1. Assuming that you already have Python installed and using windows first install fix_yahoo_finance. This is the library that helps to fetch data from Yahoo. Open Command Prompt and type -> pip install fix_yahoo_finance
2. Thats it. Open Command Prompt again and type- jupyter notebook. That shall open the notebook in your default web browser and you will see the directory. Navigate to the files where you have downloaded them (Preferably on desktop).
3. Open the file that you wish to see (.ipynb extension are of Jupyter Notebook)
4. Shift + Enter is how you execute codes in a cell and move on next cell. 5. Ctrl + Enter is how you execute the cell to run codes in them and remain in same cell.
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Downloaded your package from github and unzip done. Could you please detail a bit on how to run this package please.
The codes are written in Python 2 with anaconda distribution with Pandas 0.18 version and fix_yahoo_finance (https://github.com/ranaroussi/fix-yahoo-finance)
1. Assuming that you already have Python installed and using windows first install fix_yahoo_finance. This is the library that helps to fetch data from Yahoo.
Open Command Prompt and type -> pip install fix_yahoo_finance
2. Thats it. Open Command Prompt again and type- jupyter notebook. That shall open the notebook in your default web browser and you will see the directory. Navigate to the files where you have downloaded them (Preferably on desktop).
3. Open the file that you wish to see (.ipynb extension are of Jupyter Notebook)
4. Shift + Enter is how you execute codes in a cell and move on next cell.
5. Ctrl + Enter is how you execute the cell to run codes in them and remain in same cell.
Regards
Is there any update on supertrend code for live markets?
TIA
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def average_directional_movement_index(df, n, n_ADX):
i = 0
UpI = []
DoI = []
while i + 1 <= df.index[-1]:
UpMove = df.loc[i + 1, 'High'] - df.loc[i, 'High']
DoMove = df.loc[i, 'Low'] - df.loc[i + 1, 'Low']
if UpMove > DoMove and UpMove > 0:
UpD = UpMove
else:
UpD = 0
UpI.append(UpD)
if DoMove > UpMove and DoMove > 0:
DoD = DoMove
else:
DoD = 0
DoI.append(DoD)
i = i + 1
i = 0
TR_l = [0]
while i < df.index[-1]:
TR = max(df.loc[i + 1, 'High'], df.loc[i, 'Close']) - min(df.loc[i + 1, 'Low'], df.loc[i, 'Close'])
TR_l.append(TR)
i = i + 1
TR_s = pd.Series(TR_l)
ATR = pd.Series(TR_s.ewm(span=n, min_periods=n).mean())
UpI = pd.Series(UpI)
DoI = pd.Series(DoI)
PosDI = pd.Series(UpI.ewm(span=n, min_periods=n).mean() / ATR)
NegDI = pd.Series(DoI.ewm(span=n, min_periods=n).mean() / ATR)
ADX = pd.Series((abs(PosDI - NegDI) / (PosDI + NegDI)).ewm(span=n_ADX, min_periods=n_ADX).mean(),
name='ADX_' + str(n) + '_' + str(n_ADX))
df = df.join(ADX)
return df