Rate Trend¶

Run this example in Colab¶

This example calculates the Crude Oil Tanker Freight Rates For Very Large Crude Carriers (VLCCs) From The Arabian Gulf To China

Setup

Install the Signal Ocean SDK:

In [ ]:

pip install signal-ocean

pip install signal-ocean

Import required libraries / APIs / packages:

Import Voyages Market Data API and some additional libraries

In [1]:

import pandas as pd
import seaborn as sns

from signal_ocean import Connection
from signal_ocean.voyages_market_data import VoyagesMarketDataAPI

import pandas as pd import seaborn as sns from signal_ocean import Connection from signal_ocean.voyages_market_data import VoyagesMarketDataAPI

Fill in your personal SignalOcean API subscription key acquired here and open a connection:

In [2]:

signal_ocean_api_key = '' #replace with your subscription key

signal_ocean_api_key = '' #replace with your subscription key

Create a connection

In [3]:

connection = Connection(signal_ocean_api_key)
api = VoyagesMarketDataAPI(connection)

connection = Connection(signal_ocean_api_key) api = VoyagesMarketDataAPI(connection)

Call the API to get market data

In [2]:

result = api.get_voyage_market_data(vessel_class_id=84, include_matched_fixture = True, filter_by_matched_fixture = True,
        include_labels = True)

result = api.get_voyage_market_data(vessel_class_id=84, include_matched_fixture = True, filter_by_matched_fixture = True, include_labels = True)

Get matched fixtures into a dataframe

In [3]:

fixtures = pd.DataFrame([r.matched_fixture.__dict__ for r in result])

fixtures = pd.DataFrame([r.matched_fixture.__dict__ for r in result])

Transform Data

Keep columns with rate info and rate type in WS (World Scale)

In [4]:

fixtures = fixtures[(~fixtures['rate'].isnull())&(fixtures['rate_type']=='WS')]

fixtures = fixtures[(~fixtures['rate'].isnull())&(fixtures['rate_type']=='WS')]

Keep columns with info on the desired itinerary: Arabian Gulf to China

In [5]:

fixtures = fixtures[(fixtures['load_name'] == 'Arabian Gulf')&(fixtures['discharge_name'] == 'China')]

fixtures = fixtures[(fixtures['load_name'] == 'Arabian Gulf')&(fixtures['discharge_name'] == 'China')]

Convert to datetime to use in aggregations

In [6]:

fixtures['laycan_from'] = pd.to_datetime(fixtures['laycan_from'], errors='coerce', utc=True)
fixtures.sort_values('laycan_from', inplace = True)

fixtures['laycan_from'] = pd.to_datetime(fixtures['laycan_from'], errors='coerce', utc=True) fixtures.sort_values('laycan_from', inplace = True)

set date as index and calculate a the 7 days moving average - we use the lacan from as date

In [7]:

fixtures.set_index('laycan_from', inplace = True, drop=False)
fixtures['7_days_moving_average'] = fixtures['rate'].rolling('7D').mean()

fixtures.set_index('laycan_from', inplace = True, drop=False) fixtures['7_days_moving_average'] = fixtures['rate'].rolling('7D').mean()

extract the day and keep only last done per day

In [8]:

fixtures['Date'] = fixtures['laycan_from'].dt.tz_localize(None).dt.to_period('D').dt.to_timestamp()
fixtures = fixtures.drop_duplicates(subset=['Date'], keep='last')

fixtures['Date'] = fixtures['laycan_from'].dt.tz_localize(None).dt.to_period('D').dt.to_timestamp() fixtures = fixtures.drop_duplicates(subset=['Date'], keep='last')

Plot the rate moving average and last done

In [9]:

sns.set(rc = {'figure.figsize':(15,8)})

start_date = '2022-01-01'

sns.lineplot(data=fixtures[fixtures['Date']>start_date], x='Date', color='red', y='7_days_moving_average').set_title("Rate moving average and last done")
sns.scatterplot(data=fixtures[fixtures['Date']>start_date], x='Date', y='rate')

sns.set(rc = {'figure.figsize':(15,8)}) start_date = '2022-01-01' sns.lineplot(data=fixtures[fixtures['Date']>start_date], x='Date', color='red', y='7_days_moving_average').set_title("Rate moving average and last done") sns.scatterplot(data=fixtures[fixtures['Date']>start_date], x='Date', y='rate')

Out[9]:

<AxesSubplot:title={'center':'Rate moving average and last done'}, xlabel='Date', ylabel='7_days_moving_average'>