CashFlow Analysis¶

Before talking the scheule capabilities, it is required to introduce the user with the CashFlow module. This Module allows you to create a series of cashflow woth the purpose of creating a CashFlow Model which can be used to analized the financial behavior of any forecast produced by dcapy.dca Module

This module is inspired by Python cashflows Package

In [1]:

from datetime import date 
from dcapy.cashflow import CashFlow
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

from datetime import date from dcapy.cashflow import CashFlow import numpy as np import matplotlib.pyplot as plt import pandas as pd

In [2]:

cash1 = CashFlow(
    name = 'Cashflow_zeros',
    start = date(2021,1,1),
    end = date(2021,6,1),
    freq_input = 'M'
)
print(cash1)
print(cash1.get_cashflow())

cash1 = CashFlow( name = 'Cashflow_zeros', start = date(2021,1,1), end = date(2021,6,1), freq_input = 'M' ) print(cash1) print(cash1.get_cashflow())

name='Cashflow_zeros' const_value=0 start=datetime.date(2021, 1, 1) end=datetime.date(2021, 6, 1) periods=None freq_output=None freq_input=<FreqEnum.M: 'M'> chgpts=None
2021-01    0.0
2021-02    0.0
2021-03    0.0
2021-04    0.0
2021-05    0.0
2021-06    0.0
Freq: M, dtype: float64

By default, if the cashflow has no value assignment, it creates an array of zeros.

In [3]:

cash2 = CashFlow(
    name = 'Cashflow_cons_value',
    const_value= 2000,
    start = date(2021,1,1),
    end = date(2021,6,1),
    freq_input = 'M'
)
print(cash2)
print(cash2.get_cashflow())

cash2 = CashFlow( name = 'Cashflow_cons_value', const_value= 2000, start = date(2021,1,1), end = date(2021,6,1), freq_input = 'M' ) print(cash2) print(cash2.get_cashflow())

name='Cashflow_cons_value' const_value=2000.0 start=datetime.date(2021, 1, 1) end=datetime.date(2021, 6, 1) periods=None freq_output=None freq_input=<FreqEnum.M: 'M'> chgpts=None
2021-01    2000.0
2021-02    2000.0
2021-03    2000.0
2021-04    2000.0
2021-05    2000.0
2021-06    2000.0
Freq: M, dtype: float64

In [4]:

cash3 = CashFlow(
    name = 'Cashflow_different_values',
    const_value= [2000,1500,1252,1458,5869,550],
    start = date(2021,1,1),
    end = date(2026,1,1),
    freq_input = 'A'
)
print(cash3)
print(cash3.get_cashflow())

cash3 = CashFlow( name = 'Cashflow_different_values', const_value= [2000,1500,1252,1458,5869,550], start = date(2021,1,1), end = date(2026,1,1), freq_input = 'A' ) print(cash3) print(cash3.get_cashflow())

name='Cashflow_different_values' const_value=[2000.0, 1500.0, 1252.0, 1458.0, 5869.0, 550.0] start=datetime.date(2021, 1, 1) end=datetime.date(2026, 1, 1) periods=None freq_output=None freq_input=<FreqEnum.A: 'A'> chgpts=None
2021    2000.0
2022    1500.0
2023    1252.0
2024    1458.0
2025    5869.0
2026     550.0
Freq: A-DEC, dtype: float64

Get output cashflow on different time frequency¶

In [5]:

cash3 = CashFlow(
    name = 'Cashflow_different_values',
    const_value= 100,
    start = date(2021,1,1),
    end = date(2022,12,1),
    freq_input = 'M'
)
print(cash3)
print(cash3.get_cashflow())

cash3 = CashFlow( name = 'Cashflow_different_values', const_value= 100, start = date(2021,1,1), end = date(2022,12,1), freq_input = 'M' ) print(cash3) print(cash3.get_cashflow())

name='Cashflow_different_values' const_value=100.0 start=datetime.date(2021, 1, 1) end=datetime.date(2022, 12, 1) periods=None freq_output=None freq_input=<FreqEnum.M: 'M'> chgpts=None
2021-01    100.0
2021-02    100.0
2021-03    100.0
2021-04    100.0
2021-05    100.0
2021-06    100.0
2021-07    100.0
2021-08    100.0
2021-09    100.0
2021-10    100.0
2021-11    100.0
2021-12    100.0
2022-01    100.0
2022-02    100.0
2022-03    100.0
2022-04    100.0
2022-05    100.0
2022-06    100.0
2022-07    100.0
2022-08    100.0
2022-09    100.0
2022-10    100.0
2022-11    100.0
2022-12    100.0
Freq: M, dtype: float64

In [6]:

print(cash3.get_cashflow(freq_output='A'))

print(cash3.get_cashflow(freq_output='A'))

2021    1200.0
2022    1200.0
Freq: A-DEC, dtype: float64

In [7]:

cash3a = CashFlow(
    name = 'Cashflow_different_values',
    const_value= 100,
    start = 0,
    end = 47,
    periods=None,
    freq_input = 'M'
)
print(cash3a)
print(cash3a.get_cashflow(freq_output='A'))

cash3a = CashFlow( name = 'Cashflow_different_values', const_value= 100, start = 0, end = 47, periods=None, freq_input = 'M' ) print(cash3a) print(cash3a.get_cashflow(freq_output='A'))

name='Cashflow_different_values' const_value=100.0 start=0 end=47 periods=None freq_output=None freq_input=<FreqEnum.M: 'M'> chgpts=None
0.0    1200.0
1.0    1200.0
2.0    1200.0
3.0    1200.0
dtype: float64

Define Number of periods of the cashflow¶

In [8]:

cash4 = CashFlow(
    name = 'Cashflow_different_values',
    const_value= 10000,
    periods = 6,
    start = date(2021,1,1),
    end = date(2022,12,1),
    freq_input = 'M'
)
print(cash4)
print(cash4.get_cashflow())

cash4 = CashFlow( name = 'Cashflow_different_values', const_value= 10000, periods = 6, start = date(2021,1,1), end = date(2022,12,1), freq_input = 'M' ) print(cash4) print(cash4.get_cashflow())

name='Cashflow_different_values' const_value=10000.0 start=datetime.date(2021, 1, 1) end=datetime.date(2022, 12, 1) periods=6 freq_output=None freq_input=<FreqEnum.M: 'M'> chgpts=None
2021-01    10000.0
2021-02    10000.0
2021-03    10000.0
2021-04    10000.0
2021-05    10000.0
2021-06    10000.0
Freq: M, dtype: float64

In [9]:

### Set to the last period

### Set to the last period

In [10]:

cash5 = CashFlow(
    name = 'Cashflow_different_values',
    const_value= -10000,
    periods = -1,
    start = date(2021,1,1),
    end = date(2022,12,1),
    freq_input = 'M'
)
print(cash5)
print(cash5.get_cashflow())

cash5 = CashFlow( name = 'Cashflow_different_values', const_value= -10000, periods = -1, start = date(2021,1,1), end = date(2022,12,1), freq_input = 'M' ) print(cash5) print(cash5.get_cashflow())

name='Cashflow_different_values' const_value=-10000.0 start=datetime.date(2021, 1, 1) end=datetime.date(2022, 12, 1) periods=-1 freq_output=None freq_input=<FreqEnum.M: 'M'> chgpts=None
2022-12   -10000.0
Freq: M, dtype: float64

Set custom cashflow throgh time¶

by setting the chgpts parameter as a dictionary with date and value keys, you can make a custom cashflow to certain dates

In [11]:

cash6 = CashFlow(
    name = 'Cashflow_different_values',
    start = date(2021,1,1),
    end = date(2021,12,1),
    freq_input = 'M',
    chgpts = {
        'date': ['2021-03-01','2021-07-01', '2021-12-01'],
        'value': [-500,-600,-700]
    }
)
print(cash6)
print(cash6.get_cashflow())

cash6 = CashFlow( name = 'Cashflow_different_values', start = date(2021,1,1), end = date(2021,12,1), freq_input = 'M', chgpts = { 'date': ['2021-03-01','2021-07-01', '2021-12-01'], 'value': [-500,-600,-700] } ) print(cash6) print(cash6.get_cashflow())

name='Cashflow_different_values' const_value=0 start=datetime.date(2021, 1, 1) end=datetime.date(2021, 12, 1) periods=None freq_output=None freq_input=<FreqEnum.M: 'M'> chgpts=ChgPts(date=[datetime.date(2021, 3, 1), datetime.date(2021, 7, 1), datetime.date(2021, 12, 1)], value=[-500.0, -600.0, -700.0])
2021-01      0.0
2021-02      0.0
2021-03   -500.0
2021-04      0.0
2021-05      0.0
2021-06      0.0
2021-07   -600.0
2021-08      0.0
2021-09      0.0
2021-10      0.0
2021-11      0.0
2021-12   -700.0
Freq: M, dtype: float64

Create a cashflow from a pd.Series

In [12]:

period = pd.period_range(start='2021-01-01', end='2021-03-01', freq='M').to_timestamp()
sr = pd.Series([1000,2000,3000], index=period, name='Cashflow_different_values')
sr

period = pd.period_range(start='2021-01-01', end='2021-03-01', freq='M').to_timestamp() sr = pd.Series([1000,2000,3000], index=period, name='Cashflow_different_values') sr

Out[12]:

2021-01-01    1000
2021-02-01    2000
2021-03-01    3000
Freq: MS, Name: Cashflow_different_values, dtype: int64

In [13]:

csh_from_sr = CashFlow.from_series(sr)
csh_from_sr

csh_from_sr = CashFlow.from_series(sr) csh_from_sr

Out[13]:

CashFlow(name='Cashflow_different_values', const_value=[1000.0, 2000.0, 3000.0], start=datetime.date(2021, 1, 1), end=datetime.date(2021, 3, 1), periods=None, freq_output=None, freq_input=<FreqEnum.M: 'M'>, chgpts=None)

CashFlow Models¶

The CashFlow class is very useful when combined in a full CashFlow Model to incorporate income, opex and capex and evaluate the capital efficiency on a Free Cash Flow.

Let's create some cashflows that represent those items.

In [14]:

oil_sell = CashFlow(
    name = 'oil_sell',
    const_value= [60000,58000,55000,62000,65000,64000,60000,58000,55000,62000,65000,64000],
    start = date(2021,1,1),
    end = date(2021,12,1),
    freq_input = 'M'
)

gas_sell = CashFlow(
    name = 'gas_sell',
    const_value= [36000,34000,30000,34000,28000,31000,36000,34000,30000,34000,28000,31000],
    start = date(2021,1,1),
    end = date(2021,12,1),
    freq_input = 'M'
)

gas_sell = CashFlow(
    name = 'gas_sell',
    const_value= [36000,34000,30000,34000,28000,31000,36000,34000,30000,34000,28000,31000],
    start = date(2021,1,1),
    end = date(2021,12,1),
    freq_input = 'M'
)

opex = CashFlow(
    name = 'cost',
    const_value= -30000,
    start = date(2021,1,1),
    end = date(2021,12,1),
    freq_input = 'M'
)

capex_init = CashFlow(
    name = 'capex_initial',
    const_value= -250000,
    periods = 1,
    start = date(2021,1,1),
    end = date(2021,12,1),
    freq_input = 'M'
)

capex_final = CashFlow(
    name = 'capex_initial',
    const_value= -80000,
    periods = -1,
    start = date(2021,1,1),
    end = date(2021,12,1),
    freq_input = 'M'
)

oil_sell = CashFlow( name = 'oil_sell', const_value= [60000,58000,55000,62000,65000,64000,60000,58000,55000,62000,65000,64000], start = date(2021,1,1), end = date(2021,12,1), freq_input = 'M' ) gas_sell = CashFlow( name = 'gas_sell', const_value= [36000,34000,30000,34000,28000,31000,36000,34000,30000,34000,28000,31000], start = date(2021,1,1), end = date(2021,12,1), freq_input = 'M' ) gas_sell = CashFlow( name = 'gas_sell', const_value= [36000,34000,30000,34000,28000,31000,36000,34000,30000,34000,28000,31000], start = date(2021,1,1), end = date(2021,12,1), freq_input = 'M' ) opex = CashFlow( name = 'cost', const_value= -30000, start = date(2021,1,1), end = date(2021,12,1), freq_input = 'M' ) capex_init = CashFlow( name = 'capex_initial', const_value= -250000, periods = 1, start = date(2021,1,1), end = date(2021,12,1), freq_input = 'M' ) capex_final = CashFlow( name = 'capex_initial', const_value= -80000, periods = -1, start = date(2021,1,1), end = date(2021,12,1), freq_input = 'M' )

Create a CashFlow Model¶

In [15]:

from dcapy.cashflow import CashFlowModel

from dcapy.cashflow import CashFlowModel

In [16]:

cm = CashFlowModel(
    name = 'Example Cashflow Model',
    income=[oil_sell, gas_sell],
    opex=[opex],
    capex=[capex_init,capex_final]
)

print(cm)

cm = CashFlowModel( name = 'Example Cashflow Model', income=[oil_sell, gas_sell], opex=[opex], capex=[capex_init,capex_final] ) print(cm)

name='Example Cashflow Model' income=[CashFlow(name='oil_sell', const_value=[60000.0, 58000.0, 55000.0, 62000.0, 65000.0, 64000.0, 60000.0, 58000.0, 55000.0, 62000.0, 65000.0, 64000.0], start=datetime.date(2021, 1, 1), end=datetime.date(2021, 12, 1), periods=None, freq_output=None, freq_input=<FreqEnum.M: 'M'>, chgpts=None), CashFlow(name='gas_sell', const_value=[36000.0, 34000.0, 30000.0, 34000.0, 28000.0, 31000.0, 36000.0, 34000.0, 30000.0, 34000.0, 28000.0, 31000.0], start=datetime.date(2021, 1, 1), end=datetime.date(2021, 12, 1), periods=None, freq_output=None, freq_input=<FreqEnum.M: 'M'>, chgpts=None)] opex=[CashFlow(name='cost', const_value=-30000.0, start=datetime.date(2021, 1, 1), end=datetime.date(2021, 12, 1), periods=None, freq_output=None, freq_input=<FreqEnum.M: 'M'>, chgpts=None)] capex=[CashFlow(name='capex_initial', const_value=-250000.0, start=datetime.date(2021, 1, 1), end=datetime.date(2021, 12, 1), periods=1, freq_output=None, freq_input=<FreqEnum.M: 'M'>, chgpts=None), CashFlow(name='capex_initial', const_value=-80000.0, start=datetime.date(2021, 1, 1), end=datetime.date(2021, 12, 1), periods=-1, freq_output=None, freq_input=<FreqEnum.M: 'M'>, chgpts=None)]

Create the Free Cash Flow

In [17]:

print(cm.fcf())

print(cm.fcf())

         oil_sell  gas_sell  total_income     cost  total_opex  capex_initial  \
2021-01   60000.0   36000.0       96000.0 -30000.0    -30000.0      -250000.0   
2021-02   58000.0   34000.0       92000.0 -30000.0    -30000.0            0.0   
2021-03   55000.0   30000.0       85000.0 -30000.0    -30000.0            0.0   
2021-04   62000.0   34000.0       96000.0 -30000.0    -30000.0            0.0   
2021-05   65000.0   28000.0       93000.0 -30000.0    -30000.0            0.0   
2021-06   64000.0   31000.0       95000.0 -30000.0    -30000.0            0.0   
2021-07   60000.0   36000.0       96000.0 -30000.0    -30000.0            0.0   
2021-08   58000.0   34000.0       92000.0 -30000.0    -30000.0            0.0   
2021-09   55000.0   30000.0       85000.0 -30000.0    -30000.0            0.0   
2021-10   62000.0   34000.0       96000.0 -30000.0    -30000.0            0.0   
2021-11   65000.0   28000.0       93000.0 -30000.0    -30000.0            0.0   
2021-12   64000.0   31000.0       95000.0 -30000.0    -30000.0            0.0   

         capex_initial  total_capex       fcf   cum_fcf  
2021-01            0.0    -250000.0 -184000.0 -184000.0  
2021-02            0.0          0.0   62000.0 -122000.0  
2021-03            0.0          0.0   55000.0  -67000.0  
2021-04            0.0          0.0   66000.0   -1000.0  
2021-05            0.0          0.0   63000.0   62000.0  
2021-06            0.0          0.0   65000.0  127000.0  
2021-07            0.0          0.0   66000.0  193000.0  
2021-08            0.0          0.0   62000.0  255000.0  
2021-09            0.0          0.0   55000.0  310000.0  
2021-10            0.0          0.0   66000.0  376000.0  
2021-11            0.0          0.0   63000.0  439000.0  
2021-12       -80000.0     -80000.0  -15000.0  424000.0  

Estimate The IRR and NPV¶

In [18]:

irr = cm.irr()

print(f'IRR {round(irr,2)}')

irr = cm.irr() print(f'IRR {round(irr,2)}')

IRR 0.31

Define annual rates of 0, 5, 10 and 15%. Convert them to montly basis and call the nvp method

In [19]:

annual_rates = np.array([0,0.5,0.1,0.15])
monthly_rates = np.power(1 + annual_rates,(1/12)) - 1
print(monthly_rates)

annual_rates = np.array([0,0.5,0.1,0.15]) monthly_rates = np.power(1 + annual_rates,(1/12)) - 1 print(monthly_rates)

[0.         0.03436608 0.00797414 0.01171492]

In [20]:

cashflow_npv = cm.npv(monthly_rates)

print(cashflow_npv)

cashflow_npv = cm.npv(monthly_rates) print(cashflow_npv)

                    npv
0.000000  424000.000000
0.034366  325004.808220
0.007974  398668.970497
0.011715  387312.164217

Plot The cashflow¶

In [21]:

fig, ax = plt.subplots(2,2,figsize=(20,10))

cm.plot(ax=ax[0,0])
cm.plot(ax=ax[0,1], cum=True)
cm.plot(ax=ax[1,0], format='m')
cm.plot(ax=ax[1,1], bar_kw={'palette': {
            'income':'darkgreen',
            'opex':'blue',
            'capex':'darkred',
            'fcf':'black'
        }})

fig, ax = plt.subplots(2,2,figsize=(20,10)) cm.plot(ax=ax[0,0]) cm.plot(ax=ax[0,1], cum=True) cm.plot(ax=ax[1,0], format='m') cm.plot(ax=ax[1,1], bar_kw={'palette': { 'income':'darkgreen', 'opex':'blue', 'capex':'darkred', 'fcf':'black' }})

/Users/scuervo91/Documents/dev/dcapy/dcapy/cashflow/cashflow.py:368: UserWarning: FixedFormatter should only be used together with FixedLocator
  grax.set_yticklabels([fmt.format(i/format_dict[format]['factor']) for i in ticks])
/Users/scuervo91/Documents/dev/dcapy/dcapy/cashflow/cashflow.py:368: UserWarning: FixedFormatter should only be used together with FixedLocator
  grax.set_yticklabels([fmt.format(i/format_dict[format]['factor']) for i in ticks])
/Users/scuervo91/Documents/dev/dcapy/dcapy/cashflow/cashflow.py:376: UserWarning: FixedFormatter should only be used together with FixedLocator
  spax.set_yticklabels([fmt.format(i/format_dict[format]['factor']) for i in ticks_cum])
/Users/scuervo91/Documents/dev/dcapy/dcapy/cashflow/cashflow.py:368: UserWarning: FixedFormatter should only be used together with FixedLocator
  grax.set_yticklabels([fmt.format(i/format_dict[format]['factor']) for i in ticks])
/Users/scuervo91/Documents/dev/dcapy/dcapy/cashflow/cashflow.py:368: UserWarning: FixedFormatter should only be used together with FixedLocator
  grax.set_yticklabels([fmt.format(i/format_dict[format]['factor']) for i in ticks])

In [22]:

cm.plot(
    bar_kw={
        'palette': {
            'income':'darkgreen',
            'opex':'blue',
            'capex':'darkred',
            'fcf':'black'
        }
    }
)

cm.plot( bar_kw={ 'palette': { 'income':'darkgreen', 'opex':'blue', 'capex':'darkred', 'fcf':'black' } } )

/Users/scuervo91/Documents/dev/dcapy/dcapy/cashflow/cashflow.py:368: UserWarning: FixedFormatter should only be used together with FixedLocator
  grax.set_yticklabels([fmt.format(i/format_dict[format]['factor']) for i in ticks])

In [ ]: