Finding vessels that were opening at Fos¶

Run this example in Colab.¶

Setup¶

Install the Signal Ocean SDK:

In [ ]:

!pip install signal-ocean

!pip install signal-ocean

Set your subscription key, acquired here: https://apis.signalocean.com/profile

In [2]:

signal_ocean_api_key = ""  # replace with your subscription key

signal_ocean_api_key = "" # replace with your subscription key

Description¶

In order to perform our analysis, we will query historical tonnage lists for vessel that, in the last 5 days, were opening at Fos and could make it to Ceyhan within a 20-day window:

In [3]:

from datetime import date, timedelta

from signal_ocean import Connection
from signal_ocean.tonnage_list import (
    TonnageListAPI,
    VesselClassFilter,
    PortFilter,
    VesselFilter,
    MarketDeployment,
    CommercialStatus,
    VesselSubclass,
    DateRange,
)

connection = Connection(signal_ocean_api_key)
api = TonnageListAPI(connection)

vessel_class_filter = VesselClassFilter(name_like="aframax")
vessel_class = api.get_vessel_classes(vessel_class_filter)[0]

port_filter = PortFilter(name_like="Ceyhan")
port = api.get_ports(port_filter)[0]

laycan_end_in_days = 20
start_date = date.today() - timedelta(days=5)
end_date = date.today()

vessel_filter = VesselFilter(
    market_deployments=[MarketDeployment.RELET, MarketDeployment.SPOT],
    vessel_subclass=VesselSubclass.DIRTY,
    commercial_statuses=[CommercialStatus.ON_SUBS],
    latest_ais_since=5,
)

htl_for_supply_trend = api.get_historical_tonnage_list(
    port,
    vessel_class,
    laycan_end_in_days,
    DateRange(start_date, end_date),
    vessel_filter=vessel_filter,
)

from datetime import date, timedelta from signal_ocean import Connection from signal_ocean.tonnage_list import ( TonnageListAPI, VesselClassFilter, PortFilter, VesselFilter, MarketDeployment, CommercialStatus, VesselSubclass, DateRange, ) connection = Connection(signal_ocean_api_key) api = TonnageListAPI(connection) vessel_class_filter = VesselClassFilter(name_like="aframax") vessel_class = api.get_vessel_classes(vessel_class_filter)[0] port_filter = PortFilter(name_like="Ceyhan") port = api.get_ports(port_filter)[0] laycan_end_in_days = 20 start_date = date.today() - timedelta(days=5) end_date = date.today() vessel_filter = VesselFilter( market_deployments=[MarketDeployment.RELET, MarketDeployment.SPOT], vessel_subclass=VesselSubclass.DIRTY, commercial_statuses=[CommercialStatus.ON_SUBS], latest_ais_since=5, ) htl_for_supply_trend = api.get_historical_tonnage_list( port, vessel_class, laycan_end_in_days, DateRange(start_date, end_date), vessel_filter=vessel_filter, )

Now, we can use the data to generate a supply trend for our analysis:

In [4]:

from signal_ocean.tonnage_list import IndexLevel

supply_trend_data_frame = htl_for_supply_trend.to_data_frame()

open_ports_of_interest = ["Fos"]
open_ports_filter = supply_trend_data_frame.open_port_point_in_time.isin(
    open_ports_of_interest
)

aggregated_data = supply_trend_data_frame.groupby(
    IndexLevel.DATE, sort=True
).size()

ax = aggregated_data.plot(
    markerfacecolor="blue",
    color="skyblue",
    linewidth=4,
    label="Vessels opening at Fos",
    figsize=(12, 9),
)

ax.set_ylabel("Vessel count")
ax.legend()

from signal_ocean.tonnage_list import IndexLevel supply_trend_data_frame = htl_for_supply_trend.to_data_frame() open_ports_of_interest = ["Fos"] open_ports_filter = supply_trend_data_frame.open_port_point_in_time.isin( open_ports_of_interest ) aggregated_data = supply_trend_data_frame.groupby( IndexLevel.DATE, sort=True ).size() ax = aggregated_data.plot( markerfacecolor="blue", color="skyblue", linewidth=4, label="Vessels opening at Fos", figsize=(12, 9), ) ax.set_ylabel("Vessel count") ax.legend()

Out[4]:

<matplotlib.legend.Legend at 0x7f1600e1bca0>