pm6six commited on
Commit
465b45f
·
verified ·
1 Parent(s): ae3466a

Update app.py

Browse files
Files changed (1) hide show
  1. app.py +2 -5
app.py CHANGED
@@ -5,8 +5,9 @@ import matplotlib.pyplot as plt
5
  import gradio as gr
6
  import io
7
 
 
 
8
  def sma_crossover_strategy(initial_budget, start_date, end_date, ticker):
9
- print("Fetching data...")
10
  try:
11
  df = yf.download(ticker, start=start_date, end=end_date, progress=False)
12
  if df.empty:
@@ -15,10 +16,8 @@ def sma_crossover_strategy(initial_budget, start_date, end_date, ticker):
15
  return None, f"Error fetching data: {str(e)}", None
16
 
17
  df = df[['Close']]
18
-
19
  df['SMA_50'] = df['Close'].rolling(window=50).mean()
20
  df['SMA_150'] = df['Close'].rolling(window=150).mean()
21
-
22
  df['Signal'] = 0
23
  df['Signal'][df['SMA_50'] > df['SMA_150']] = 1
24
  df['Signal'][df['SMA_50'] < df['SMA_150']] = -1
@@ -28,7 +27,6 @@ def sma_crossover_strategy(initial_budget, start_date, end_date, ticker):
28
  shares = 0
29
  portfolio_values = []
30
 
31
- print("Starting simulation...")
32
  for index, row in df.iterrows():
33
  if pd.isna(row['Close']):
34
  continue
@@ -38,7 +36,6 @@ def sma_crossover_strategy(initial_budget, start_date, end_date, ticker):
38
  elif row['Position'] == -1 and shares > 0:
39
  cash = shares * row['Close']
40
  shares = 0
41
-
42
  portfolio_value = cash + (shares * row['Close'])
43
  portfolio_values.append(portfolio_value)
44
 
 
5
  import gradio as gr
6
  import io
7
 
8
+ print("Starting app...")
9
+
10
  def sma_crossover_strategy(initial_budget, start_date, end_date, ticker):
 
11
  try:
12
  df = yf.download(ticker, start=start_date, end=end_date, progress=False)
13
  if df.empty:
 
16
  return None, f"Error fetching data: {str(e)}", None
17
 
18
  df = df[['Close']]
 
19
  df['SMA_50'] = df['Close'].rolling(window=50).mean()
20
  df['SMA_150'] = df['Close'].rolling(window=150).mean()
 
21
  df['Signal'] = 0
22
  df['Signal'][df['SMA_50'] > df['SMA_150']] = 1
23
  df['Signal'][df['SMA_50'] < df['SMA_150']] = -1
 
27
  shares = 0
28
  portfolio_values = []
29
 
 
30
  for index, row in df.iterrows():
31
  if pd.isna(row['Close']):
32
  continue
 
36
  elif row['Position'] == -1 and shares > 0:
37
  cash = shares * row['Close']
38
  shares = 0
 
39
  portfolio_value = cash + (shares * row['Close'])
40
  portfolio_values.append(portfolio_value)
41