SMA Full Code Walkthrough

sma.py

import requests
import numpy as np
import pandas as pd

intrinio_api_key = "YOUR_INTRINIO_API_KEY_HERE"

def _stock_prices_dataset(ticker):

  res = requests.get(f"https://api-v2.intrinio.com/securities/{ticker}/prices?page_size=10000&api_key={intrinio_api_key}")
  stock_prices_dataset = pd.DataFrame(res.json().get("stock_prices"))
  stock_prices_dataset = stock_prices_dataset[["date", "adj_close"]][::-1].reset_index(drop=True)

  stock_prices_dataset["returns"] = np.log(stock_prices_dataset["adj_close"]).diff()
  stock_prices_dataset["returns"] = stock_prices_dataset["returns"].shift(-1)

  return stock_prices_dataset

def _sma_stock_prices_dataset(stock_prices_dataset, long_sma = 200, short_sma = 50):

  stock_prices_dataset[f"{long_sma}_day_sma"] = stock_prices_dataset["adj_close"].rolling(long_sma).mean()
  stock_prices_dataset[f"{short_sma}_day_sma"] = stock_prices_dataset["adj_close"].rolling(short_sma).mean()
  stock_prices_dataset = stock_prices_dataset.dropna()
  stock_prices_dataset = stock_prices_dataset.reset_index(drop=True)

  return stock_prices_dataset

def _sma_crossover_dataset(sma_stock_prices_dataset, long_sma = 200, short_sma = 50):

  sma_crossover_datalist = []

  sma_stock_prices_dataset["crossover_signal"] = np.where(sma_stock_prices_dataset[f"{short_sma}_day_sma"] >= sma_stock_prices_dataset[f"{long_sma}_day_sma"], 1, 0)
  sma_stock_prices_dataset["prev_crossover_signal"] = sma_stock_prices_dataset["crossover_signal"].shift(1, fill_value=0)

  sma_stock_prices_dataset["buy"] = (sma_stock_prices_dataset["crossover_signal"] == 1) & (sma_stock_prices_dataset["prev_crossover_signal"] == 0)
  sma_stock_prices_dataset["buy"]  = sma_stock_prices_dataset["buy"] .replace({True: 1, False: 0})
  sma_stock_prices_dataset["sell"] = (sma_stock_prices_dataset["crossover_signal"] == 0) & (sma_stock_prices_dataset["prev_crossover_signal"] == 1)
  sma_stock_prices_dataset["sell"]  = sma_stock_prices_dataset["sell"] .replace({True: 1, False: 0})
  
  holding = False
  for sma_stock_prices in sma_stock_prices_dataset.to_dict("records"):
    
    if holding and sma_stock_prices.get("sell") == 1:
      holding = False
    
    if not holding and sma_stock_prices.get("buy") == 1:
      holding = True

    sma_stock_prices["hold"] = holding
    sma_crossover_datalist.append(sma_stock_prices)
  
  sma_crossover_dataset = pd.DataFrame(sma_crossover_datalist)

  return sma_crossover_dataset

def _sma_returns_data(sma_crossover_dataset):

  sma_crossover_dataset["algo_returns"] = sma_crossover_dataset["returns"] * sma_crossover_dataset["hold"]
  sma_return = round(sma_crossover_dataset["algo_returns"].sum() * 100, 3)
  buy_and_hold_return = round(sma_crossover_dataset["returns"].sum() * 100, 3)
  total_entries = sma_crossover_dataset["buy"].sum()

  return sma_return, buy_and_hold_return, total_entries

def sma_handler(ticker, long_sma = 200, short_sma = 50):

  stock_prices_dataset = _stock_prices_dataset(ticker)

  start_date = stock_prices_dataset.iloc[0]['date']
  sma_stock_prices_dataset = _sma_stock_prices_dataset(stock_prices_dataset, long_sma, short_sma)
  sma_crossover_dataset = _sma_crossover_dataset(sma_stock_prices_dataset, long_sma, short_sma)
  sma_return, buy_and_hold_return, total_entries = _sma_returns_data(sma_crossover_dataset)
  
  print(f"Start Date: {start_date}")
  print(f"SMA Return: {sma_return}")
  print(f"Buy and Hold Return: {buy_and_hold_return}")
  print(f"Total Entries: {total_entries}")

sma_handler("PLTR")

# Start Date: 2020-09-30
# SMA Return: -15.29
# Buy and Hold Return: -66.865
# Total Entries: 2

sma_handler("AAPL")

# Start Date: 1982-09-16
# SMA Return: 429.385
# Buy and Hold Return: 685.877
# Total Entries: 26
  
sma_handler("GME")

# Start Date: 2002-02-13
# SMA Return: 345.54
# Buy and Hold Return: 303.315
# Total Entries: 16