Entry Signals

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

from optopsy import long_calls, apply_signal, rsi_below, sustained, signal, day_of_week

# Enter only when RSI(14) is below 30
entry_dates = apply_signal(data, rsi_below(14, 30))
results = long_calls(data, entry_dates=entry_dates)

# Require RSI below 30 for 5 consecutive days
entry_dates = apply_signal(data, sustained(rsi_below(14, 30), days=5))
results = long_calls(data, entry_dates=entry_dates)

# Compose signals with & and |
sig = signal(rsi_below(14, 30)) & signal(day_of_week(3))  # Oversold + Thursday
entry_dates = apply_signal(data, sig)
results = long_calls(data, entry_dates=entry_dates)

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.

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
from optopsy import apply_signal, rsi_below, rsi_above

entry_dates = apply_signal(data, rsi_below(period=14, threshold=30))
exit_dates = apply_signal(data, rsi_above(period=14, threshold=70))
results = op.long_calls(data, entry_dates=entry_dates, exit_dates=exit_dates)

SMA - trend filter

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

from optopsy import apply_signal, sma_above

entry_dates = apply_signal(data, sma_above(period=50))
results = op.short_puts(data, entry_dates=entry_dates)

MACD - enter on bullish crossover

from optopsy import apply_signal, macd_cross_above

entry_dates = apply_signal(data, macd_cross_above(fast=12, slow=26, signal_period=9))
results = op.long_call_spread(data, entry_dates=entry_dates)

Stochastic - oversold entry

from optopsy import apply_signal, stoch_below

entry_dates = apply_signal(data, stoch_below(k_period=14, d_period=3, threshold=20))
results = op.long_calls(data, entry_dates=entry_dates)

Bollinger Bands - mean reversion

Enter when price dips below the lower band:

from optopsy import apply_signal, bb_below_lower

entry_dates = apply_signal(data, bb_below_lower(length=20, std=2.0))
results = op.long_puts(data, entry_dates=entry_dates)

EMA Crossover - golden cross

Fast EMA crosses above slow EMA:

from optopsy import apply_signal, ema_cross_above

entry_dates = apply_signal(data, ema_cross_above(fast=10, slow=50))
results = op.long_calls(data, entry_dates=entry_dates)

ADX - trend strength filter

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

from optopsy import apply_signal, adx_above, signal, ema_cross_above

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

Supertrend - trend direction

Enter when Supertrend flips bullish:

from optopsy import apply_signal, supertrend_buy

entry_dates = apply_signal(data, supertrend_buy(period=7, multiplier=3.0))
results = op.long_call_spread(data, entry_dates=entry_dates)

ATR - low-volatility regime filter

Only sell premium in low-volatility regimes:

from optopsy import apply_signal, atr_below

entry_dates = apply_signal(data, atr_below(period=14, multiplier=0.75))
results = op.iron_condor(data, entry_dates=entry_dates)

Keltner Channel - breakout entry

Enter when price breaks above the upper Keltner Channel:

from optopsy import apply_signal, kc_above_upper

entry_dates = apply_signal(data, kc_above_upper(length=20, scalar=1.5))
results = op.long_calls(data, entry_dates=entry_dates)

Squeeze - volatility compression

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

from optopsy import apply_signal, squeeze_off

entry_dates = apply_signal(data, squeeze_off())
results = op.long_straddles(data, entry_dates=entry_dates)

Volume - MFI oversold

Enter when Money Flow Index indicates oversold conditions:

from optopsy import apply_signal, mfi_below

entry_dates = apply_signal(stock_df, mfi_below(period=14, threshold=20))
results = op.long_calls(data, entry_dates=entry_dates)

Calendar - restrict entries to specific days

from optopsy import apply_signal, day_of_week

# Enter only on Mondays and Fridays
entry_dates = apply_signal(data, day_of_week(0, 4))
results = op.short_straddles(data, entry_dates=entry_dates)

Combining Multiple Signals

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

from optopsy import apply_signal, signal, rsi_below, sma_above, atr_below, day_of_week
from optopsy import and_signals, or_signals

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

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

# OR: enter when EITHER condition fires
from optopsy import macd_cross_above, bb_below_lower
entry = or_signals(macd_cross_above(), bb_below_lower())
entry_dates = apply_signal(data, entry)
results = op.long_call_spread(data, entry_dates=entry_dates)

Sustained Signals

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

from optopsy import apply_signal, sustained, rsi_below, bb_below_lower

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

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

Using Stock OHLCV Data

By default, signals compute indicators from the option chain's underlying_price column. For more accurate TA signals (especially those needing high/low/volume data), use apply_signal on a separate stock OHLCV DataFrame and pass the result as entry_dates:

import pandas as pd
import optopsy as op
from optopsy import apply_signal, adx_above, supertrend_buy, signal

# Load OHLCV stock data (must have: underlying_symbol, quote_date, close;
# optional: open, high, low, volume)
stock_df = pd.read_csv("SPX_daily_ohlcv.csv", parse_dates=["quote_date"])

# Compute entry dates from stock data using real high/low for trend signals
entry = signal(adx_above(period=14, threshold=25)) & signal(supertrend_buy())
entry_dates = apply_signal(stock_df, entry)

# Pass pre-computed dates to the strategy
results = op.long_straddles(data, 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 underlying_price 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.

Sell premium when IV is elevated

from optopsy import apply_signal, iv_rank_above

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

Buy options when IV is cheap

from optopsy import apply_signal, iv_rank_below

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

# Pass to any strategy
results = op.long_calls(data, 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.apply_signal(my_signals, combined)
results = op.long_calls(data, 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
from optopsy import apply_signal, signal, rsi_below

# Custom: only enter when underlying price is above 4000
def price_above_4000(data):
    return data["underlying_price"] > 4000

entry_dates = apply_signal(data, price_above_4000)
results = op.iron_condor(data, entry_dates=entry_dates)

# Combine custom signals with built-in ones
entry = signal(price_above_4000) & signal(rsi_below(14, 30))
entry_dates = apply_signal(data, entry)
results = op.long_calls(data, entry_dates=entry_dates)