def plot_industry_roc(startDate, endDate):
dates = pd.date_range(startDate, endDate)
symbolList = data.getSubIndustrySymbol()
df = utl.loadPriceData(symbolList, dates)
df_roc = ind.roc(df)
df_roc.fillna(0, inplace=True) # fill NaN to 0
result = np.mean(df_roc)
result = result.where(result > 0)
result = result.where(result >= result['SET'])
maxIndexResult = np.argsort(result)
nameSymbol = [
df_roc.columns[index] for index in reversed(maxIndexResult)
if result[index] > 0
]
print("List of Industry Group and Sector that ROC > SET (ROC)\n",
nameSymbol)
df_norm = utl.normalize_data(df)
utg.plotStock(df_norm,
nameSymbol,
startDate,
endDate,
title='ROC: Industry Group and Sector')
def plot_example():
df_norm = utl.normalize_data(df)
nameSymbol = [
'TRUBB', 'SGP', 'JAS', 'ZMICO', 'BCP', 'VNT', 'SCG', 'DCON', 'PTTGC',
'IT', 'PTTEP'
]
utg.plotStock(df_norm,
nameSymbol,
startDate,
endDate,
title='I am interested')
def brek_new_high():
lasted = df.ix[-1:]
n_period = 30
previous = df.ix[-1 * (n_period + 1):-1]
max_price = previous.max()
result = (lasted - max_price) / max_price * 100
result = result.values[0]
maxIndexResult = np.argsort(result)
nameSymbol = [
df.columns[index] for index in reversed(maxIndexResult)
if result[index] > -0.5
]
print("List of break new high", nameSymbol)
df_norm = utl.normalize_data(df)
utg.plotStock(df_norm,
nameSymbol[0:5],
startDate,
endDate,
title='close to break new high')
utg.plotStock(df_norm,
nameSymbol[6:10],
startDate,
endDate,
title='close to break new high')
utg.plotStock(df_norm,
nameSymbol[-6:-1],
startDate,
endDate,
title='close to break new high')
def max_sharpe_ratio(periods=5):
df_sr = ind.create_dataframe_SR(df, symbolList, window=periods)
df_sr.fillna(0, inplace=True) # fill NaN to 0
df_sr = df_sr.shift(-(periods - 1))
df_sr.dropna(0, inplace=True) # drop NaN at tail
result = np.mean(df_sr)
# Returns the indices that would sort an array
# max values at last element
maxIndexResult = np.argsort(result)
nameSymbol = [
df_sr.columns[index] for index in reversed(maxIndexResult)
if result[index] > 0
]
# save to dump file
pickle.dump(nameSymbol, open("symbol_list.p", "wb"))
print("List max sharpe ratio\n", nameSymbol)
df_norm = utl.normalize_data(df)
utg.plotStock(df_norm, nameSymbol[0:5], startDate, endDate,
'High sharpe ratio')
def max_change(startDate, endDate):
dates = pd.date_range(startDate, endDate)
symbolList = data.getAllSymbol()
df = utl.loadPriceData(symbolList, dates)
day4 = df.ix[-4:] # select last dast in 4 days
day4 = day4.iloc[::-1] # reverse rows
day4 = day4.pct_change() # calcuate percent change of prices
day4 = day4[1:4] # remove NaN in first rows
day3 = day4.fillna(0)
result = day3.mean()
print(day3)
maxIndexResult = np.argsort(result)
nameSymbol = [
day3.columns[index] for index in reversed(maxIndexResult)
if result[index] > 3
]
print("List of high percent change\n", nameSymbol)
df_norm = utl.normalize_data(df)
utg.plotStock(df_norm,
nameSymbol[0:5],
startDate,
endDate,
title='High percent change')