import requests

import pandas as pd

import os

import traceback

import matplotlib.pyplot as plt

def main():

try:

print("Starting")

# Service name, key and the symbol we will be working with. You can use env files for better security.

SERVICE_NAME = "Service Name"

SERVICE_KEY = "Service Key"

SELECTED_SYMBOL = "FP-LBR-0150"

#Change the directory and file name as needed.

OUTPUT_DIR = r"Your directory"

OUTPUT_PATH = os.path.join(OUTPUT_DIR, "fastmarkets_data.csv")

print("Authenticating")

#The authentication

auth_url = "https://auth.fastmarkets.com/connect/token"

post_data = {

'grant_type': 'servicekey',

'client_id': 'service_client',

'scope': 'fastmarkets.physicalprices.api',

'serviceName': SERVICE_NAME,

'serviceKey': SERVICE_KEY

}

headers = {'Content-Type': 'application/x-www-form-urlencoded'}

print(f" Calling auth endpoint: {auth_url}")

response = requests.post(auth_url, data=post_data, headers=headers, verify=False)

print(f" Auth response status: {response.status_code}")

response.raise_for_status()

access_token = f"bearer {response.json()['access_token']}"

print("Successful")

#Defining the endpoints

instruments_url = f"https://api.fastmarkets.com/physical/v2/Instruments?Symbols={SELECTED_SYMBOL}"

historical_prices_url = f"https://api.fastmarkets.com/physical/v2/Prices/history/None/exclude?Symbols={SELECTED_SYMBOL}&FromDate=2025-01-01"

api_headers = {'Authorization': access_token}

print("Fetching data")

#Get data

print(f" Fetching instruments from: {instruments_url}")

instruments_response = requests.get(instruments_url, headers=api_headers, verify=False)

print(f" Instruments response status: {instruments_response.status_code}")

instruments_response.raise_for_status()

instruments = instruments_response.json()

print(f" Got {len(instruments.get('instruments', []))} instruments")

print(f" Fetching historical prices from: {historical_prices_url}")

prices_response = requests.get(historical_prices_url, headers=api_headers, verify=False)

print(f" Prices response status: {prices_response.status_code}")

prices_response.raise_for_status()

historical_prices = prices_response.json()

print(f" Got historical prices data")

# JSON to dataframe, ensure you have pandas

print("Step 3: Transforming data...")

instruments_table = pd.DataFrame(instruments['instruments'])

# Expand the nested prices

prices_records = []

for instrument in historical_prices['instruments']:

for price in instrument['prices']:

prices_records.append({

'symbol': instrument['symbol'],

'assessmentDate': price['assessmentDate'],

'low': price['low'],

'high': price['high'],

'revision': price['revision']

})

historical_prices_table = pd.DataFrame(prices_records)

print(f" Created {len(prices_records)} price records")

#Join by symbols

combined_table = pd.merge(historical_prices_table, instruments_table, on='symbol', how='inner')

print(f" Joined tables: {len(combined_table)} total rows")

#Selecting relevant columns

final_columns = ['symbol', 'description', 'assessmentDate', 'low', 'high', 'revision', 'status']

final_table = combined_table[final_columns]

#Changing data types

final_table['assessmentDate'] = final_table['assessmentDate'].astype(str)

final_table['low'] = final_table['low'].astype('Int64')

final_table['high'] = final_table['high'].astype('Int64')

final_table['symbol'] = final_table['symbol'].astype(str)

#Saving as .csv

print("Saving to CSV")

os.makedirs(OUTPUT_DIR, exist_ok=True)

final_table.to_csv(OUTPUT_PATH, index=False)

print(f"Data saved to: {OUTPUT_PATH}")

print(f"Final data: {len(final_table)} rows, {len(final_table.columns)} columns")

print("\nFirst 3 rows:")

print(final_table.head(3).to_string())

except Exception as e:

print(f"ERROR: {e}")

print("\nFull error details:")

traceback.print_exc()

if __name__ == "__main__":

main()

# Read the CSV file we've just created

df = pd.read_csv('fastmarkets_data.csv')

# Convert assessmentDate to datetime

df['assessmentDate'] = pd.to_datetime(df['assessmentDate'])

# Create a simple line chart

plt.figure(figsize=(12, 6))

plt.plot(df['assessmentDate'], df['low'], label='Low Price', marker='o')

plt.plot(df['assessmentDate'], df['high'], label='High Price', marker='s')

plt.title('Fastmarkets Price History')

plt.xlabel('Date')

plt.ylabel('Price')

plt.legend()

plt.xticks(rotation=45)

plt.tight_layout()

plt.savefig('price_chart.png') # Save as image

plt.show()