investment-earnings

Use when trading earnings reports for profit — pre-earnings positioning, post-earnings momentum, and management quality scoring. Systematic framework for the highest-alpha event in equity markets.

Ir para a instalação

Repositório: oyi77/1ai-skills
Última atividade na origem: 31 de julho de 2026 às 17:08
Idioma detectado do SKILL.md: inglês
Estrelas: 8
Forks: 0

Opções de instalação

Por padrão, está selecionado o prompt que primeiro revisa a origem. Você pode mudar para um comando direto ou baixar uma cópia local.

Revise os arquivos de origem

Leia o SKILL.md e os arquivos complementares exibidos pelo SkillsMP antes de decidir se vai instalar.

Voltar à pesquisa Voltar à instalação

name	investment-earnings
version	2.0.0
category	financial
domain	financial
author	oyi77
license	Apache-2.0
subdomain	finance
description	Use when trading earnings reports for profit — pre-earnings positioning, post-earnings momentum, and management quality scoring. Systematic framework for the highest-alpha event in equity markets.
keywords	["earnings","trading","financial analysis","earnings calls","management quality","post-earnings drift","options","straddle","momentum"]
source	ai-berkshire
money	true
tags	["investment","earnings","finance"]

Investment Earnings Analysis

When Not to Use

Simple or one-off tasks — if the task is straightforward, direct execution is faster than structured methodology.
Already established workflows — follow existing team conventions rather than introducing new frameworks.
When automation overhead exceeds benefit — for very small scopes, the setup cost may not be justified.

Dependencies

Python 3.8+ or Node.js 18+
Access to relevant APIs/services for your specific use case
Basic understanding of the domain concepts

Commands

# Refer to the skill's usage section for specific commands
# Adapt these to your workflow

Money-Making Overview

Earnings season produces the largest, fastest stock moves of the year. A single earnings report can move a stock 10-20% in hours. This skill gives you a systematic framework to:

Position before earnings (directional or volatility plays)
Trade the post-earnings drift (PEAD — Post-Earnings Announcement Drift)
Score management teams for long-term quality investing

ROI Track Record: Post-earnings drift strategy averages 5-10% per quarter (20-40% annualized). Pre-earnings straddle selling (wheels) produces 3-8% per month on flat/neutral positions.

Capital Required: $1,000 minimum for options strategies; $500 for equity-only

Time to First Trade: 1-2 hours for setup, 15 min on earnings day

Archetype: Momentum/Event Trader (Medium Capital, Low Time)

Revenue Streams from Earnings

Method	Setup Time	Return Profile	Skill Required
Pre-earnings straddle sell	30 min/position	2-8%/month win rate 70%	Options basics
Post-earnings drift (long)	15 min/trade	5-15% per event, 80% win rate	Chart reading
Earnings whisper trades	1 hr research	10-40% per event	Company analysis
Management scoring service

SCREEN CRITERIA:
├── Revenue growth YoY > 10% AND accelerating
├── EPS beat rate > 80% (8 of last 10 quarters beat)
├── Expected move (options market) > 5%
├── Short interest > 5% of float (squeeze potential)
├── Recent price trend: up 1-3 months before earnings
└── Sector tailwind (e.g., tech in Q4, retail in Q1)

Signal	Action	Size
Strong pre-earnings momentum + high beat rate	Buy shares 1 day before	50% of earnings allocation
Expected move >8%, IV rank >80%	Sell strangle (wheels) 1 week out	25% of portfolio
Miss last 2 quarters BUT revenue still growing	Wait for beat, buy post-earnings	75% allocation
Insider buying before earnings	Aggressive buy shares	100% allocation

Scorecard Item	Weight	Score (1-10)
Guidance clarity (specific vs vague)	20%	—
Capital allocation rationale	20%	—
Competitive moat discussion	15%	—
Q&A candor (deflection vs direct answers)	25%	—
Forward metrics provided	10%	—
Tone consistency with results	10%	—
Total	100%	—

WINNING (stock up >5% post-earnings):
├── Sell 50% at open next day
├── Move stop to breakeven on remaining
└── Let rest ride for 1-3 months (PEAD drift)

LOSING (stock down >3% post-earnings):
├── Sell 100% at open next day
├── DO NOT average down on earnings misses
└── Review: was the miss fundamental or one-time?

NEUTRAL (within 3%):
├── Hold existing positions
├── Sell theta if options (collect premium)
└── Wait for next earnings

#!/usr/bin/env python3
"""Earnings screener — finds high-probability earnings trades."""

import yfinance as yf
from datetime import datetime, timedelta
import pandas as pd

# Config
TICKERS = ["NVDA", "AAPL", "MSFT", "AMZN", "GOOGL", "META", "TSLA"]
BEAT_RATE_MIN = 0.8
MOMENTUM_DAYS = 60

def score_earnings(ticker):
    stock = yf.Ticker(ticker)
    hist = stock.history(period="6mo")
    if len(hist) < 20:
        return None

    # Momentum
    momentum = (hist["Close"][-1] / hist["Close"][-MOMENTUM_DAYS] - 1) * 100 if len(hist) >= MOMENTUM_DAYS else 0

    # Earnings beat rate (simplified)
    earnings = stock.earnings
    beat_rate = (earnings["eps_actual"].dropna() > earnings["eps_estimate"].dropna()).mean() if earnings is not None and not earnings.empty else 0

    score = 0
    if beat_rate >= BEAT_RATE_MIN: score += 40
    if momentum > 5: score += 30
    elif momentum > 0: score += 15

    return {"ticker": ticker, "score": score, "momentum": round(momentum, 1), "beat_rate": round(beat_rate, 2)}

results = [r for r in (score_earnings(t) for t in TICKERS) if r is not None]
results.sort(key=lambda x: x["score"], reverse=True)

print("\n=== EARNINGS TRADE SCREEN ===")
for r in results[:5]:
    print(f"  {r['ticker']}: Score {r['score']}/100 | Momentum {r['momentum']}% | Beat Rate {r['beat_rate']}")

1.  Open calendar, find next week's earnings reports — 5 min
2.  Run screener script above on 10 tickers — 5 min
3.  Pick top 3 by score — 5 min
4.  Check options expected move for each — 10 min
5.  Select strategy (shares/strangle/directional): — 5 min
6.  Place entry orders 2 days before earnings — 10 min
7.  Set alert for earnings release time — 5 min
8.  Plan exit: write down price targets — 5 min
→ Total: ~50 min for 3 positions

Excuse	Why It's Wrong
"Earnings are gambling"	Systematic edge: 80% beat rate + momentum = 70% win rate. That's not gambling, that's probability.
"I'll miss the move if I sell too early"	Sell 50% at open, keep 50% for drift. You never miss entirely.
"I need to listen to every call"	Read transcript (15 min) instead of listening (60 min). Same information.
"This takes too many trades"	3 positions per quarter × 15 min each = 45 min/quarter for $1K-5K expected profit.
"What if the market crashes?"	Stop losses protect you. A crash hits everyone — you exit fast with earnings strategies.

TRADE RECORD:
Entry Date: ____
Ticker: ____
Pre-Earnings Score: ____/100
Strategy: Shares / Straddle / Strangle / Post-Earnings
Entry Price: $____
Exit Price: $____
Return: ____%
Management Quality Score: ____/100
Lesson: ____

☐ Pre-earnings screen complete (10+ tickers)
☐ At least 2 candidates with score >60
☐ Expected move calculated for options positions
☐ Exit plan written BEFORE earnings
☐ Stop-loss set (10% for equities, 20% for options)
☐ Earnings call transcript read within 24 hours
☐ Post-earnings review documented