strategy_000004 · Foreseer V3

Horizon Breakdown

top 100 only

Horizon	Average Top-100 Hits	Predicted Top-100 Avg Actual Rank
3m	25.22	236.87
6m	25.19	235.64
12m	24.46	237.09

Run Details

strategy_test_results #6

id	6
strategy_id	strategy_000004
strategy_hash	29b66c080a0027d3a30586a95be7cfc972b3757d331ec95b69bb0fb5eb3d0464
evaluator_version	v3_top100_monthly
start_date	2015-01-01
end_date	2025-06-01
created_at	2026-07-24 21:02:16
updated_at	2026-07-24 21:02:16

Sample Top-100 Lists

2020-03-31 / 6m / 23 hits

Predicted top 100

A, AAL, AAP, AAPL, ABBV, ABC, ABMD, ABT, ACN, ADBE, ADI, ADM, ADP, ADSK, AEE, AEP, AES, AFL, AIG, AIZ, AJG, AKAM, ALB, ALGN, ALK, ALL, ALLE, ALXN, AMAT, AMCR, AMD, AME, AMGN, AMP, AMT, AMZN, ANET, ANSS, AON, AOS, APD, APH, APTV, ARE, ATO, ATVI, AVB, AVGO, AVY, AWK, AXP, AZO, BA, BAC, BIIB, BIO, BMY, CAG, CARR, CCI, CLX, CNC, CPB, CTLT, DLR, DPZ, DVA, DXCM, EA, EQIX, FTNT, GILD, GIS, HRL, HUM, INTC, JKHY, K, KR, LDOS, LLY, MSCI, MSFT, NEE, NEM, NFLX, NOW, NVDA, ODFL, OTIS, REGN, RMD, ROL, SBAC, SJM, TMUS, TSLA, TYL, VRTX, WST

Actual top 100

AAP, AAPL, ABMD, ADBE, ADSK, ALB, ALGN, AMD, AMP, AMZN, APD, APH, APTV, ATVI, AVGO, BBY, BWA, CARR, CDNS, CE, CHRW, CMG, CMI, CPRT, CRM, CTAS, CTLT, CTSH, DD, DE, DHI, DHR, DRI, DXCM, EBAY, EMN, ETSY, EXPE, FCX, FDX, GLW, GPS, HAL, HD, IDXX, IPGP, JCI, KMX, LEG, LEN, LH, LOW, MAS, MCHP, MGM, MOS, NCLH, NKE, NOW, NSC, NVDA, NWS, NWSA, ORLY, PAYC, PH, PHM, PNR, POOL, PVH, PWR, PYPL, QCOM, QRVO, RCL, ROK, ROL, SHW, SIVB, SNPS, SWK, SWKS, SYF, TDG, TEL, TER, TGT, TMO, TSCO, TSLA, TT, UPS, URI, VAR, VIAC, VTR, WHR, WMB, WST, WY

Matched tickers

AAP, AAPL, ABMD, ADBE, ADSK, ALB, ALGN, AMD, AMP, AMZN, APD, APH, APTV, ATVI, AVGO, CARR, CTLT, DXCM, NOW, NVDA, ROL, TSLA, WST

Strategy Script

29b66c080a

"""Generated V3 stock-ranking strategy."""
from __future__ import annotations

import numpy as np


STRATEGY_ID = "strategy_000004"
DESCRIPTION = """
Ranks candidates with date-local normalization and a higher emphasis on
near-term momentum, growth, valuation, volatility, and constructive pullbacks.
"""
FACTORS_USED = [
    "return_1m_pct",
    "return_3m_pct",
    "return_6m_pct",
    "return_12m_pct",
    "momentum_12_1_pct",
    "eps_growth_pct",
    "revenue_growth_pct",
    "operating_margin_pct",
    "free_cash_flow_margin_pct",
    "forward_pe",
    "pe",
    "vol_63d",
    "from_52w_high_pct",
    "from_200d_ma_pct",
    "market_cap",
]
PARAMETERS = {
    "r1": 0.384497074445,
    "r3": 0.381685296643,
    "r6": -0.245338429014,
    "r12": -0.059927288111,
    "m121": -0.075349992155,
    "growth": 0.770849543724,
    "margin": 0.032539894260,
    "value": 0.537869452153,
    "vol": 0.787837043192,
    "size": 0.343140979477,
    "pull": 0.475639458254,
    "sma": 0.259117920485,
    "min_sma": -0.158681044236,
}
COMPLEXITY = 6
PARENT_STRATEGY_ID = "strategy_000001"
GENERATION = 2
RANDOM_SEED = 20260725
CREATED_AT = "2026-07-25T00:00:00+00:00"


def _date_zscore(df, column):
    clean = df[column].replace([np.inf, -np.inf], np.nan)
    if "trade_date" not in df.columns:
        std = clean.std()
        if not np.isfinite(std) or std == 0:
            return clean * 0.0
        return ((clean - clean.mean()) / std).fillna(0)

    grouped = clean.groupby(df["trade_date"])
    mean = grouped.transform("mean")
    std = grouped.transform("std").replace(0, np.nan)
    return ((clean - mean) / std).fillna(0)


def eligibility_filter(df):
    return df["from_200d_ma_pct"].fillna(0) >= PARAMETERS["min_sma"]


def calculate_score(df):
    growth = 0.55 * _date_zscore(df, "eps_growth_pct") + 0.45 * _date_zscore(df, "revenue_growth_pct")
    margin = 0.55 * _date_zscore(df, "operating_margin_pct") + 0.45 * _date_zscore(df, "free_cash_flow_margin_pct")
    value = -0.65 * _date_zscore(df, "forward_pe") - 0.35 * _date_zscore(df, "pe")
    pullback = (-df["from_52w_high_pct"].fillna(0)).clip(lower=0, upper=0.45)
    constructive_pullback = pullback.where(df["return_6m_pct"].fillna(0) > 0, 0)
    size = _date_zscore(df.assign(_log_market_cap=np.log1p(df["market_cap"].clip(lower=0).fillna(0))), "_log_market_cap")

    score = (
        PARAMETERS["r1"] * _date_zscore(df, "return_1m_pct")
        + PARAMETERS["r3"] * _date_zscore(df, "return_3m_pct")
        + PARAMETERS["r6"] * _date_zscore(df, "return_6m_pct")
        + PARAMETERS["r12"] * _date_zscore(df, "return_12m_pct")
        + PARAMETERS["m121"] * _date_zscore(df, "momentum_12_1_pct")
        + PARAMETERS["growth"] * growth
        + PARAMETERS["margin"] * margin
        + PARAMETERS["value"] * value
        + PARAMETERS["vol"] * _date_zscore(df, "vol_63d")
        + PARAMETERS["size"] * size
        + PARAMETERS["pull"] * constructive_pullback
        + PARAMETERS["sma"] * _date_zscore(df, "from_200d_ma_pct")
    )
    score = score.where(eligibility_filter(df), -1e9)
    return score.replace([np.inf, -np.inf], np.nan).fillna(score.median()).fillna(0.0)