Beta Regime Analysis¶

The Beta Regime module helps identify market risk-on/risk-off conditions by analyzing the relative strength of individual stocks compared to a benchmark (typically SPY).

Concept Overview¶

What is Beta?¶

Beta (β) measures the volatility of a stock relative to a benchmark index:

$$\beta = \frac{\text{Cov}(r_{\text{asset}}, r_{\text{benchmark}})}{\text{Var}(r_{\text{benchmark}})}$$

β > 1: Stock is more volatile than the benchmark (amplifies market moves)
β < 1: Stock is less volatile than the benchmark (dampens market moves)
β ≈ 1: Stock moves roughly in line with the benchmark
β < 0: Stock moves inversely to the benchmark (rare for stocks)

Relative Strength & Regime Detection¶

The regime detection uses a relative strength ratio:

$$RS = \frac{\text{Asset Price}}{\text{Benchmark Price}}$$

This ratio is then compared to its moving average:

Risk-On: RS above its moving average → Asset outperforming benchmark
Risk-Off: RS below its moving average → Asset underperforming benchmark

CLI Commands¶

Screen for Beta Regime¶

# Screen all NASDAQ stocks
stockcharts-beta-regime

# Filter for risk-on stocks only
stockcharts-beta-regime --regime risk-on

# Screen weekly charts against QQQ benchmark
stockcharts-beta-regime --benchmark QQQ --interval 1wk

# Apply price and volume filters
stockcharts-beta-regime --min-price 10 --max-price 100 --min-volume 500000

# Save results to CSV
stockcharts-beta-regime --output results.csv

Plot Beta Regime Chart¶

# Plot a single ticker
stockcharts-plot-beta AAPL

# Compare against QQQ instead of SPY
stockcharts-plot-beta AAPL --benchmark QQQ

# Use weekly interval with custom MA period
stockcharts-plot-beta AAPL --interval 1wk --ma-period 40

# Save chart to file
stockcharts-plot-beta AAPL --output aapl_beta.png

Python API¶

Analyze a Single Ticker¶

from stockcharts.indicators.beta import analyze_beta_regime
from stockcharts.data.fetch import fetch_ohlc

# Fetch data
asset_df = fetch_ohlc("AAPL", lookback="2y")
benchmark_df = fetch_ohlc("SPY", lookback="2y")

# Analyze regime
result = analyze_beta_regime(
    asset_df,
    benchmark_df,
    ma_period=200,
    beta_window=60
)

print(f"Regime: {result['regime']}")
print(f"Relative Strength: {result['rs_current']:.4f}")
print(f"200 MA: {result['rs_ma_current']:.4f}")
print(f"% from MA: {result['pct_from_ma']:.2%}")
print(f"Beta: {result['beta']:.2f}")

Screen Multiple Tickers¶

from stockcharts.screener.beta_regime import screen_beta_regime, save_results_to_csv

# Screen all NASDAQ for risk-on stocks
results = screen_beta_regime(
    benchmark="SPY",
    regime_filter="risk-on",
    min_price=10,
    min_volume=500000,
    verbose=True
)

# Display results
for r in results[:10]:
    print(f"{r.ticker}: {r.regime} (RS: {r.relative_strength:.4f}, β: {r.beta:.2f})")

# Save to CSV
save_results_to_csv(results, "risk_on_stocks.csv")

Compute Raw Beta¶

from stockcharts.indicators.beta import compute_rolling_beta
from stockcharts.data.fetch import fetch_ohlc

asset_df = fetch_ohlc("AAPL", lookback="2y")
benchmark_df = fetch_ohlc("SPY", lookback="2y")

# 60-day rolling beta
beta = compute_rolling_beta(
    asset_df["Close"],
    benchmark_df["Close"],
    window=60
)

print(f"Current 60-day Beta: {beta.iloc[-1]:.2f}")

Parameters¶

Screener Parameters¶

Parameter	Default	Description
`benchmark`	"SPY"	Benchmark ticker (SPY, QQQ, etc.)
`interval`	"1d"	Candle interval: "1d", "1wk", "1mo"
`ma_period`	200	Moving average period for regime detection
`beta_window`	60	Rolling window for beta calculation
`regime_filter`	"all"	Filter: "risk-on", "risk-off", "all"
`min_price`	None	Minimum stock price filter
`max_price`	None	Maximum stock price filter
`min_volume`	None	Minimum average volume filter
`batch_size`	50	Tickers per batch download

Auto-Adjustment for Weekly Interval¶

When using --interval 1wk with ma_period=200, the period automatically adjusts to 40 weeks (equivalent to ~200 trading days).

Trading Applications¶

Risk-On Screening¶

Find stocks that are outperforming the market:

stockcharts-beta-regime --regime risk-on --min-volume 1000000

These stocks are showing relative strength and may continue to outperform.

Risk-Off Screening¶

Find stocks that are underperforming:

stockcharts-beta-regime --regime risk-off --min-price 20

These stocks are showing relative weakness; consider for mean-reversion or avoiding longs.

Combining with Other Signals¶

Use beta regime as a filter with RSI divergence:

from stockcharts.screener.beta_regime import screen_beta_regime
from stockcharts.screener.rsi_divergence import screen_rsi_divergence

# First find risk-off stocks (underperforming)
weak_stocks = screen_beta_regime(regime_filter="risk-off", verbose=False)
weak_tickers = [r.ticker for r in weak_stocks]

# Then check for bullish divergences (potential reversal)
divergences = screen_rsi_divergence(
    tickers=weak_tickers,
    divergence_type="bullish",
    verbose=True
)

Output Fields¶

BetaRegimeResult¶

Field	Type	Description
`ticker`	str	Stock symbol
`company_name`	str	Company name
`benchmark`	str	Benchmark used
`regime`	str	"risk-on" or "risk-off"
`relative_strength`	float	Current RS ratio
`ma_value`	float	RS moving average value
`pct_from_ma`	float	Percentage distance from MA
`beta`	float	Rolling beta value
`close_price`	float	Current close price
`benchmark_price`	float	Current benchmark price
`interval`	str	Time interval used
`ma_period`	int	MA period used
## McGlone Contrarian Sector Analysis

The scripts/sector_regime.py script implements Mike McGlone's contrarian framework for timing equity sector entry.

McGlone's 3-Criteria Buy System¶

Mike McGlone (Bloomberg Intelligence) advocates a contrarian approach:

Beta below 200-day MA (Risk-Off regime) - Sector underperforming SPY
VIX spike to 30-40 - Fear at extreme levels
Market correction ≥ 10% - Prices discounted from 52-week highs

BUY signal = All 3 conditions met (capitulation complete)

Signal Types¶

Signal	Meaning	Action
`BUY`	All 3 conditions met	Enter positions - capitulation complete
`WATCH_BUY`	2/3 conditions met	Prepare to buy, wait for final trigger
`ACCUMULATE`	Risk-off + pullback (5-10%)	Scale in slowly
`WATCH`	Deep risk-off (>10% below MA)	Approaching buy zone
`WAIT`	Risk-off but no capitulation	Patient - no action yet
`HOLD`	Risk-on uptrend	Hold existing positions
`EXTENDED`	Far above MA (>15%)	Risky to chase
`NEUTRAL`	No clear signal	No action

Usage¶

# Analyze 11 S&P 500 GICS sectors
python scripts/sector_regime.py

# Industry sub-sectors (XBI, KRE, XHB, etc.)
python scripts/sector_regime.py --industries

# SmallCap sector ETFs (PSC* series)
python scripts/sector_regime.py --smallcap

# Leveraged Bull/Bear ETFs (2x/3x)
python scripts/sector_regime.py --leveraged

# Sectors + Industries combined
python scripts/sector_regime.py --all

# All 90+ ETFs comprehensive
python scripts/sector_regime.py --comprehensive

Available ETF Categories¶

Category	Count	Examples
S&P 500 GICS Sectors	11	XLK, XLF, XLE, XLV, XLI, etc.
S&P 500 Industries	18	XBI, KRE, XHB, XRT, XOP, etc.
SmallCap Sectors	9	PSCT, PSCF, PSCE, PSCH, etc.
iShares Sectors	4	IGM, IGV, IGE, IYT
Leveraged Bull (2x/3x)	24	TECL, FAS, ERX, LABU, etc.
Leveraged Bear (2x/3x)	18	TECS, FAZ, ERY, LABD, etc.
Thematic	6	PILL, TEXU, etc.

Example Output¶

===============================================================================
MARKET CONDITIONS (McGlone's 3 Criteria)
-------------------------------------------------------------------------------
  [ ] VIX Spike (>=30):     VIX = 20.4 (ELEVATED) -> NO
  [ ] Correction (>=-10%): SPY = $595.23, Drawdown = -1.0% (NEAR_HIGHS) -> NO
  [ ] Risk-Off Regime:   (See sector analysis below)

  MARKET-LEVEL: 0/2 macro conditions met
     WAIT - No capitulation signals yet

===============================================================================
S&P 500 GICS SECTORS - McGlone Contrarian Analysis
===============================================================================

Sector/Industry      ETF   Beta  Regime   %FromMA  Signal       Action
-------------------------------------------------------------------------------
Utilities            XLU    0.45 - risk-off   -4.2% WAIT
Materials            XLB    1.12 - risk-off   -2.8% WAIT
Energy               XLE    1.25 + risk-on    +3.5% NEUTRAL
Technology           XLK    1.35 + risk-on   +12.1% HOLD          
Financials           XLF    1.10 + risk-on   +18.5% EXTENDED      !

Integration with Beta Regime Screener¶

The sector regime script uses the same underlying screen_beta_regime() function with optimized defaults for McGlone's methodology:

Period: 10 years (stable relative strength history)
Interval: Weekly (1wk) with 40-bar MA (≈200 daily bars)
Benchmark: SPY

from stockcharts.screener.beta_regime import screen_beta_regime

# Run the same analysis programmatically
results = screen_beta_regime(
    tickers=["XLK", "XLF", "XLE", "XLV", "XLI"],
    interval="1wk",
    lookback="10y",
    regime_filter="all",
)

for r in results:
    print(f"{r.ticker}: {r.regime} ({r.pct_from_ma:+.1f}%)")