + ' trades, portfolio instruments only)':^150}")

print("=" * 150)

# ========== OVERALL ==========

print(f"\n--- OVERALL ---")

analyze_group("ALL TRADES", all_trades)

# ========== BY INSTRUMENT ==========

print(f"\n\n{'=' * 150}")

print(f"{'BY INSTRUMENT':^150}")

print(f"{'=' * 150}")

by_inst = defaultdict(list)

for t in all_trades:

by_inst[t["instrument"]].append(t)

for inst in sorted(by_inst.keys(), key=lambda x: sum(t["profit"] or 0 for t in by_inst[x]), reverse=True):

analyze_group(inst, by_inst[inst])

# ========== BY INSTRUMENT x DIRECTION ==========

print(f"\n\n{'=' * 150}")

print(f"{'BY INSTRUMENT x DIRECTION':^150}")

print(f"{'=' * 150}")

by_combo = defaultdict(list)

for t in all_trades:

by_combo[(t["instrument"], t["direction"])].append(t)

for combo in sorted(by_combo.keys(), key=lambda x: sum(t["profit"] or 0 for t in by_combo[x]), reverse=True):

analyze_group(f"{combo[0]} {combo[1]}", by_combo[combo])

# ========== BY INSTRUMENT x DIRECTION x SESSION ==========

print(f"\n\n{'=' * 150}")

print(f"{'BY INSTRUMENT x DIRECTION x SESSION':^150}")

print(f"{'=' * 150}")

by_full = defaultdict(list)

for t in all_trades:

rth = is_rth(t["instrument"], t["entryTime"] or "")

sess = "RTH" if rth is True else ("ETH" if rth is False else "UNK")

by_full[(t["instrument"], t["direction"], sess)].append(t)

for combo in sorted(by_full.keys(), key=lambda x: sum(t["profit"] or 0 for t in by_full[x]), reverse=True):

analyze_group(f"{combo[0]} {combo[1]} {combo[2]}", by_full[combo])

# ========== TIME OF DAY BY INSTRUMENT ==========

print(f"\n\n{'=' * 150}")

print(f"{'TIME-OF-DAY BY INSTRUMENT (top instruments)':^150}")

print(f"{'=' * 150}")

for inst in ["NQ","MNQ","RTY","M2K","ES","MES","GC","MGC","YM","MYM","CL","MCL"]:

inst_trades = by_inst.get(inst, [])

if len(inst_trades) < 5:

continue

print(f"\n --- {inst} ({len(inst_trades)} trades, ${sum(t['profit'] or 0 for t in inst_trades):>+,.2f}) ---")

hh_map = defaultdict(list)

for t in inst_trades:

hh_map[t["entryHalfHour"] or "unknown"].append(t["profit"] or 0)

print(f" {'Time':>8s} {'N':>5s} {'P/L':>12s} {'WR':>7s} {'PF':>7s} {'$/Trade':>10s}")

for hh in sorted(hh_map.keys()):

profs = hh_map[hh]

total = sum(profs)

wr = sum(1 for p in profs if p > 0) / len(profs) * 100

w = [p for p in profs if p > 0]

l = [p for p in profs if p < 0]

pf_val = pf(w, l)

avg = statistics.mean(profs)

bar = "+" * max(0, int(total / 200)) if total > 0 else "-" * max(0, int(-total / 200))

print(f" {hh:>8s} {len(profs):>5d} ${total:>+10,.2f} {wr:>6.1f}% {pf_val:>6.2f} ${avg:>+8,.2f} {bar}")

# ========== MONTHLY BY INSTRUMENT ==========

print(f"\n\n{'=' * 150}")

print(f"{'MONTHLY P/L BY INSTRUMENT':^150}")

print(f"{'=' * 150}")

for inst in ["NQ","MNQ","RTY","M2K","ES","MES","GC","MGC","YM","MYM","CL","MCL"]:

inst_trades = by_inst.get(inst, [])

if len(inst_trades) < 5:

continue

print(f"\n --- {inst} ---")

monthly = defaultdict(float)

monthly_n = defaultdict(int)

for t in inst_trades:

m = t["entryDate"][:7]

monthly[m] += (t["profit"] or 0)

monthly_n[m] += 1

cum = 0

for m in sorted(monthly.keys()):

cum += monthly[m]

bar = "+" * max(0, int(monthly[m] / 200)) if monthly[m] > 0 else "-" * max(0, int(-monthly[m] / 200))

print(f" {m}: {monthly_n[m]:>3d}t ${monthly[m]:>+8,.0f} (cum: ${cum:>+10,.0f}) {bar}")

# ========== DIRECTION BREAKDOWN PER INSTRUMENT ==========

print(f"\n\n{'=' * 150}")

print(f"{'LONG vs SHORT PER INSTRUMENT -- ORIGINAL vs INVERSED':^150}")

print(f"{'=' * 150}")

print(f"\n {'Inst+Dir':<15s} {'N':>5s} {'Days':>5s} {'Orig P/L':>12s} {'Orig PF':>8s} {'Orig WR':>8s} {'Inv P/L':>12s} {'Inv PF':>8s} {'Better':>10s} {'Confidence'}")

print(f" {'-' * 110}")

for combo in sorted(by_combo.keys(), key=lambda x: (x[0], x[1])):

trades = by_combo[combo]

n = len(trades)

if n < 3:

continue

days = len(set(t["entryDate"] for t in trades))

profits = [t["profit"] or 0 for t in trades]

total = sum(profits)

w = [p for p in profits if p > 0]

l = [p for p in profits if p < 0]

wr = len(w) / n * 100

pf_val = pf(w, l)

inv_w = [-p for p in l]

inv_l = [-p for p in w]

inv_pf = pf(inv_w, inv_l)

better = "ORIGINAL" if pf_val >= inv_pf else "INVERSE"

better_pf = max(pf_val, inv_pf)

conf = ""

if n >= 30 and days >= 15 and better_pf >= 1.3:

conf = "HIGH"

elif n >= 15 and days >= 8 and better_pf >= 1.15:

conf = "MEDIUM"

elif n >= 8 and better_pf >= 1.1:

conf = "LOW"

else:

conf = "VERY LOW"