Copper impact in Exchange Rate

import pandas as pd import yfinance as yf import matplotlib.pyplot as plt import plotly.graph_objects as go import numpy as np import warnings warnings.filterwarnings("ignore")

def generate(symbol): symbol = yf.Ticker(symbol) df = symbol.history(period="max") df['change'] = (df['Close'].diff().bfill()*100 / df['Close']).astype(float) df['Date'] = df.index df['Date_abs'] = df['Date'].astype(int) df = df[df.Close != 0] return df

copper = generate('HG=F')[['Date', 'Close']].rename(columns={'Close': 'Close_Copper'}).reset_index(drop=True) usd_clp = generate('CLP=X')[['Date', 'Close']].rename(columns={'Close': 'Close_CLP'}).reset_index(drop=True) copper

Datedatetime64[ns]

2000-08-30 00:00:00 - 2022-03-21 00:00:00

Close_Copperfloat64

0.6039999723434448 - 4.928999900817871

2000-08-30T00:00:00.000000

0.8849999904632568

2000-08-31T00:00:00.000000

0.8849999904632568

2000-09-01T00:00:00.000000

0.8889999985694885

2000-09-05T00:00:00.000000

0.906000018119812

2000-09-06T00:00:00.000000

0.9014999866485596

2000-09-07T00:00:00.000000

0.906000018119812

2000-09-08T00:00:00.000000

0.9120000004768372

2000-09-11T00:00:00.000000

0.9129999876022339

2000-09-12T00:00:00.000000

0.9284999966621399

2000-09-13T00:00:00.000000

0.9275000095367432

def join_dfs(left_df, right_df): result = pd.merge(left_df, right_df, on='Date', how='inner') result = result.sort_values('Date') return result df = join_dfs(copper, usd_clp) df = df[df['Close_CLP']>10] df['Copper in CLP'] = df['Close_Copper'] * df['Close_CLP'] df = df.rename(columns={'Close_Copper': 'Copper', 'Close_CLP': 'USD in CLP'}) df

Datedatetime64[ns]

2003-12-01 00:00:00 - 2022-03-21 00:00:00

Copperfloat64

0.9555000066757202 - 4.928999900817871

2003-12-01T00:00:00.000000

0.9555000066757202

2003-12-02T00:00:00.000000

0.9695000052452087

2003-12-03T00:00:00.000000

0.9760000109672546

2003-12-04T00:00:00.000000

0.9629999995231628

2003-12-05T00:00:00.000000

0.968999981880188

2003-12-08T00:00:00.000000

0.9664999842643738

2003-12-09T00:00:00.000000

0.9779999852180481

2003-12-10T00:00:00.000000

0.9775000214576721

2003-12-11T00:00:00.000000

0.9760000109672546

2003-12-12T00:00:00.000000

0.9865000247955322

Copper in CLP showed a constant trend between 2006 and 2020, afterwards the higher exchange rate and Higher Copper prices have raised the Copper/CLP to unseen values. This is positive news for Chile's economy as the metal is the biggest export.

import plotly.express as px import pandas as pd df = df[df['Date']>'2000-01-01'] df['Date'] = pd.to_datetime(df['Date']) df['Year'] = df['Date'].dt.year df.index = df['Date'] df = df.resample('Y').mean() df['Date'] = df.index fig = px.line(df, x="USD in CLP", y="Copper", text="Year") fig.update_traces(textposition="top left") fig.show()