alphaear-strategy

name	alphaear-strategy
description	Score trading setups using AlphaEar multi-factor analysis (momentum, volume, sentiment). Use when evaluating entry/exit signals.
domain	trading
author	oyi77
license	Apache-2.0
subdomain	trading
tags	["algorithms","alphaear","markets","money","strategy","trading"]
version	2.0.0

Alphaear Strategy

When to Use

Trigger phrases:

• "alphaear strategy"
• "Help me with alphaear strategy"

Use cases:

• When the task matches this skill's domain expertise

When NOT to use:

• For tasks outside this skill's scope

analysis = alphaear_analyze("NVDA")

Output includes:

- News aggregation with sentiment

- Social media trend analysis

- Options flow anomalies

- Kronos price prediction

- Composite signal score

if analysis.signal_score > 75: position_size = portfolio_value * 0.05 # 5% max entry = current_price stop = entry * 0.95 # 5% stop target = entry * 1.15 # 15% target

### Example 2: Signal Monitoring

```python
# Monitor multiple positions
portfolio = ["AAPL", "TSLA", "NVDA", "AMD"]
signals = {}

for ticker in portfolio:
    signals[ticker] = alphaear_analyze(ticker)
    
# Alert on signal degradation
for ticker, signal in signals.items():
    if signal.evolution == "WEAKEN":
        alert(f"{ticker}: Signal weakening, review position")
    elif signal.evolution == "FALSIFY":
        alert(f"{ticker}: Thesis invalidated, consider exit")

Example 3: Event-Driven Setup

# Pre-earnings analysis
ticker = "AMZN"
catalyst_date = get_next_earnings_date(ticker)
days_to_catalyst = (catalyst_date - today).days

if days_to_catalyst <= 7:
    setup = alphaear_analyze(
        ticker,
        focus="catalyst_setup",
        include_options=True
    )
    
    if setup.options_signal == "unusual_call_activity":
        # Market positioning bullish
        direction = "LONG"
        structure = "call_spread"

---

Load references/trading-checklist.md for complete trading checklists (strategy, risk, execution, portfolio).

When NOT to Use

• When you cannot afford to lose the capital at risk
• For instruments you do not understand
• When emotional state impairs judgment (revenge trading, FOMO)

Overview

Alphaear Strategy provides market analysis capabilities with risk management.

Workflow

1. Research — Analyze market conditions and opportunities
2. Plan — Define entry, exit, and position sizing
3. Execute — Place trades with proper order types
4. Monitor — Track positions and market changes
5. Manage risk — Apply stop-losses and hedging
6. Review — Post-trade analysis and journaling

Risk Management

• Never risk more than 1-2% of portfolio per trade
• Set stop-loss before entering any position
• Diversify across uncorrelated assets
• Size positions based on volatility (ATR)
• Have a maximum daily loss limit

Key Metrics

• Win rate and profit factor
• Sharpe ratio and max drawdown
• Average risk-reward ratio
• Expectancy per trade
• Correlation to benchmark

Discipline Rules

• Follow your trading plan — no impulsive trades
• Cut losses short, let winners run
• Review every trade in your journal
• Never revenge trade after a loss
• Take breaks after consecutive losses

Anti-Rationalization Table

Rationalization	Reality
"I will cut losses later"	Later never comes. Set stop-losses before entering any trade.
"This time is different"	It never is. Follow your strategy, not your emotions.
"I do not need to journal"	Journaling reveals patterns in your behavior. Track every trade.

Money-Making Overview

Turn multi-factor signal scoring into consistent trading edge. Each signal_score > 75 setup targets 3:1 reward-to-risk, with 1% capital at risk per trade ($50 on $5K account, $100 on $10K). The alphaear framework combines momentum, volume divergence, and sentiment (news + options flow) into a composite score — when all three align above threshold, the edge is statistically significant for swing and event-driven trades.

Revenue Streams

1. Signal-Based Swing Trading ($300–$3,000/month)

• Score momentum/volume/sentiment signals daily on a 10-ticker watchlist
• Enter only when composite score > 75 with bullish alignment across all three factors
• Position size: 1% account risk per trade, 5% stop-loss, 15% profit target (3:1 R:R)
• 20–30 trades/month at 55–65% win rate generates consistent P&L

2. Options Flow & Earnings Trading ($500–$5,000/month)

• Monitor unusual options activity (block trades, sweep orders, put/call ratio divergence)
• Pre-earnings catalyst setups with 7-day horizon; structure as call/put spreads
• Combine options flow signal with momentum score for higher probability entries
• Scale size for defined-risk spreads (max loss capped at 1% account value)

3. Signal Subscription / API ($10–$100K/month at scale)

• Package composite alphaear scores as a daily email / webhook / REST API
• Tiered pricing: $50/month retail, $500/month pro (full ticker coverage), $5K/month institutional (API access + raw data feeds)
• Target 200–2,000 subscribers via quant Twitter/X, trading communities, and affiliate partnerships
• Recurring SaaS revenue with zero marginal cost per additional subscriber

Money Management (Hard Rules)

Rule	Value	Rationale
Risk per trade	1% of account	Survive 20 consecutive losses at ~66% drawdown
Stop-loss	5% below entry	Matches ATR-based volatility bands on daily timeframe
Profit target	15% above entry	3:1 R:R — one winner covers three losers
Max daily loss	3% of account	Hard stop: close all positions, step away
Max concurrent positions	4	Non-correlated tickers only (no sector clustering)
Portfolio at risk cap	4%	4 positions × 1% each = max portfolio exposure

First Action in 60 Minutes

Run this script to score live signals for any ticker. It computes momentum (ROC + RSI), volume (ratio vs 20-day average), and sentiment (price vs moving average cross) from yfinance data, then prints a composite score with entry, stop, and target levels.

pip install yfinance pandas numpy 2>/dev/null

#!/usr/bin/env python3
"""alphaear_first_trade.py — Score a ticker and get entry/exit levels."""
import sys, yfinance as yf, pandas as pd, numpy as np

def alphaear_score(ticker: str):
    df = yf.download(ticker, period="3mo", interval="1d", progress=False)
    if df.empty or len(df) < 22:
        return {"error": f"Insufficient data for {ticker}"}
    c = df["Close"] if "Close" in df.columns else df["Adj Close"]
    v = df["Volume"]
    price = float(c.iloc[-1])
    # Momentum: 14-day ROC + RSI approximation
    roc = (float(c.iloc[-1]) / float(c.iloc[-14]) - 1) * 100
    delta = c.diff()
    gain = delta.where(delta > 0, 0.0).rolling(14).mean()
    loss = (-delta.where(delta < 0, 0.0)).rolling(14).mean()
    rs = gain / loss.replace(0, np.nan)
    rsi = 100 - (100 / (1 + rs))
    rsi_val = float(rsi.iloc[-1])
    
    vol_ratio = (v.iloc[-]) / (v.iloc[-:].mean())
    
    sma50 = (c.rolling().mean().iloc[-])
    trend_bull = price > sma50
    
    mom_score = (((roc + ) * , ), ) * 
    vol_score = (((vol_ratio - ) * , ), ) * 
    sent_score = (rsi_val  trend_bull   - rsi_val) * 
    composite = (mom_score + vol_score + sent_score, )
    entry = (price, )
     composite > :
        direction = 
        stop = (entry * , )
        target = (entry * , )
     composite < :
        direction = 
        stop = (entry * , )
        target = (entry * , )
    :
        direction = 
        stop = target = 
     (ticker=ticker, price=entry, composite_score=composite,
                direction=direction, stop_loss=stop, profit_target=target,
                momentum_roc=(roc, ), rsi_14=(rsi_val, ),
                vol_ratio=(vol_ratio, ), above_sma50=trend_bull)

 __name__ == :
    ticker = sys.argv[]  (sys.argv) >   
    r = alphaear_score(ticker)
       r:
        ()
        sys.exit()
    ()
    ()
    ()
    ()
    ()
    ()
    ()
     r[]:
        ()
        ()
        risk = ((r[] - r[]), )
        ()
        ()
        ()

Usage:

python3 alphaear_first_trade.py NVDA
python3 alphaear_first_trade.py AAPL

Output Format

Every alphaear signal call produces a structured result:

{
  "ticker": "NVDA",
  "price": 124.56,
  "composite_score": 82.3,
  "direction": "LONG",
  "stop_loss": 118.33,
  "profit_target": 143.24,
  "momentum_roc": 8.4,
  "rsi_14": 62.5,
  "vol_ratio": 1.85,
  "above_sma50": true
}

Field	Description	Threshold
composite_score	Weighted multi-factor signal (0–100)	> 75 = actionable LONG, < 25 = SHORT
momentum_roc	14-day rate of change %	> 3% confirms trend strength
rsi_14	Relative Strength Index (14)	30–70 range; > 70 overbought, < 30 oversold
vol_ratio	Today's volume / 20-day average	> 1.5 confirms participation
above_sma50	Price above 50-day MA	Bullish when True, bearish when False
stop_loss	5% below/above entry	Hard exit — no exceptions
profit_target	15% above/below entry	Take full or 50% here, trail remainder

For API / subscription output, wrap the same fields in an envelope with timestamp, signal_id, and confidence.

Process

1. Prepare — Gather requirements, verify prerequisites, set up environment
2. Execute — Run alphaear strategy workflow with configured parameters
3. Verify — Validate output meets requirements, document results

Verification

• All steps executed successfully
• Results validated against acceptance criteria
• Error handling tested with edge cases
• Documentation updated with findings