# calculate average from daily data from natural gas futures from yahoo finance
import yfinance
import pandas as pd
import mplfinance as mpf
# read NG=F 2 years worth of data
nat_gas = yfinance.download("NG=F", start="2021-01-01", end="2023-01-01")
nat_gas['Month'] = pd.to_datetime(nat_gas.index)
# pad all date
# print(nat_gas.groupby(by=['Volume', 'Adj Close', pd.Grouper(key='Month', freq='3M')])['Open'].agg('mean'))
mths_3 = pd.date_range(end = nat_gas.index[-1], freq=pd.offsets.BQuarterEnd(),periods=8)
# mths_6 = pd.date_range(end = nat_gas.index[-1], freq=pd.offsets.MonthBegin(6),periods=4)
mth3 = (nat_gas
#filter for last three months
.loc[mths_3]
.groupby(["Month", "Volume"])
.agg(avg_ng_last_3_months=("Open","mean"))
)
# mth6 = (nat_gas
# #filter for last six months
# .loc[mths_6]
# .groupby(["Volume", "Month"])
# .agg(avg_invoices_last_6_months=("Open","mean"))
# )
print(mth3)
# random guess from last two quarters of profit
# at 4.558 assume 360 revenue for Q4 and 160M profit
# 475 revenue and 260M profit for Q4
# considering the investor presentation, thinking its closer to 360M revenue and 160 M profit.
quarterly_profit = 160
# 266.05M * 0.2
# if deliveries are 3 quarters behind then
quarterly_dividend = 266.05 * 0.2
print(quarterly_dividend)
remaining_revenue = quarterly_profit - quarterly_dividend
print(remaining_revenue)
# 176 debt
total_debt = 176
# max debt reduction (they target to get to 50M USD in debt)
min_debt_reduction = total_debt - 100
print(min_debt_reduction)
# bunch of risk of falling gas prices, but with new supply reduction from russia, doubt it would be so bad.
# save fig
mpf.plot(nat_gas,type='ohlc',mav=(30, 60, 90), volume=True, style='yahoo', savefig='ng.png')
