PythagorasGoal/scripts/research/blind/agents/agent_14_rsi.py

"""Agent 14 — RSI reversion, trend-gated (family=meanrev, slug=rsi).

The angle (assigned): RSI reversion. Long when RSI<lo, short when RSI>hi (bl.rsi),
GATED by a longer trend filter. Tune lo/hi/win.

Reading the train curves first (both A and B, split='train'): they trend UP hard
(ann vol ~0.7-0.9, total ret +6.7x / +23x over the window). The TEXTBOOK 30/70 RSI
thresholds are dead here: in these up-curves RSI sits >70 ~11% of bars and the dips
only floor around RSI 40-45 — RSI<30 in an uptrend happens ~0.1% of the time. A naive
symmetric "short every RSI>70" rule would just short the bull and bleed. So the
mean-reversion has to be REGIME-AWARE, and the lo/hi have to be tuned to the data's
actual RSI distribution, not the textbook:

  * In an UPTREND (close above a long SMA) RSI dips are BUY-THE-DIP reversion. We go
    LONG when RSI drops below LO and HOLD that long (hysteresis) until RSI recovers
    past a higher EXIT level — the classic RSI entry/exit pair — then flat. We do NOT
    short RSI>hi here (overbought in an uptrend keeps running; that is momentum).
  * In a DOWNTREND (close below the long SMA) the symmetry returns: RSI>HI is a
    reversion SHORT (rips fade back down); RSI<LO we stand flat (don't knife-catch
    long against a downtrend). The short side is weighted < 1 because the curves drift
    up — on train it adds a touch of PnL with no DD cost but is not where the edge is.

The long trend filter does two jobs: it picks WHICH side of the RSI book is reversion
(buy dips in up-trend / sell rips in down-trend) and it suppresses the side that fights
the drift. TREND_WIN=150 is the DD sweet spot on train (DD 0.11 vs 0.16-0.21 at 100/200)
— the gate is what keeps the drawdown small. Sizing is smooth (further past the
threshold -> bigger appetite, no hard 0/1 fee-churning flips) then vol-targeted so the
two curves are risk-comparable and exposure shrinks into vol spikes (crashes are vol
spikes), bounding the drawdown.

HONEST NOTE: in a market that trends this hard, a trend-gated RSI dip-buy partially
degenerates toward trend participation — the dips it buys are shallow (RSI ~50s, not
30s) and it rides them up. The genuine reversion content is the buy-low/exit-high cycle
and the DD control from the trend gate + vol-target; the short side carries almost no
weight in the train edge. The result is an honest-but-modest combined train Sharpe ~1.1
at ~11% DD (vs long-only buy&hold's ~7-23x PnL at ~70-80% DD) — i.e. a fraction of the
buy&hold PnL but ~6-7x less drawdown.

CAUSAL: rsi() is an EWMA of past gains/losses (<= i); the SMA trend filter is trailing;
the hold-state is a forward cumulative pass over PAST bars only; vol_target uses trailing
realized vol. No shift(-k), no centered windows, no global fit. Verified by causality_ok
(max_diff 0.0).

Tuning (train only, combined A&B; coarse->fine sweep + plateau check). Chosen cell is
INTERIOR on every axis — RW in [18..25], LO in [56..62], EXIT in [75..85], TWIN=150,
TVOL [0.20..0.25] all stay sharpe_min ~1.0..1.26 at DD ~0.11..0.13, a broad plateau not
a spike. (Pushing LO/EXIT higher keeps lifting train Sharpe but only by degenerating into
buy-and-hold, so we stop at an interior dip-entry cell that is still genuinely a dip rule.)
  RSI_WIN=20, LO=58, HI=68, EXIT=78, TREND_WIN=150
  SHORT_W=0.5, TARGET_VOL=0.25, VOL_WIN_DAYS=35, LEV_CAP=1.5, BASE=0.6
  -> train combined: pnl_mean ~0.87, maxdd_worst ~0.11, sharpe_min ~1.14
"""
import numpy as np
import blindlib as bl

RSI_WIN = 20        # RSI lookback (the "win" of the angle; 20 > textbook 14 for these trends)
LO = 58.0           # oversold/dip threshold -> reversion LONG (tuned to the curves' RSI floor)
HI = 68.0           # overbought threshold -> reversion SHORT (downtrend only)
EXIT = 78.0         # dip-long is HELD until RSI recovers past EXIT (hysteresis entry/exit pair)
TREND_WIN = 150     # long SMA: above = uptrend (buy dips), below = downtrend (sell rips). DD sweet spot.
SHORT_W = 0.5       # weight on the downtrend short side; <1 because the curves drift up
BASE = 0.6          # base long size while holding a dip (scaled up if still oversold)
TARGET_VOL = 0.25
VOL_WIN_DAYS = 35
LEV_CAP = 1.5


def signal(df):
    c = df["close"].values.astype(float)
    n = len(c)
    rs = bl.rsi(c, RSI_WIN)
    trend_up = c > bl.sma(c, TREND_WIN)            # causal trailing SMA trend gate

    # --- smooth reversion appetite from RSI (further past threshold -> bigger) ---
    long_app = np.clip((LO - rs) / 25.0, 0.0, 1.0)        # oversold -> long appetite
    short_app = np.clip((rs - HI) / (100.0 - HI), 0.0, 1.0)  # overbought -> short appetite

    # --- trend-gated RSI reversion with hysteresis on the dip-long ---
    # The forward pass below is PURE PAST-ONLY: in_long at bar i depends only on bars <= i
    # (rs, trend_up are causal; the state machine never looks ahead). Causality verified.
    held = np.zeros(n)
    in_long = False
    for i in range(n):
        if in_long:
            # exit the held dip-long when the trend breaks down OR RSI has recovered
            if (not trend_up[i]) or (rs[i] >= EXIT):
                in_long = False
        else:
            # enter a dip-long only in an uptrend when RSI is below LO (oversold dip)
            if trend_up[i] and rs[i] < LO:
                in_long = True
        if in_long:
            held[i] = max(BASE, long_app[i])     # ride the recovery, bigger if still oversold
        else:
            # when not holding a long, only the downtrend reversion-short passes through
            held[i] = (-SHORT_W * short_app[i]) if (not trend_up[i]) else 0.0

    pos = bl.vol_target(held, df, target_vol=TARGET_VOL,
                        vol_win_days=VOL_WIN_DAYS, leverage_cap=LEV_CAP)
    return np.clip(np.nan_to_num(pos, nan=0.0), -1.0, 1.0)