def analysis_stocks_RTS(start_date,
end_date,
margin=50,
factor=0.325,
investment_size=200):
df_result = pd.read_csv('dataRTS.csv', index_col='Ticker')
df_result = df_result.sort_index()
symbols = df_result.index
dates = pd.date_range(start_date, end_date) # date range as index
df_data = get_data_us(symbols, dates,
benchmark=None) # get data for each symbol
# Fill missing values
fill_missing_values(df_data)
# return prices
# df_result['Commision'] = prices['Commision']
df_result['Spread'] = df_result['Spread'] * factor
df_result['Close'] = df_data[symbols].iloc[-1, :].values
df_result['MinNbStock'] = df_result['Lot'] * 0.01
df_result['PriceStockMarg'] = df_result['MinNbStock'] * df_result[
'Close'] / margin
df_result['NbMaxVol'] = round(
investment_size / df_result['PriceStockMarg'], 0)
df_result['MaxVolLot'] = round(
investment_size / df_result['PriceStockMarg'] * 0.01, 2)
df_result['CommMin'] = (
df_result['Spread'] - df_result['Buy'] -
df_result['Sell']) / df_result['Lot'] * df_result['MinNbStock']
df_result['Comm_Trade'] = (
df_result['Spread'] - df_result['Buy'] -
df_result['Sell']) / df_result['Lot'] * df_result['NbMaxVol']
df_result['Comm_Price'] = round(
df_result['CommMin'] / df_result['MinNbStock'] /
df_result['Close'].values * 100, 3)
df_result['Comm_Invest'] = round(
df_result['Comm_Trade'] / investment_size * 100, 3)
relative_strength = 40*df_data[symbols].pct_change(periods = 63).fillna(0) \
+ 20*df_data[symbols].pct_change(periods = 126).fillna(0) \
+ 20*df_data[symbols].pct_change(periods = 189).fillna(0) \
+ 20*df_data[symbols].pct_change(periods = 252).fillna(0)
df_result['RSW'] = relative_strength.iloc[-1, :].values
return df_result, df_data
def my_portfolio(start="2018-7-10", end="2018-10-16"):
dates = pd.date_range(start, end)
symbols = [
'PHC', 'MBB', 'GEX', 'MBS', 'NDN', 'PNJ', 'STB', 'PVD', 'VRC', 'VIX'
]
# symbols = ['PHC','MBB','GEX', 'MBS']
df_data = get_data(symbols, dates,
benchmark='^VNINDEX') # get data for each symbol
fill_missing_values(df_data)
plot_normalized_data(df_data,
title=" Daily porfolio value with VNINDEX ",
xlabel="Date",
ylabel=" Normalized price ")
def passive_strategy(start_date, end_date, market="SPY"):
symbols = getliststocks(typestock=market)
dates = pd.date_range(start_date, end_date) # date range as index
df_data = get_data_us(symbols, dates,
benchmark=market) # get data for each symbol
df_volume = get_data_us(symbols, dates, benchmark=market,
colname='Volume') # get data for each symbol
df_high = get_data_us(symbols, dates, benchmark=market, colname='High')
df_low = get_data_us(symbols, dates, benchmark=market, colname='Low')
df_value = (df_volume * df_data).fillna(0)
valueM30 = df_value.rolling(window=30).mean()
vol_mean = pd.Series(df_volume.mean(), name='Volume')
max_high = pd.Series(df_high.max(), name='MaxHigh')
min_low = pd.Series(df_low.min(), name='MinLow')
cpm = pd.Series(max_high / min_low, name='CPM')
value_mean = pd.Series(df_value.mean(), name='Value')
# Fill missing values
fill_missing_values(df_data)
# Assess the portfolio
allocations, cr, adr, sddr, sr = optimize_portfolio(sd=start_date,
ed=end_date,
syms=symbols,
benchmark=market,
country='US',
gen_plot=True)
# Print statistics
print("Start Date:", start_date)
print("End Date:", end_date)
print("Symbols:", symbols)
print("Optimal allocations:", allocations)
print("Sharpe Ratio:", sr)
print("Volatility (stdev of daily returns):", sddr)
print("Average Daily Return:", adr)
print("Cumulative Return:", cr)
investment = 50000000
df_result = pd.DataFrame(index=symbols)
df_result['Opt allocs'] = allocations
df_result['Cash'] = allocations * investment
df_result['Close'] = df_data[symbols].iloc[-1, :].values
df_result['Volume'] = df_volume[symbols].iloc[-1, :].values
df_result['VolumeMean'] = vol_mean[symbols]
df_result['Value'] = df_result['Close'] * df_result['Volume']
df_result['ValueMean'] = value_mean[symbols]
df_result['ValueMA30'] = valueM30[symbols].iloc[-1, :].values
# df_result['MaxH'] = max_high
# df_result['MinL'] = min_low
df_result['CPM'] = cpm[symbols]
df_result['Shares'] = round(
df_result['Cash'] / df_result['Close'].values / 1000, 0)
df_result['Volatility'] = df_data[symbols].pct_change().std()
alpha_beta = analysis_alpha_beta(df_data, symbols, market)
df_result['Alpha'] = alpha_beta['Alpha']
df_result['Beta'] = alpha_beta['Beta']
relative_strength = 40*df_data[symbols].pct_change(periods = 63).fillna(0) \
+ 20*df_data[symbols].pct_change(periods = 126).fillna(0) \
+ 20*df_data[symbols].pct_change(periods = 189).fillna(0) \
+ 20*df_data[symbols].pct_change(periods = 252).fillna(0)
df_result['RSW'] = relative_strength.iloc[-1, :].values
return df_result, df_data
def passive_strategy(start_date, end_date, market="^VNINDEX", symbols=None):
if symbols == None:
symbols = getliststocks(typestock=market)
dates = pd.date_range(start_date, end_date) # date range as index
df_data = get_data(symbols, dates,
benchmark=market) # get data for each symbol
df_volume = get_data(symbols, dates, benchmark=market,
colname='<Volume>') # get data for each symbol
df_high = get_data(symbols, dates, benchmark=market, colname='<High>')
df_low = get_data(symbols, dates, benchmark=market, colname='<Low>')
# covariance = numpy.cov(asset , SPY)[0][1]
# variance = numpy.var(asset)
#
# beta = covariance / variance
df_volume = df_volume.fillna(0)
df_value = (df_volume * df_data).fillna(0)
valueM30 = df_value.rolling(window=30).mean()
vol_mean = pd.Series(df_volume.mean(), name='Volume')
max_high = pd.Series(df_high.max(), name='MaxHigh')
min_low = pd.Series(df_low.min(), name='MinLow')
cpm = pd.Series(max_high / min_low, name='CPM')
value_mean = pd.Series(df_value.mean(), name='ValueMean')
# Fill missing values
fill_missing_values(df_data)
# Assess the portfolio
allocations, cr, adr, sddr, sr = optimize_portfolio(sd=start_date,
ed=end_date,
syms=symbols,
benchmark=market,
gen_plot=True)
# Print statistics
print("Start Date:", start_date)
print("End Date:", end_date)
print("Symbols:", symbols)
print("Optimal allocations:", allocations)
print("Sharpe Ratio:", sr)
print("Volatility (stdev of daily returns):", sddr)
print("Average Daily Return:", adr)
print("Cumulative Return:", cr)
investment = 50000000
df_result = pd.DataFrame(index=symbols)
df_result['Opt allocs'] = allocations
df_result['Cash'] = allocations * investment
df_result['Close'] = df_data[symbols].iloc[-1, :].values
df_result['PCT_Change'] = 100 * (df_data[symbols].iloc[-1, :].values -
df_data[symbols].iloc[0, :].values
) / df_data[symbols].iloc[0, :].values
df_result['Volume'] = df_volume[symbols].iloc[-1, :].values
df_result['VolumeMean'] = vol_mean[symbols]
df_result['Value'] = df_result['Close'] * df_result['Volume']
df_result['ValueMean'] = value_mean[symbols]
df_result['ValueMA30'] = valueM30[symbols].iloc[-1, :].values
# df_result['MaxH'] = max_high
# df_result['MinL'] = min_low
df_result['CPM'] = cpm[symbols]
df_result['Shares'] = round(
df_result['Cash'] / df_result['Close'].values / 1000, 0)
df_result['Volatility'] = df_data[symbols].pct_change().std()
df_result['PCT_Change0D'] = df_data[symbols].pct_change().iloc[
-1, :].values * 100
df_result['PCT_Change1D'] = df_data[symbols].pct_change().iloc[
-2, :].values * 100
df_result['PCT_Change2D'] = df_data[symbols].pct_change().iloc[
-3, :].values * 100
alpha_beta = analysis_alpha_beta(df_data, symbols, market)
df_result['Alpha'] = alpha_beta['Alpha']
df_result['Beta'] = alpha_beta['Beta']
relative_strength = 40*df_data[symbols].pct_change(periods = 63).fillna(0) \
+ 20*df_data[symbols].pct_change(periods = 126).fillna(0) \
+ 20*df_data[symbols].pct_change(periods = 189).fillna(0) \
+ 20*df_data[symbols].pct_change(periods = 252).fillna(0)
relative_strength1M = 100 * df_data[symbols].pct_change(
periods=21).fillna(0)
relative_strength2M = 100 * df_data[symbols].pct_change(
periods=42).fillna(0)
df_result['RSW'] = relative_strength.iloc[-1, :].values
df_result['RSW1M'] = relative_strength1M.iloc[-1, :].values
df_result['RSW2M'] = relative_strength2M.iloc[-1, :].values
marketVNI = df_data[symbols].pct_change()
advances = marketVNI[marketVNI > 0]
declines = marketVNI[marketVNI <= 0]
dec = advances.isnull().sum(axis=1)
adv = declines.isnull().sum(axis=1)
df_market = pd.DataFrame(index=marketVNI.index)
df_market[market + 'Volume'] = df_volume[market]
df_market[market + 'PCT_Volume'] = df_volume[market].pct_change() * 100
df_market[market + 'PCT_Index'] = df_data[market].pct_change() * 100
# df_market['Adv'] = adv
# df_market['Dec'] = dec
df_market[market + 'Adv_Dec'] = adv - dec
df_market[market + 'Dec/Adv'] = dec / adv
strength = pd.Series(index=marketVNI.index)
strength[(df_market[market + 'Adv_Dec'] > 0)
& (df_market[market + 'PCT_Index'] > 0)] = 1
strength[(df_market[market + 'Adv_Dec'] < 0)
& (df_market[market + 'PCT_Index'] < 0)] = -1
strength[(df_market[market + 'Adv_Dec'] < 0)
& (df_market[market + 'PCT_Index'] > 0)] = 0
df_market[market + 'Strength'] = strength
# np.where((df_data[symbols].pct_change() > 0), 1, -1)
return df_result, df_data, df_market
def analysis_market(tickers,
start,
end,
update=False,
market="^VNINDEX",
source="cp68"):
if tickers == None:
tickers = getliststocks(typestock=market)
if update:
end_date = datetime.datetime.today()
dates = pd.date_range(start, end_date) # date range as index
df_data = pd.DataFrame(index=dates)
df_volume = pd.DataFrame(index=dates)
else:
dates = pd.date_range(start, end) # date range as index
df_data = pd.DataFrame(index=dates)
df_volume = pd.DataFrame(index=dates)
for ticker in tickers:
# print(" Analysing ..." , ticker)
# try:
#
df = process_data(ticker=ticker,
start=start,
end=end,
realtime=update,
source=source)
# print(df_temp.head())
df_data = df_data.join(df['Close'])
df_data = df_data.rename(columns={'Close': ticker})
df_volume = df_volume.join(df['Volume'])
df_volume = df_volume.rename(columns={'Volume': ticker})
# except Exception as e:
# print (e)
# print("Error in reading symbol: ", ticker)
# pass
fill_missing_values(df_data)
fill_missing_values(df_volume)
marketVNI = df_data[tickers].pct_change()
advances = marketVNI[marketVNI > 0]
declines = marketVNI[marketVNI <= 0]
dec = advances.isnull().sum(axis=1)
adv = declines.isnull().sum(axis=1)
df_market = pd.DataFrame(index=marketVNI.index)
# df_market[market+'Volume'] = df_volume[market]
# df_market[market+'PCT_Volume'] = df_volume[market].pct_change() *100
# df_market[market+'PCT_Index'] = df_data[market].pct_change() *100
# df_market[market+'Adv_Dec'] = adv - dec
# df_market[market+'Dec/Adv'] = dec/adv
# strength = pd.Series(index = marketVNI.index)
# strength[(df_market[market+'Adv_Dec']> 0) & (df_market[market+'PCT_Index'] > 0)] = 1
# strength[(df_market[market+'Adv_Dec']< 0) & (df_market[market+'PCT_Index'] < 0)] = -1
# strength[(df_market[market+'Adv_Dec']< 0) & (df_market[market+'PCT_Index'] > 0)] = 0
# df_market[market+'Strength'] = strength
return df_market
def passive_strategy(start_date,
end_date,
market=None,
symbols=None,
realtime=True,
source='yahoo'):
if symbols == None:
symbols = getliststocks(typestock=market)
if realtime:
end_date = datetime.datetime.today()
dates = pd.date_range(start_date, end_date) # date range as index
df_data = get_data(symbols,
dates,
benchmark=market,
colname='Adj Close',
realtime=realtime,
source=source) # get data for each symbol
# Fill missing values
fill_missing_values(df_data)
df_volume = get_data(symbols,
dates,
benchmark=market,
colname='Volume',
realtime=realtime,
source=source) # get data for each symbol
df_rsi = get_RSI(symbols, df_data)
df_volume = df_volume.fillna(0)
df_result = pd.DataFrame(index=symbols)
df_result['Ticker'] = symbols
df_result['Close'] = df_data[symbols].iloc[-1, :].values
df_result['PCT_C'] = 100 * (df_data[symbols].iloc[-1, :].values -
df_data[symbols].iloc[0, :].values
) / df_data[symbols].iloc[0, :].values
df_result['Volume'] = df_volume[symbols].iloc[
-1, :].values + df_volume[symbols].iloc[-2, :].values
df_result['Value'] = df_result['Close'] * df_result['Volume']
df_result['Volatility'] = df_data[symbols].pct_change().std()
df_result['PCT_3D'] = df_data[symbols].pct_change().iloc[
-4, :].values * 100
df_result['PCT_2D'] = df_data[symbols].pct_change().iloc[
-3, :].values * 100
df_result['PCT_1D'] = df_data[symbols].pct_change().iloc[
-2, :].values * 100
df_result['PCT_0D'] = df_data[symbols].pct_change().iloc[
-1, :].values * 100
relative_strength = 40*df_data[symbols].pct_change(periods = 63).fillna(0) \
+ 20*df_data[symbols].pct_change(periods = 126).fillna(0) \
+ 20*df_data[symbols].pct_change(periods = 189).fillna(0) \
+ 20*df_data[symbols].pct_change(periods = 252).fillna(0)
relative_strength1M = 100 * df_data[symbols].pct_change(
periods=21).fillna(0)
relative_strength2M = 100 * df_data[symbols].pct_change(
periods=42).fillna(0)
df_result['RSW'] = relative_strength.iloc[-1, :].values
df_result['RSW1M'] = relative_strength1M.iloc[-1, :].values
df_result['RSW2M'] = relative_strength2M.iloc[-1, :].values
df_result['RSI'] = df_rsi[symbols].iloc[-1, :].values
return df_result, df_data