"""US free-cash-flow / balance-sheet strength regime strategy (FinLab).

How it works
------------
1. Build a liquid US stock universe: the top 500 names by 60-day average
   dollar volume, with price > $10 and dollar volume > $10M.
2. For every stock, check fundamentals (trailing twelve months, aligned to
   SEC filing dates): positive free cash flow, FCF margin above 5%, and
   debt-to-equity below 1.2, with price above its 200-day moving average.
3. Breadth gate: count what share of the universe passes. When more than 5%
   of liquid US stocks are cash-generating and in an uptrend - and QQQ itself
   is above its 200-day average with positive 6-month momentum - the market
   regime is "risk-on".
4. Execution: in risk-on, hold the 2 strongest leveraged growth ETFs
   (TQQQ / TECL) by 6-month momentum. Otherwise rotate into the strongest
   defensive asset (IEF / GLD / SHY). Monthly rebalance, 8% stop-loss.

Requires a FinLab account (free tier works): https://ai.finlab.tw
"""

import finlab
from finlab import data
from finlab.backtest import sim
from finlab.dataframe import FinlabDataFrame

finlab.login()

RISK_ASSETS = ["TQQQ", "TECL"]
DEFENSIVE_ASSETS = ["IEF", "GLD", "SHY"]
START_DATE = "2016-01-01"

# ---------------------------------------------------------------------------
# 1. Liquid US universe (point-in-time, survivorship-safe)
# ---------------------------------------------------------------------------
data.set_market("us")
close = data.get("us_price:adj_close")
volume = data.get("us_price:volume")

dollar_volume = (close * volume).rolling(60, min_periods=20).mean()
top_liquid = dollar_volume.is_largest(500)
liquid_enough = dollar_volume > 10_000_000
price_ok = close > 10

returns = close.pct_change()
extreme_move = returns.abs() >= 0.50
known_bad = extreme_move.rolling(252, min_periods=1).max().shift(1).fillna(False) > 0
clean = ~known_bad.astype(bool)

universe = top_liquid & liquid_enough & price_ok & clean

# ---------------------------------------------------------------------------
# 2. Free-cash-flow / balance-sheet fundamentals (filing-date aligned)
# ---------------------------------------------------------------------------
revenue = data.get("us_income_statement:revenue")
ocf = data.get("us_cash_flow:operating_cash_flow")
capex = data.get("us_cash_flow:capital_expenditure")
total_debt = data.get("us_balance_sheet:total_debt")
equity = data.get("us_balance_sheet:total_stockholders_equity")

fcf = (ocf + capex).rolling(4).sum()          # trailing-twelve-month free cash flow
ttm_revenue = revenue.rolling(4).sum()
fcf_margin = fcf / ttm_revenue
debt_to_equity = total_debt / equity
trend = close > close.average(200)

fcf_entry = universe & trend & (fcf > 0) & (fcf_margin > 0.05) & (debt_to_equity < 1.2)

# ---------------------------------------------------------------------------
# 3. Breadth gate + market trend filter
# ---------------------------------------------------------------------------
fund_close = data.get("us_fund_price:adj_close")[["QQQ"] + RISK_ASSETS + DEFENSIVE_ASSETS]

fcf_breadth = fcf_entry.sum(axis=1) / universe.sum(axis=1)
qqq_trend = fund_close["QQQ"] > fund_close["QQQ"].rolling(200, min_periods=100).mean()
qqq_momentum = fund_close["QQQ"] / fund_close["QQQ"].shift(126) - 1
risk_on = (fcf_breadth > 0.05) & (qqq_trend & (qqq_momentum > 0)).reindex(fcf_breadth.index)

# ---------------------------------------------------------------------------
# 4. ETF rotation: leveraged growth in risk-on, defensive otherwise
# ---------------------------------------------------------------------------
risk_score = FinlabDataFrame(fund_close[RISK_ASSETS].pct_change(126))
defensive_score = FinlabDataFrame(
    fund_close[DEFENSIVE_ASSETS].pct_change(63) - fund_close[DEFENSIVE_ASSETS].pct_change(21)
)

risk_on_daily = risk_on.reindex(fund_close.index).fillna(False)
risky_position = risk_score.is_largest(2) & risk_on_daily.to_frame().reindex(risk_score.index).iloc[:, 0]
risky_position = risky_position.astype(float).div(risky_position.astype(float).sum(axis=1), axis=0).fillna(0)
defensive_position = (
    defensive_score.is_largest(1)
    & (~risk_on_daily).to_frame().reindex(defensive_score.index).iloc[:, 0]
).astype(float)

position = (
    risky_position.reindex(fund_close.index).fillna(0)
    .join(defensive_position.reindex(fund_close.index).fillna(0), how="outer")
    .fillna(0)
    .T.groupby(level=0).max().T
)
position = position.loc[START_DATE:]

# ---------------------------------------------------------------------------
# 5. Backtest (monthly rebalance, 8% stop-loss)
# ---------------------------------------------------------------------------
data.set_market("us_fund")
report = sim(
    position,
    resample="M",
    name="fcf_balance_sheet_strength",
    upload=False,
    fee_ratio=0,
    tax_ratio=0,
    trade_at_price="close",
    position_limit=1,
    stop_loss=0.08,
    touched_exit=True,
)

stats = report.get_stats()
print(f"CAGR:           {stats['cagr']:.2%}")
print(f"Monthly Sharpe: {stats['monthly_sharpe']:.2f}")
print(f"Max drawdown:   {stats['max_drawdown']:.2%}")
report.display()