An alpha template is a reusable recipe that captures an economic idea and leaves “slots” (data fields, operators, groups, decay, neutralization choices, etc.) to instantiate many candidate alphas. Typical structure: clean data (backfill, winsorize) → transform/compare across time or peers → rank/neutralize → (optionally) decay/turnover tune. Templates encourage systematic search, reuse, and diversification while keeping an explicit economic rationale.

Some Example Templates and rationales

CAPM residual (market/sector-neutral return): ts_regression(returns, group_mean(returns, log(ts_mean(cap,21)), sector), 252, rettype=0) after backfill+winsorize. Rationale: strip market/sector beta to isolate idiosyncratic alpha; sector-weighted by smoothed log-cap to reduce large-cap dominance.
CAPM beta (slope) template: same regression with rettype=2; pre-clean target/market (ts_backfill(...,63) + winsorize(std=4)). Rationale: rank stocks by relative risk within sector; long low-β, short high-β, or study β dispersion across groups.
CAPM generalized to any feature: data = winsorize(ts_backfill(<data>,63),std=4); data_gpm = group_mean(data, log(ts_mean(cap,21)), sector); resid = ts_regression(data, data_gpm, 252, rettype=0). Rationale: pull out the component unexplained by group average of same feature; reduces common-mode exposure.
Actual vs estimate spread (analyst): group_zscore( group_zscore(<act>, industry) – group_zscore(<est>, industry), industry ) or the abstracted group_compare(diff(group_compare(act,...), group_compare(est,...)), ...). Rationale: surprise/beat-miss signal within industry, normalized to peers to avoid level bias.
Analyst term-structure (fp1 vs fy1/fp2/fy2): group_zscore( group_zscore(anl14_mean_eps_<period1>, industry) – group_zscore(anl14_mean_eps_<period2>, industry), industry ) with operator/group slots. Rationale: cross-period expectation steepness; rising near-term vs long-term forecasts can flag momentum/inflection.
Option Greeks net spread: group_operator(<put_greek> - <call_greek>, <grouping_data>) over industry/sector (Delta/Gamma/Vega/Theta). Rationale: options-implied sentiment/convexity skew vs peers; outlier net Greeks may precede spot moves; extend with multi-Greek composites or time-series deltas.


based on the following guidance of how to make a data collation template into a signal, and guidance on how to utilize the best of operators.

guidance of how to make a data collation template into a signal
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D:\BRAINProject\cnhkmcp\cnhkmcp\untracked\APP\trailSomeAlphas\skills\template_final_enhance\单因子思考逻辑链.md
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'guidance on how to use the best of operators'
D:\BRAINProject\cnhkmcp\cnhkmcp\untracked\APP\trailSomeAlphas\skills\template_final_enhance\op总结.md
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improve the following raw template
{  "template": "({disclosed_intangibles} + {goodwill}) / {tot_asset} * {sales_rev_turn}",
    "idea": "Intangible asset intensity scaled by revenue productivity to measure efficiency of non-physical capital."
}  
in the template, {} represent datafield set so keep it as that way, you should just return your new template with the operators you choose and properparameter. the output keey the same in json format and try to output as much diverse enhanced template as you can.
[{
    "enhanced_template": "",
    "idea": ""
},{
    "enhanced_template": "",
    "idea": ""
},
]



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sample output

[
    {
        "enhanced_template": "signed_power(group_zscore(winsorize((ts_backfill({disclosed_intangibles},63) + ts_backfill({goodwill},63)) / ts_backfill({tot_asset},63), std=4) * winsorize(ts_backfill({sales_rev_turn},63), std=4), sector), 0.5)",
        "idea": "Sector-neutral intangible capital efficiency with concave transformation (sqrt). Assumes mid-range efficiency differentials are noisy; sub-linear scaling prevents extreme outliers from dominating while preserving rank order."
    },
    {
        "enhanced_template": "trade_when(greater(rank(winsorize(ts_backfill({sales_rev_turn},63), std=4)), 0.6), group_zscore(winsorize((ts_backfill({disclosed_intangibles},63) + ts_backfill({goodwill},63)) / ts_backfill({tot_asset},63), std=4), sector), 0)",
        "idea": "Intangible intensity signal conditioned on high asset productivity (top 40% turnover only). Filters out low-turnover value traps and focuses on efficient knowledge-capital deployers within sector peers."
    },
    {
        "enhanced_template": "ts_zscore(ts_delta(winsorize((ts_backfill({disclosed_intangibles},126) + ts_backfill({goodwill},126)) / ts_backfill({tot_asset},126), std=4), 63), 252) * s_log_1p(group_zscore(winsorize(ts_backfill({sales_rev_turn},21), std=4), sector))",
        "idea": "Time-series momentum in intangible capital intensity (quarterly change) scaled by recent productivity. Captures corporate investment shifts into knowledge assets while compressing extreme productivity outliers via symmetric log transform."
    },
    {
        "enhanced_template": "ts_regression(winsorize((ts_backfill({disclosed_intangibles},63) + ts_backfill({goodwill},63)) / ts_backfill({tot_asset},63) * ts_backfill({sales_rev_turn},63), std=4), group_mean(winsorize((ts_backfill({disclosed_intangibles},63) + ts_backfill({goodwill},63)) / ts_backfill({tot_asset},63) * ts_backfill({sales_rev_turn},63), std=4), log(ts_mean(ts_backfill({close},1),21)), sector), 252, rettype=0)",
        "idea": "CAPM-style residual extracting idiosyncratic intangible efficiency unexplained by size-weighted sector averages. Strips common capital-structure exposure to isolate firm-specific capital allocation skill."
    },
    {
        "enhanced_template": "bucket(rank(group_zscore(winsorize((ts_backfill({disclosed_intangibles},63) + ts_backfill({goodwill},63)) / ts_backfill({tot_asset},63), std=4) * winsorize(ts_backfill({sales_rev_turn},63), std=4), sector)), '0,1,0.2')",
        "idea": "Quintile bucketing of sector-adjusted intangible productivity creating discrete long-short portfolios. Treats middle quintiles as noise (neutral weight), isolating extreme efficient vs inefficient capital allocators."
    }
]