py-market-profile

Once you have a sliced MarketProfile object (mp_slice), you can extract various market profile metrics and properties:

• .profile: Returns a Pandas Series representing the volume at different price levels.
• .poc_price: Returns the Point of Control (POC) price.
• .profile_range: Returns a tuple of (min_price, max_price) for the profile.
• .value_area: Returns a tuple of (low_price, high_price) for the value area.
• .balanced_target: Returns the balanced target price.
• .initial_balance(): Returns a tuple representing the initial balance range.
• .open_range(): Returns a tuple representing the open range.
• .low_value_nodes: Returns a Pandas Series of low value nodes.
• .high_value_nodes: Returns a Pandas Series of high value nodes.

# Assuming mp_slice is a sliced MarketProfile object
print(mp_slice.poc_price)
print(mp_slice.value_area)
print(mp_slice.initial_balance())
print(mp_slice.low_value_nodes)

Once you have a MarketProfileSlice (via slicing a MarketProfile), you can access several key market profile metrics:

• open_range(): Returns a tuple (low, high) representing the price range during the open_range_size period.
• initial_balance(): Returns a tuple (low, high) representing the price range during the initial_balance_delta period.
• calculate_value_area(): Returns a tuple (val, vah) representing the Value Area Low and Value Area High.
• calculate_balanced_target(): Returns the calculated balanced target price (bt).
• find_extrema(sign): Finds local extrema in the profile. Use np.less for Low Value Nodes (LVN) and np.greater for High Value Nodes (HVN).
• as_dict(): Returns a dictionary containing all calculated metrics.

Dictionary Keys in as_dict():

• or_low, or_high: Open Range bounds.
• ib_low, ib_high: Initial Balance bounds.
• poc: Point of Control price.
• low, high: Profile price range bounds.
• val, vah: Value Area Low and High.
• bt: Balanced Target.
• lvn: Low Value Nodes (Series).
• hvn: High Value Nodes (Series).

# Assuming sliced_mp is a MarketProfileSlice
metrics = sliced_mp.as_dict()

print(f"POC: {metrics['poc']}")
print(f"Value Area: {metrics['val']} - {metrics['vah']}")
print(f"Open Range: {metrics['or_low']} - {metrics['or_high']}")

The MarketProfile class is the primary entry point for analyzing market data. It takes a pandas DataFrame containing market data (expected to have 'Close', 'Low', 'High', and 'Volume' columns) and applies market profile logic based on specified parameters.

Parameters:

• df: A pandas DataFrame.
• tick_size (float, default: 0.05): The minimum price increment.
• prices_per_row (int, default: 1): Multiplier for tick size to determine row size.
• row_size (float, default: tick_size * prices_per_row): The actual price width of a profile row.
• open_range_size (pd.Timedelta, default: 10 minutes): Duration of the open range.
• initial_balance_delta (pd.Timedelta, default: 1 hour): Duration of the initial balance period.
• value_area_pct (float, default: 0.70): The percentage of volume/TPO to include in the value area.
• mode (str, default: 'vol'): The calculation mode. Use 'vol' for volume-based profiles or 'tpo' for Time Price Opportunity (count-based) profiles.

import pandas as pd
from market_profile import MarketProfile

# Assuming df has 'Close', 'Low', 'High', 'Volume' and a DatetimeIndex
df = pd.read_csv('market_data.csv', index_col=0, parse_dates=True)

mp = MarketProfile(
    df, 
    tick_size=0.25, 
    mode='vol', 
    value_area_pct=0.70
)

Once a MarketProfile (or a slice of one) is created, you can access calculated market indicators and the underlying profile data:

• mp.profile: The underlying profile data (can be plotted using pandas .plot())
• mp.poc_price: Point of Control price
• mp.profile_range: The range of the profile
• mp.value_area: The value area range
• mp.balanced_target: The balanced target price
• mp.initial_balance(): Method to return the initial balance range
• mp.open_range(): Method to return the opening range

Note: The methods initial_balance() and open_range() return a tuple representing the range.

# Accessing indicators from a MarketProfile slice
print "Initial balance: %f, %f" % mp_slice.initial_balance()
print "Opening range: %f, %f" % mp_slice.open_range()
print "POC: %f" % mp_slice.poc_price
print "Profile range: %f, %f" % mp_slice.profile_range
print "Value area: %f, %f" % mp_slice.value_area
print "Balanced Target: %f" % mp_slice.balanced_target

# Plotting the profile data
mp_slice.profile.plot(kind='bar')