Features Engineering - Market Regime¶

Understanding market regimes is essential for traders and quants to adapt strategies to different market conditions. Identifying whether the market is trending or ranging helps filter signals and optimize risk management.

In this notebook, we will explore market regime detection techniques using the quantreo package.

Tips: Market regime detection is often used as a filter for trend-following or mean-reversion strategies. Combining regimes with volatility or momentum filters can significantly reduce noise and improve model performance.

In [1]:

# Import the Features Engineering Package from Quantreo
import quantreo.features_engineering as fe

# To display the graphics
import matplotlib.pyplot as plt
plt.style.use("seaborn-v0_8")

# Import the Features Engineering Package from Quantreo import quantreo.features_engineering as fe # To display the graphics import matplotlib.pyplot as plt plt.style.use("seaborn-v0_8")

In [2]:

# Import a dataset to test the functions and create new ones easily
from quantreo.datasets import load_generated_ohlcv

df = load_generated_ohlcv()
df = df.loc["2016"]

# Show the data
df

# Import a dataset to test the functions and create new ones easily from quantreo.datasets import load_generated_ohlcv df = load_generated_ohlcv() df = df.loc["2016"] # Show the data df

Out[2]:

	open	high	low	close	volume
time
2016-01-04 00:00:00	104.944241	105.312073	104.929735	105.232289	576.805768
2016-01-04 04:00:00	105.233361	105.252139	105.047564	105.149357	485.696723
2016-01-04 08:00:00	105.159851	105.384745	105.141110	105.330306	403.969745
2016-01-04 12:00:00	105.330306	105.505799	104.894155	104.923404	1436.917324
2016-01-04 16:00:00	104.914147	105.023293	104.913252	105.014347	1177.672605
...	...	...	...	...	...
2016-12-30 04:00:00	103.632257	103.711884	103.495896	103.564574	563.932484
2016-12-30 08:00:00	103.564574	103.629321	103.555581	103.616731	697.707475
2016-12-30 12:00:00	103.615791	103.628165	103.496810	103.515847	1768.926665
2016-12-30 16:00:00	103.515847	103.692243	103.400370	103.422193	921.437054
2016-12-30 20:00:00	103.420285	103.429955	103.195921	103.226868	764.291608

1548 rows × 5 columns

Kama Market Regime¶

The kama_market_regime function generates a market regime indicator based on the relative position of two Kaufman's Adaptive Moving Averages (KAMA): a fast KAMA and a slow KAMA.

This method is inspired by the traditional moving average crossover logic, but with the adaptivity of KAMA smoothing.

Tip: The choice of l1, l2, and l3 parameters for each KAMA influences both trend reactivity and noise filtering. For trend-following strategies, using l1_fast = 50 and l1_slow = 200 is common, but you can adjust them depending on your timeframe.

In [9]:

df["kama_trend"] = fe.market_regime.kama_market_regime(df, "close", l1_fast=50, l2_fast=2, l3_fast=30,
                                                       l1_slow=200, l2_slow=2, l3_slow=30)
df["kama_trend"]

df["kama_trend"] = fe.market_regime.kama_market_regime(df, "close", l1_fast=50, l2_fast=2, l3_fast=30, l1_slow=200, l2_slow=2, l3_slow=30) df["kama_trend"]

Out[9]:

time
2016-01-04 00:00:00   -1
2016-01-04 04:00:00   -1
2016-01-04 08:00:00   -1
2016-01-04 12:00:00   -1
2016-01-04 16:00:00   -1
                      ..
2016-12-30 04:00:00    1
2016-12-30 08:00:00    1
2016-12-30 12:00:00    1
2016-12-30 16:00:00    1
2016-12-30 20:00:00    1
Name: kama_trend, Length: 1548, dtype: int64

In [8]:

# Highlight market regimes using KAMA trend signals
import matplotlib.pyplot as plt

# Prepare data and detect regime shifts
dft = df[["kama_trend", "close"]].dropna().assign(time=lambda x: x.index)
dft["zone_id"] = dft["kama_trend"].ne(dft["kama_trend"].shift()).cumsum()

# Aggregate start, end, and regime label for each zone
zones = dft.groupby('zone_id').agg(
    start=('time', 'first'),
    end=('time', 'last'),
    regime=('kama_trend', 'first')
)

# Plot price with regime background coloring
fig, ax = plt.subplots(figsize=(12, 6))
ax.plot(dft.index, dft['close'], label='Close Price', linewidth=1)

for _, row in zones.iterrows():
    color = 'green' if row['regime'] == 1 else 'red'
    ax.axvspan(row['start'], row['end'], color=color, alpha=0.2)

ax.set_title("Close Price with KAMA Market Regime Highlighting", fontsize=14)
ax.set_xlabel("Time")
ax.set_ylabel("Price")
ax.legend()
plt.tight_layout()
plt.show()

# Highlight market regimes using KAMA trend signals import matplotlib.pyplot as plt # Prepare data and detect regime shifts dft = df[["kama_trend", "close"]].dropna().assign(time=lambda x: x.index) dft["zone_id"] = dft["kama_trend"].ne(dft["kama_trend"].shift()).cumsum() # Aggregate start, end, and regime label for each zone zones = dft.groupby('zone_id').agg( start=('time', 'first'), end=('time', 'last'), regime=('kama_trend', 'first') ) # Plot price with regime background coloring fig, ax = plt.subplots(figsize=(12, 6)) ax.plot(dft.index, dft['close'], label='Close Price', linewidth=1) for _, row in zones.iterrows(): color = 'green' if row['regime'] == 1 else 'red' ax.axvspan(row['start'], row['end'], color=color, alpha=0.2) ax.set_title("Close Price with KAMA Market Regime Highlighting", fontsize=14) ax.set_xlabel("Time") ax.set_ylabel("Price") ax.legend() plt.tight_layout() plt.show()