Entry Signals

Filter strategy entries using 80+ technical analysis signals powered by pandas-ta-classic. Use signal_dates to compute valid dates from stock price data, then pass them as entry_dates or exit_dates to any strategy.

import optopsy as op

# Load stock and options data separately
stocks = op.load_cached_stocks("SPY")
options = op.load_cached_options("SPY")

# Enter only when RSI(14) is below 30
entry_dates = op.signal_dates(stocks, op.rsi_below(14, 30))
results = op.long_calls(options, entry_dates=entry_dates)

# Require RSI below 30 for 5 consecutive days
entry_dates = op.signal_dates(stocks, op.sustained(op.rsi_below(14, 30), days=5))
results = op.long_calls(options, entry_dates=entry_dates)

# Compose signals with & and |
sig = op.signal(op.rsi_below(14, 30)) & op.signal(op.day_of_week(3))  # Oversold + Thursday
entry_dates = op.signal_dates(stocks, sig)
results = op.long_calls(options, entry_dates=entry_dates)

How It Works

Signals are computed on stock price data (OHLCV), not options data. The flow is:

1. Load stock data — via load_cached_stocks() or your own DataFrame
2. Build signals — run signal functions on stock data to get entry/exit dates
3. Run strategy — pass the dates to a strategy along with options data

# Stock data needs: underlying_symbol, quote_date, close
# Optional: open, high, low, volume (for OHLCV signals)
stocks = op.load_cached_stocks("SPY")

# Or bring your own DataFrame
stocks = pd.DataFrame({
    "underlying_symbol": "SPY",
    "quote_date": my_dates,
    "close": my_prices,
})

Available Signals

Momentum

Signal	Description	Default Parameters
rsi_below, rsi_above	RSI threshold (oversold / overbought)	period=14, threshold=30 / 70
macd_cross_above, macd_cross_below	MACD / signal line crossover	fast=12, slow=26, signal_period=9
stoch_below, stoch_above	Stochastic %K threshold	k_period=14, d_period=3, threshold=20 / 80
stochrsi_below, stochrsi_above	Stochastic RSI %K threshold	period=14, rsi_period=14, k_smooth=3, d_smooth=3, threshold=20 / 80
willr_below, willr_above	Williams %R threshold	period=14, threshold=-80 / -20
cci_below, cci_above	Commodity Channel Index threshold	period=20, threshold=-100 / 100
roc_above, roc_below	Rate of Change threshold	period=10, threshold=0
ppo_cross_above, ppo_cross_below	Percentage Price Oscillator crossover	fast=12, slow=26, signal_period=9
tsi_cross_above, tsi_cross_below	True Strength Index crossover	long=25, short=13, signal_period=13
cmo_above, cmo_below	Chande Momentum Oscillator threshold	period=14, threshold=50 / -50
uo_above, uo_below	Ultimate Oscillator threshold	fast=7, medium=14, slow=28, threshold=70 / 30
squeeze_on, squeeze_off	Squeeze (BB inside/outside KC)	bb_length=20, bb_std=2.0, kc_length=20, kc_scalar=1.5
ao_above, ao_below	Awesome Oscillator threshold	fast=5, slow=34, threshold=0
smi_cross_above, smi_cross_below	Stochastic Momentum Index crossover	fast=5, slow=20, signal_period=5
kst_cross_above, kst_cross_below	Know Sure Thing crossover	(uses pandas-ta defaults)
fisher_cross_above, fisher_cross_below	Fisher Transform crossover	period=9

Overlap (Moving Averages)

Signal	Description	Default Parameters
sma_above, sma_below	Price vs. Simple Moving Average	period=20
ema_cross_above, ema_cross_below	EMA fast/slow crossover	fast=10, slow=50
dema_cross_above, dema_cross_below	Double EMA crossover	fast=10, slow=50
tema_cross_above, tema_cross_below	Triple EMA crossover	fast=10, slow=50
hma_cross_above, hma_cross_below	Hull MA crossover	fast=10, slow=50
kama_cross_above, kama_cross_below	Kaufman Adaptive MA crossover	fast=10, slow=50
wma_cross_above, wma_cross_below	Weighted MA crossover	fast=10, slow=50
zlma_cross_above, zlma_cross_below	Zero-Lag MA crossover	fast=10, slow=50
alma_cross_above, alma_cross_below	Arnaud Legoux MA crossover	fast=10, slow=50

Volatility

Signal	Description	Default Parameters
atr_above, atr_below	ATR vs. median ATR regime	period=14, multiplier=1.0
bb_above_upper, bb_below_lower	Price vs. Bollinger Bands	length=20, std=2.0
kc_above_upper, kc_below_lower	Price vs. Keltner Channel	length=20, scalar=1.5
donchian_above_upper, donchian_below_lower	Price vs. Donchian Channel	lower_length=20, upper_length=20
natr_above, natr_below	Normalized ATR threshold (% of price)	period=14, threshold=2.0 / 1.0
massi_above, massi_below	Mass Index threshold (reversal signal)	fast=9, slow=25, threshold=27 / 26.5

Trend

Signal	Description	Default Parameters
adx_above, adx_below	Average Directional Index threshold	period=14, threshold=25 / 20
aroon_cross_above, aroon_cross_below	Aroon Up/Down crossover	period=25
supertrend_buy, supertrend_sell	Supertrend direction flip	period=7, multiplier=3.0
psar_buy, psar_sell	Parabolic SAR direction flip	af0=0.02, af=0.02, max_af=0.2
chop_above, chop_below	Choppiness Index threshold	period=14, threshold=61.8 / 38.2
vhf_above, vhf_below	Vertical Horizontal Filter threshold	period=28, threshold=0.4

Volume

Require OHLCV data with a volume column.

Signal	Description	Default Parameters
mfi_above, mfi_below	Money Flow Index threshold	period=14, threshold=80 / 20
obv_cross_above_sma, obv_cross_below_sma	OBV vs. its SMA crossover	sma_period=20
cmf_above, cmf_below	Chaikin Money Flow threshold	period=20, threshold=0.05 / -0.05
ad_cross_above_sma, ad_cross_below_sma	A/D Line vs. its SMA crossover	sma_period=20

IV Rank

Require options data with an implied_volatility column. IV rank signals are the exception — they run on options data, not stock data.

Signal	Description	Default Parameters
iv_rank_above, iv_rank_below	IV rank percentile threshold	threshold=0.5, window=252

Calendar

Signal	Description	Default Parameters
day_of_week	Restrict to specific weekdays	Days: 0=Mon ... 4=Fri

Custom

Signal	Description	Default Parameters
custom_signal	Boolean flag from a DataFrame column	flag_col='signal'

Combinators

Function	Description
sustained(signal, days=5)	Require signal True for N consecutive bars
and_signals(sig1, sig2, ...)	All signals must be True
or_signals(sig1, sig2, ...)	At least one signal must be True
Signal class with & / \|	Fluent operator chaining

Signal Examples

RSI - enter on oversold, exit on overbought

import optopsy as op

stocks = op.load_cached_stocks("SPY")
options = op.load_cached_options("SPY")

entry_dates = op.signal_dates(stocks, op.rsi_below(period=14, threshold=30))
exit_dates = op.signal_dates(stocks, op.rsi_above(period=14, threshold=70))
results = op.long_calls(options, entry_dates=entry_dates, exit_dates=exit_dates)

SMA - trend filter

Only enter when price is above its 50-day moving average:

entry_dates = op.signal_dates(stocks, op.sma_above(period=50))
results = op.short_puts(options, entry_dates=entry_dates)

MACD - enter on bullish crossover

entry_dates = op.signal_dates(stocks, op.macd_cross_above(fast=12, slow=26, signal_period=9))
results = op.long_call_spread(options, entry_dates=entry_dates)

Stochastic - oversold entry

entry_dates = op.signal_dates(stocks, op.stoch_below(k_period=14, d_period=3, threshold=20))
results = op.long_calls(options, entry_dates=entry_dates)

Bollinger Bands - mean reversion

Enter when price dips below the lower band:

entry_dates = op.signal_dates(stocks, op.bb_below_lower(length=20, std=2.0))
results = op.long_puts(options, entry_dates=entry_dates)

EMA Crossover - golden cross

Fast EMA crosses above slow EMA:

entry_dates = op.signal_dates(stocks, op.ema_cross_above(fast=10, slow=50))
results = op.long_calls(options, entry_dates=entry_dates)

ADX - trend strength filter

Only enter trend-following strategies when ADX confirms a strong trend:

entry = op.signal(op.adx_above(period=14, threshold=25)) & op.signal(op.ema_cross_above(10, 50))
entry_dates = op.signal_dates(stocks, entry)
results = op.long_calls(options, entry_dates=entry_dates)

Supertrend - trend direction

Enter when Supertrend flips bullish:

entry_dates = op.signal_dates(stocks, op.supertrend_buy(period=7, multiplier=3.0))
results = op.long_call_spread(options, entry_dates=entry_dates)

ATR - low-volatility regime filter

Only sell premium in low-volatility regimes:

entry_dates = op.signal_dates(stocks, op.atr_below(period=14, multiplier=0.75))
results = op.iron_condor(options, entry_dates=entry_dates)

Keltner Channel - breakout entry

Enter when price breaks above the upper Keltner Channel:

entry_dates = op.signal_dates(stocks, op.kc_above_upper(length=20, scalar=1.5))
results = op.long_calls(options, entry_dates=entry_dates)

Squeeze - volatility compression

Enter when the squeeze releases (Bollinger Bands expand outside Keltner Channels):

entry_dates = op.signal_dates(stocks, op.squeeze_off())
results = op.long_straddles(options, entry_dates=entry_dates)

Volume - MFI oversold

Enter when Money Flow Index indicates oversold conditions:

entry_dates = op.signal_dates(stocks, op.mfi_below(period=14, threshold=20))
results = op.long_calls(options, entry_dates=entry_dates)

Calendar - restrict entries to specific days

# Enter only on Mondays and Fridays
entry_dates = op.signal_dates(stocks, op.day_of_week(0, 4))
results = op.short_straddles(options, entry_dates=entry_dates)

Combining Multiple Signals

Use the Signal class with & (AND) and | (OR) operators, or the functional and_signals / or_signals helpers:

import optopsy as op

stocks = op.load_cached_stocks("SPY")
options = op.load_cached_options("SPY")

# Fluent API: oversold + uptrend + low volatility
entry = op.signal(op.rsi_below(14, 30)) & op.signal(op.sma_above(50)) & op.signal(op.atr_below(14, 0.75))
entry_dates = op.signal_dates(stocks, entry)
results = op.long_calls(options, entry_dates=entry_dates)

# Functional API: same logic
entry = op.and_signals(op.rsi_below(14, 30), op.sma_above(50), op.atr_below(14, 0.75))
entry_dates = op.signal_dates(stocks, entry)
results = op.long_calls(options, entry_dates=entry_dates)

# OR: enter when EITHER condition fires
entry = op.or_signals(op.macd_cross_above(), op.bb_below_lower())
entry_dates = op.signal_dates(stocks, entry)
results = op.long_call_spread(options, entry_dates=entry_dates)

Sustained Signals

Require a condition to persist for multiple consecutive days before triggering:

stocks = op.load_cached_stocks("SPY")
options = op.load_cached_options("SPY")

# RSI must stay below 30 for 5 straight days
entry_dates = op.signal_dates(stocks, op.sustained(op.rsi_below(14, 30), days=5))
results = op.long_calls(options, entry_dates=entry_dates)

# Bollinger Band breach sustained for 3 days
entry_dates = op.signal_dates(stocks, op.sustained(op.bb_below_lower(20, 2.0), days=3))
results = op.long_puts(options, entry_dates=entry_dates)

Using Your Own Stock Data

You can use any DataFrame with underlying_symbol, quote_date, and close columns. Signals that need high/low/volume will use those columns if present, falling back to close if not.

import pandas as pd
import optopsy as op

# Load your own OHLCV data
stock_df = pd.read_csv("SPY_daily.csv", parse_dates=["quote_date"])

# Use it directly with signal_dates
entry = op.signal(op.adx_above(period=14, threshold=25)) & op.signal(op.supertrend_buy())
entry_dates = op.signal_dates(stock_df, entry)

options = op.load_cached_options("SPY")
results = op.long_straddles(options, entry_dates=entry_dates)

Tip

Signals that use high/low data (Stochastic, Williams %R, CCI, ATR, Keltner, Donchian, ADX, Aroon, Supertrend, PSAR, Choppiness, MFI, OBV, CMF, A/D) will fall back to using close as a proxy if high and low columns are not present. For best accuracy, provide real OHLCV data.

IV Rank - volatility regime filter

IV Rank measures where current implied volatility sits relative to its trailing range. Requires an implied_volatility column in your options data. This is the one signal type that runs on options data, not stock data.

Sell premium when IV is elevated

options = op.load_cached_options("SPY")

# Enter short strategies when IV rank is above 50th percentile (1-year lookback)
entry_dates = op.signal_dates(options, op.iv_rank_above(threshold=0.5, window=252))
results = op.iron_condor(options, entry_dates=entry_dates)

Buy options when IV is cheap

# Enter long strategies when IV rank is in the bottom 30%
entry_dates = op.signal_dates(options, op.iv_rank_below(threshold=0.3, window=252))
results = op.long_straddles(options, entry_dates=entry_dates)

Note

IV rank signals work directly on options chain data (not stock OHLCV). The signal computes ATM implied volatility per quote date and ranks it over the trailing window.

Custom Signal from DataFrame

Use custom_signal() to create a signal from any DataFrame with a boolean flag column. This lets you define arbitrary entry/exit conditions using external data sources, model outputs, or manual annotations.

import pandas as pd
import optopsy as op

# Any DataFrame with dates and a boolean flag works
my_signals = pd.DataFrame({
    "underlying_symbol": ["SPY", "SPY", "SPY"],
    "quote_date": ["2018-01-02", "2018-01-03", "2018-01-04"],
    "buy": [True, False, True],
})

# Create a signal from the DataFrame
sig = op.custom_signal(my_signals, flag_col="buy")
entry_dates = op.signal_dates(my_signals, sig)

# Pass to any strategy
options = op.load_cached_options("SPY")
results = op.long_calls(options, entry_dates=entry_dates, raw=True)

The DataFrame must contain underlying_symbol, quote_date, and the flag column. Integer (0/1), nullable boolean, and NaN values are all handled — NaN is treated as False.

Composing custom signals with built-in signals

import optopsy as op

sig = op.custom_signal(my_signals, flag_col="buy")

# Combine with built-in signals using & and |
combined = op.signal(sig) & op.signal(op.day_of_week(1))
entry_dates = op.signal_dates(my_signals, combined)

options = op.load_cached_options("SPY")
results = op.long_calls(options, entry_dates=entry_dates)

Custom Signal Functions

Any function matching the signature (pd.DataFrame) -> pd.Series[bool] can be used as a signal:

import optopsy as op

stocks = op.load_cached_stocks("SPY")
options = op.load_cached_options("SPY")

# Custom: only enter when close price is above 400
def price_above_400(data):
    return data["close"] > 400

entry_dates = op.signal_dates(stocks, price_above_400)
results = op.iron_condor(options, entry_dates=entry_dates)

# Combine custom signals with built-in ones
entry = op.signal(price_above_400) & op.signal(op.rsi_below(14, 30))
entry_dates = op.signal_dates(stocks, entry)
results = op.long_calls(options, entry_dates=entry_dates)