def get_ind_and_signals(self, prices):
short_MA = ind.moving_average(prices['close'], self.short_period)
long_MA = ind.moving_average(prices['close'], self.long_period)
signal = long_MA.copy()
signal[short_MA > long_MA] = 1
signal[short_MA < long_MA] = -1
signal[short_MA == long_MA] = 0
signal[long_MA.isnull()] = 0
indicator = pd.DataFrame({'Timestamp': signal.index, 'short_MA': short_MA, 'long_MA': long_MA})
indicator = indicator.set_index('Timestamp')
indicator = indicator.tz_localize(pytz.timezone('UTC'))
return indicator, signal
def update(path):
df = pd.read_csv(path)
df = indicators.moving_average(df, 15)
df = indicators.exponential_moving_average(df, 30)
df = indicators.relative_strength_index(df, 14)
df = indicators.macd(df, 12, 26)
# print(df)
df = df.astype(float)
f = lambda x: mdates.date2num(datetime.datetime.fromtimestamp(x))
df['Date'] = df['Date'].apply(f)
# print(df['Date'])
return df
def main(instrument, periods, granularity, outfile, daily_alignment):
print(f'Periods : {periods}')
print(f'Instrument : {instrument}')
print(f'Granularity : {granularity}')
print(f'daily alignment: {daily_alignment}')
print(f'Outfile : {outfile}')
if instrument.upper() not in INSTRUMENTS:
raise Exception(f'Invalid instrument {instrument}')
if granularity.upper() not in GRANULARITY:
raise Exception(f'Invalid granularity {granularity}')
instrument = instrument.upper()
granularity = granularity.upper()
debug = True
if not debug:
pd.set_option('display.max_columns', 50)
# pd.set_option('display.max_rows', 500000)
pd.set_option('display.width', 1000)
stock = get_oanda_api(
[instrument],
granularity=granularity,
count=periods,
daily_alignment=daily_alignment,
)
nsize = len(stock[instrument]['Close'])
# Calculate MACD
stock[instrument] = stock[instrument].join(
macd(stock[instrument]['Close'])
)
# Calculate RSI for n = 14
stock[instrument] = stock[instrument].join(
rsi(stock[instrument]['Close'])
)
# Calculate Profile and Loss
stock[instrument] = stock[instrument].join(
pnl(stock[instrument]['Close'])
)
# Calculate MACD Percentile
stock[instrument] = stock[instrument].join(
macd_percentile(stock[instrument]['MACD'])
)
# Calculate RSI Percentile
stock[instrument] = stock[instrument].join(
rsi_percentile(stock[instrument]['RSI'])
)
# Calculate Profile and Loss Percentile
stock[instrument] = stock[instrument].join(
pnl_percentile(stock[instrument]['Profit/Loss'])
)
# Calculate Divergence factor 1 and 2
stock[instrument] = stock[instrument].join(
pd.Series(
(
stock[instrument]['MACD Percentile'] + 0.1 -
stock[instrument]['RSI Percentile']
) / 2.0,
name='Divergence Factor 1'
)
)
stock[instrument] = stock[instrument].join(
pd.Series(
stock[instrument]['Divergence Factor 1'] -
stock[instrument]['PNL Percentile'],
name='Divergence Factor 2'
)
)
# Calculate Divergence factor 3
n = 19
for i in range(nsize):
stock[instrument].loc[i: nsize, 'Macd_20'] = (
stock[instrument]['MACD'].iloc[i] -
stock[instrument]['MACD'].iloc[i - n]
)
stock[instrument].loc[i: nsize, 'Prc_20'] = (
(stock[instrument]['Close'].iloc[i] -
stock[instrument]['Close'].iloc[i - n])
) / stock[instrument]['Close'].iloc[i - n]
stock[instrument].loc[i: nsize, 'Divergence Factor 3'] = (
stock[instrument]['Macd_20'].iloc[i] /
stock[instrument]['Close'].iloc[i]
) - stock[instrument]['Prc_20'].iloc[i]
stock[instrument] = stock[instrument].join(
rsi(stock[instrument]['Close'], 20, name='RSI_20')
)
# Calculate the momentum factors
stock[instrument] = stock[instrument].join(
pnl_n(stock[instrument]['Close'], 10)
)
stock[instrument] = stock[instrument].join(
pnl_n(stock[instrument]['Close'], 30)
)
stock[instrument]['Close_fwd'] = stock[instrument]['Close'].shift(-2)
stock[instrument].loc[-1: nsize, 'Close_fwd'] = stock[instrument]['Close'].iloc[-1]
stock[instrument].loc[-2: nsize, 'Close_fwd'] = stock[instrument]['Close'].iloc[-2]
stock[instrument] = stock[instrument].join(
macd(
stock[instrument]['Close_fwd'],
name='MACD_fwd'
)
)
n = 19
stock[instrument] = stock[instrument].join(
pd.Series(
stock[instrument]['MACD_fwd'].diff(n) - stock[instrument]['MACD'],
name='M_MACD_CHANGE'
)
)
stock[instrument] = stock[instrument].join(
rsi(stock[instrument]['Close_fwd'], n=20, name='RSI_20_fwd')
)
stock[instrument] = stock[instrument].join(
pd.Series(
stock[instrument]['RSI_20_fwd'] - stock[instrument]['RSI_20'],
name='M_RSI_CHANGE'
)
)
# Calculate the ADX, PDI & MDI
_adx, _pdi, _mdi = adx(stock[instrument])
stock[instrument] = stock[instrument].join(_adx)
stock[instrument] = stock[instrument].join(_pdi)
stock[instrument] = stock[instrument].join(_mdi)
# Calculate the Moving Averages: 5, 10, 20, 50, 100
for period in [5, 10, 20, 50, 100]:
stock[instrument] = stock[instrument].join(
moving_average(
stock[instrument]['Close'],
period,
name=f'{period}MA'
)
)
# Calculate the Williams PCTR
stock[instrument] = stock[instrument].join(
williams(stock[instrument])
)
# Calculate the Minmax Range
n = 17
for i in range(nsize):
maxval = stock[instrument]['High'].iloc[i - n: i].max()
minval = stock[instrument]['Low'].iloc[i - n: i].min()
rng = abs(maxval) - abs(minval)
# where is the last price in the range of minumimn to maximum
pnow = stock[instrument]['Close'].iloc[i - n: i]
if len(pnow.iloc[-1: i].values) > 0:
whereinrng = (
(pnow.iloc[-1: i].values[0] - abs(minval)) / rng
) * 100.0
stock[instrument].loc[i: nsize, 'MinMaxPosition'] = whereinrng
stock[instrument].loc[i: nsize, 'High_Price(14)'] = maxval
stock[instrument].loc[i: nsize, 'Low_Price(14)'] = minval
headers = [
'Close',
'adx',
'pdi',
'mdi',
'MACD',
'RSI',
'Divergence Factor 1',
'Divergence Factor 2',
'Divergence Factor 3',
]
stock[instrument].to_csv(
outfile,
columns=headers,
mode='w',
sep=',',
date_format='%d-%b-%Y %r',
)
def Function_for_file_generation():
global specific_insts
global ggpath
ggpath = gpath
debug = False
if not debug:
pd.set_option('display.max_columns', 50)
pd.set_option('display.width', 1000)
# user_input=input("Do You Want Select Instrument Manually:y or n")
# man_inst_names=None
# if user_input=="y":
# man_inst_names=input("Enter Instrument names Separated by Space:")
# man_inst_names = man_inst_names.split()
instruments = INSTRUMENTS
# # instruments = ['AUD_CAD',]
# # instruments = ['AUD_CHF',]
# instruments = ['AUD_CHF', 'AUD_CAD']
# instruments = ['AUD_CAD',]
#data = get_file_data()
stock = get_oanda_api(instruments, granularity='D')
# print (stock['AUD_CAD'])
# stock = get_ig_api(instruments)
# print (stock['AUD_CAD'])
# instruments = data['Close'].columns.values
# # Initialize all assign all instrument data to dataframes
# stock = {}
# for instrument in instruments:
# values = {}
# for key in COLUMNS:
# values[key] = data.get(key, {}).get(instrument)
# values['Date'] = data.get('Date').iloc[:len(values[key]), 0]
# stock[instrument] = pd.DataFrame(values, columns=COLUMNS)
# print(stock[SELECTED_INSTRUMENT])
# return
# Calculate the MACD, RSI and Profit and Loss for all instrument paid
# Also, Calculate the MACD, RSI and Profit and Loss percentile for all
# instruments
instruments_list = []
CCI_list = []
dic_for_all_cci = {}
for instrument in instruments:
nsize = len(stock[instrument]['Close'])
# Calculate MACD
stock[instrument] = stock[instrument].join(
macd(stock[instrument]['Close']))
# Calculate RSI for n = 14
stock[instrument] = stock[instrument].join(
rsi(stock[instrument]['Close']))
#changeInPrice
stock[instrument]["Change In Price"] = change_in_price(
stock[instrument]["Close"].values)
# Calculate Profile and Loss
stock[instrument] = stock[instrument].join(
pnl(stock[instrument]['Close']))
# Calculate MACD Percentile
stock[instrument] = stock[instrument].join(
macd_percentile(stock[instrument]['MACD']))
# Calculate RSI Percentile
stock[instrument] = stock[instrument].join(
rsi_percentile(stock[instrument]['RSI']))
# Calculate Profile and Loss Percentile
stock[instrument] = stock[instrument].join(
pnl_percentile(stock[instrument]['Profit/Loss']))
#Calculate CCI
high = stock[instrument]["High"].values
close = stock[instrument]["Close"].values
low = stock[instrument]["Low"].values
#create instrument dataframe
ccis = talib.CCI(high, low, close, timeperiod=14)
#ccis=list(ccis)
instruments_list.append(instrument)
CCI_list.append(ccis[-1])
dic_for_all_cci[instrument] = ccis
stock[instrument]["CCI"] = ccis
# Calculate Divergence factor 1 and 2
stock[instrument] = stock[instrument].join(
pd.Series((stock[instrument]['MACD Percentile'] + 0.1 -
stock[instrument]['RSI Percentile']) / 2.0,
name='Divergence Factor 1'))
stock[instrument] = stock[instrument].join(
pd.Series(stock[instrument]['Divergence Factor 1'] -
stock[instrument]['PNL Percentile'],
name='Divergence Factor 2'))
# Calculate Divergence factor 3
n = 19
for i in range(nsize):
stock[instrument].loc[i:nsize, 'Macd_20'] = (
stock[instrument]['MACD'].iloc[i] -
stock[instrument]['MACD'].iloc[i - n])
stock[instrument].loc[i:nsize, 'Prc_20'] = (
(stock[instrument]['Close'].iloc[i] -
stock[instrument]['Close'].iloc[i - n])
) / stock[instrument]['Close'].iloc[i - n]
stock[instrument].loc[i:nsize, 'Divergence Factor 3'] = (
stock[instrument]['Macd_20'].iloc[i] /
stock[instrument]['Close'].iloc[i]
) - stock[instrument]['Prc_20'].iloc[i]
stock[instrument] = stock[instrument].join(
rsi(stock[instrument]['Close'], 20, name='RSI_20'))
# Calculate the momentum factors
stock[instrument] = stock[instrument].join(
pnl_n(stock[instrument]['Close'], 10))
stock[instrument] = stock[instrument].join(
pnl_n(stock[instrument]['Close'], 30))
stock[instrument]['Close_fwd'] = stock[instrument]['Close'].shift(-2)
stock[instrument].loc[
-1:nsize, 'Close_fwd'] = stock[instrument]['Close'].iloc[-1]
stock[instrument].loc[
-2:nsize, 'Close_fwd'] = stock[instrument]['Close'].iloc[-2]
stock[instrument] = stock[instrument].join(
macd(stock[instrument]['Close_fwd'], name='MACD_fwd'))
n = 19
stock[instrument] = stock[instrument].join(
pd.Series(stock[instrument]['MACD_fwd'].diff(n) -
stock[instrument]['MACD'],
name='M_MACD_CHANGE'))
stock[instrument] = stock[instrument].join(
rsi(stock[instrument]['Close_fwd'], n=20, name='RSI_20_fwd'))
stock[instrument] = stock[instrument].join(
pd.Series(stock[instrument]['RSI_20_fwd'] -
stock[instrument]['RSI_20'],
name='M_RSI_CHANGE'))
# Calculate the ADX, PDI & MDI
_adx, _pdi, _mdi = adx(stock[instrument])
stock[instrument] = stock[instrument].join(_adx)
stock[instrument] = stock[instrument].join(_pdi)
stock[instrument] = stock[instrument].join(_mdi)
# Calculate the Moving Averages: 5, 10, 20, 50, 100
for period in [5, 10, 20, 50, 100]:
stock[instrument] = stock[instrument].join(
moving_average(stock[instrument]['Close'],
period,
name=f'{period}MA'))
# Calculate the Williams PCTR
stock[instrument] = stock[instrument].join(williams(stock[instrument]))
# Calculate the Minmax Range
n = 17
for i in range(nsize):
maxval = stock[instrument]['High'].iloc[i - n:i].max()
minval = stock[instrument]['Low'].iloc[i - n:i].min()
rng = abs(maxval) - abs(minval)
# where is the last price in the range of minumimn to maximum
pnow = stock[instrument]['Close'].iloc[i - n:i]
if len(pnow.iloc[-1:i].values) > 0:
whereinrng = (
(pnow.iloc[-1:i].values[0] - abs(minval)) / rng) * 100.0
stock[instrument].loc[i:nsize, 'MinMaxPosition'] = whereinrng
stock[instrument].loc[i:nsize, 'High_Price(14)'] = maxval
stock[instrument].loc[i:nsize, 'Low_Price(14)'] = minval
stock[instrument]['Divergence factor Avg'] = (
stock[instrument]['Divergence Factor 1'] +
stock[instrument]['Divergence Factor 2'] +
stock[instrument]['Divergence Factor 3']) / 3.0
stock[instrument]['Momentum Avg'] = (
stock[instrument]['M_MACD_CHANGE'] +
stock[instrument]['M_RSI_CHANGE'] +
stock[instrument]['Profit/Loss_10'] +
stock[instrument]['Profit/Loss_30']) / 4.0
df_instrument = pd.DataFrame()
df_instrument["Open"] = stock[instrument]["Open"]
df_instrument["High"] = stock[instrument]['High']
df_instrument["Low"] = stock[instrument]['Low']
df_instrument["Close"] = stock[instrument]['Close']
df_instrument["Volume"] = stock[instrument]['Volume']
df_instrument["Price"] = stock[instrument]['Close']
df_instrument["Change In Price"] = change_in_price(
stock[instrument]['Close'].values)
df_instrument["CCI"] = stock[instrument]['CCI']
df_instrument["PNL Percentile"] = stock[instrument]['PNL Percentile']
df_instrument["Divergence Factor 1"] = stock[instrument][
'Divergence Factor 1']
df_instrument["Divergence Factor 2"] = stock[instrument][
'Divergence Factor 2']
df_instrument["Divergence Factor 3"] = stock[instrument][
'Divergence Factor 3']
df_instrument["Momentum Factor 1"] = stock[instrument]["M_MACD_CHANGE"]
df_instrument["Momentum Factor 2"] = stock[instrument]['M_RSI_CHANGE']
df_instrument["Momentum Factor 3"] = stock[instrument][
'Profit/Loss_10']
df_instrument["Momentum Factor 4"] = stock[instrument][
'Profit/Loss_30']
df_instrument["RSI"] = stock[instrument]["RSI"]
df_instrument["MACD"] = stock[instrument]["MACD"]
df_instrument["WPCTR"] = stock[instrument]["Williams PCTR"]
df_instrument["pdi"] = stock[instrument]["pdi"]
df_instrument["mdi"] = stock[instrument]["mdi"]
df_instrument["adx"] = stock[instrument]["adx"]
#df_instrument= df_instrument[pd.notnull(df_instrument['CCI'])]
df_instrument = df_instrument.dropna(how="any")
df_instrument["CCI Percentile"] = cci_percentile(df_instrument["CCI"])
df_instrument["Divergence Factor 4"] = df_instrument[
"CCI Percentile"] - df_instrument["PNL Percentile"]
df_instrument['Divergence Factor 1 Rank'] = rank_formulation(
df_instrument['Divergence Factor 1'].values)
df_instrument['Divergence Factor 2 Rank'] = rank_formulation(
df_instrument['Divergence Factor 2'].values)
df_instrument['Divergence Factor 3 Rank'] = rank_formulation(
df_instrument['Divergence Factor 3'].values)
df_instrument['Divergence Factor 4 Rank'] = rank_formulation(
df_instrument['Divergence Factor 4'].values)
df_instrument['DF Avg Rank'] = (
df_instrument['Divergence Factor 1 Rank'] +
df_instrument['Divergence Factor 2 Rank'] +
df_instrument['Divergence Factor 3 Rank'] +
df_instrument['Divergence Factor 4 Rank']) / 4.0
df_instrument['Momentum Factor 1 Rank'] = rank_formulation(
df_instrument['Momentum Factor 1'].values)
df_instrument['Momentum Factor 2 Rank'] = rank_formulation(
df_instrument['Momentum Factor 2'].values)
df_instrument['Momentum Factor 3 Rank'] = rank_formulation(
df_instrument['Momentum Factor 3'].values)
df_instrument['Momentum Factor 4 Rank'] = rank_formulation(
df_instrument['Momentum Factor 4'].values)
df_instrument['MF Avg Rank'] = (
df_instrument['Momentum Factor 1 Rank'] +
df_instrument['Momentum Factor 2 Rank'] +
df_instrument['Momentum Factor 3 Rank'] +
df_instrument['Momentum Factor 4 Rank']) / 4.0
df_instrument["% Rank of DF Avgs"] = rank_formulation(
df_instrument['DF Avg Rank'].values)
df_instrument["% Rank of MF Avgs"] = rank_formulation(
df_instrument['MF Avg Rank'].values)
df_instrument = df_instrument[[
'Open',
'High',
'Low',
'Close',
'Volume',
'Price',
'Change In Price',
'Divergence Factor 1',
'Divergence Factor 2',
'Divergence Factor 3',
'Divergence Factor 4',
'DF Avg Rank',
'% Rank of DF Avgs',
'Divergence Factor 1 Rank',
'Divergence Factor 2 Rank',
'Divergence Factor 3 Rank',
'Divergence Factor 4 Rank',
'Momentum Factor 1',
'Momentum Factor 2',
'Momentum Factor 3',
'Momentum Factor 4',
'Momentum Factor 1 Rank',
'Momentum Factor 2 Rank',
'Momentum Factor 3 Rank',
'Momentum Factor 4 Rank',
'MF Avg Rank',
'% Rank of MF Avgs',
'RSI',
'MACD',
'WPCTR',
'CCI',
'CCI Percentile',
'PNL Percentile',
'pdi',
'mdi',
'adx',
]]
df_instrument.to_csv(gpath + "all_folders/" + instrument + ".csv")
ccis_df = pd.DataFrame(dic_for_all_cci)
cci_percentile_list = []
dic = {"Instrument": instruments_list, "CCI": CCI_list}
new_df = pd.DataFrame(dic)
cci_percentile_list = cci_percentile(new_df["CCI"]).to_list()
#sys.exit()
# calculate the aggregrate for each oeruod
# calculate the Divergence_Macd_Prc_Rank
for nrow in range(nsize):
row = [
stock[instrument]['Divergence Factor 3'].iloc[nrow]
for instrument in instruments
]
series = pd.Series(row).rank() / len(row)
for i, instrument in enumerate(instruments):
stock[instrument].loc[nrow:nsize,
'Divergence_Macd_Prc_Rank'] = series.iloc[i]
# calculate the Divergence and Momentum average rank
indices = [instrument for instrument in instruments]
columns = [
'Price',
"Change In Price",
'Divergence Factor 1',
'Divergence Factor 2',
'Divergence Factor 3',
'Divergence Factor 1 Rank',
'Divergence Factor 2 Rank',
'Divergence Factor 3 Rank',
'M_MACD_CHANGE',
'M_RSI_CHANGE',
'Profit/Loss_10',
'Profit/Loss_30',
'M_MACD_CHANGE Rank',
'M_RSI_CHANGE Rank',
'Profit/Loss_10 Rank',
'Profit/Loss_30 Rank',
'MF Avg Rank',
'% Rank of MF Avgs',
'MinMaxPosition',
'RSI',
'WPCTR',
'pdi',
'mdi',
'adx',
'High_Price(14)',
'Low_Price(14)',
'5MA',
'10MA',
'20MA',
'50MA',
'100MA',
"MACD",
'PNL Percentile',
"DF Avg Rank",
"% Rank of DF Avgs",
]
periods = []
for i in range(nsize):
period = []
for instrument in instruments:
period.append([
stock[instrument]['Close'].iloc[i],
stock[instrument]["Change In Price"].iloc[i],
stock[instrument]['Divergence Factor 1'].iloc[i],
stock[instrument]['Divergence Factor 2'].iloc[i],
stock[instrument]['Divergence Factor 3'].iloc[i],
None,
None,
None,
stock[instrument]['M_MACD_CHANGE'].iloc[i],
stock[instrument]['M_RSI_CHANGE'].iloc[i],
stock[instrument]['Profit/Loss_10'].iloc[i],
stock[instrument]['Profit/Loss_30'].iloc[i],
None,
None,
None,
None,
None,
None,
stock[instrument]['MinMaxPosition'].iloc[i],
stock[instrument]['RSI'].iloc[i],
stock[instrument]['Williams PCTR'].iloc[i],
stock[instrument]['pdi'].iloc[i],
stock[instrument]['mdi'].iloc[i],
stock[instrument]['adx'].iloc[i],
stock[instrument]['High_Price(14)'].iloc[i],
stock[instrument]['Low_Price(14)'].iloc[i],
stock[instrument]['5MA'].iloc[i],
stock[instrument]['10MA'].iloc[i],
stock[instrument]['20MA'].iloc[i],
stock[instrument]['50MA'].iloc[i],
stock[instrument]['100MA'].iloc[i],
stock[instrument]["MACD"].iloc[i],
stock[instrument]['PNL Percentile'].iloc[i],
None,
None,
])
df = pd.DataFrame(data=period, index=indices, columns=columns)
df['Divergence Factor 1 Rank'] = rank_formulation(
df["Divergence Factor 1"].values)
df['Divergence Factor 2 Rank'] = rank_formulation(
df["Divergence Factor 2"].values)
df['Divergence Factor 3 Rank'] = rank_formulation(
df["Divergence Factor 3"].values)
df['Momentum Factor 1 Rank'] = rank_formulation(
df['M_MACD_CHANGE'].values)
df['Momentum Factor 2 Rank'] = rank_formulation(
df['M_RSI_CHANGE'].values)
df['Momentum Factor 3 Rank'] = rank_formulation(
df['Profit/Loss_10'].values)
df['Momentum Factor 4 Rank'] = rank_formulation(
df['Profit/Loss_30'].values)
df['MF Avg Rank'] = (
df['Momentum Factor 1 Rank'] + df['Momentum Factor 1 Rank'] +
df['Momentum Factor 1 Rank'] + df['Momentum Factor 1 Rank']) / 4.0
df['% Rank of MF Avgs'] = rank_formulation(df['MF Avg Rank'].values)
#df.to_excel("target_data.xlsx")
periods.append(df)
pnl_percentile_nparaay = np.array(df["PNL Percentile"].values)
cci_percentile_nparray = cci_percentile_list
divergent_factor_4 = cci_percentile_nparray - pnl_percentile_nparaay
df["CCI"] = CCI_list
df["CCI Percentile"] = cci_percentile_list
df["Divergence Factor 4"] = divergent_factor_4
df['Divergence Factor 4 Rank'] = rank_formulation(
df['Divergence Factor 1'].values)
df['DF Avg Rank'] = (
df['Divergence Factor 1 Rank'] + df['Divergence Factor 2 Rank'] +
df['Divergence Factor 3 Rank'] + df['Divergence Factor 4 Rank']) / 4.0
df["% Rank of DF Avgs"] = rank_formulation(df['DF Avg Rank'].values)
df = df[[
'Price',
'Change In Price',
'Divergence Factor 1',
'Divergence Factor 2',
'Divergence Factor 3',
'Divergence Factor 4',
'Divergence Factor 1 Rank',
'Divergence Factor 2 Rank',
'Divergence Factor 3 Rank',
'Divergence Factor 4 Rank',
'DF Avg Rank',
'% Rank of DF Avgs', #'Momentum Factor 1','Momentum Factor 2','Momentum Factor 3','Momentum Factor 4',
#'Momentum Factor 1 Rank','Momentum Factor 2 Rank','Momentum Factor 3 Rank','Momentum Factor 4 Rank','MF Avg Rank', '% Rank of MF Avgs',
'M_MACD_CHANGE',
'M_RSI_CHANGE',
'Profit/Loss_10',
'Profit/Loss_30',
'M_MACD_CHANGE Rank',
'M_RSI_CHANGE Rank',
'Profit/Loss_10 Rank',
'Profit/Loss_30 Rank',
'MF Avg Rank',
'% Rank of MF Avgs',
'MinMaxPosition',
'RSI',
'MACD',
'WPCTR',
'CCI',
'CCI Percentile',
'PNL Percentile',
'pdi',
'mdi',
'adx',
'High_Price(14)',
'Low_Price(14)',
'5MA',
'10MA',
'20MA',
'50MA',
'100MA'
]]
df.to_excel(gpath + "all_folders/" + "target_data.xlsx")
df.sort_values(by="% Rank of DF Avgs", inplace=True)
df.to_excel(gpath + "all_folders/" + "ordered_target_data.xlsx")
top5, last5 = instrument_selection_rules(df)
specific_insts = None
specific_insts = top5 + last5
dflist1 = []
if specific_insts is not None:
'''dflist1=Graph_Plots_For_Individual_Instrument(specific_insts,False)
dflist1.to_csv(gpath+"all_folders"+"/"+"ins_ind_flag.csv")
dfDiverge=dflist.copy()
dfDiverge=dfDiverge.loc[dfDiverge['imp_var'].isin(['Divergence Factor 1','Divergence Factor 2','Divergence Factor 3','Divergence Factor 4'])]
dfDiverge.reset_index(drop=True,inplace=True)
dfDiverge.to_csv(gpath+"all_folders"+"/"+"selected_ins_ind_flag.csv")'''
for rule_instrument in specific_insts:
data = pd.read_csv(gpath + "all_folders/" + rule_instrument +
".csv",
index_col="Date")
data.to_csv(gpath + "rule_select_inst/" + rule_instrument + ".csv")
def plot_graph(self):
from_ = self.date_picker.date()
from_ = datetime.date(day=from_.day(), month=from_.month(),
year=from_.year())
to = from_ + datetime.timedelta(days=1)
from_ = from_.strftime("%Y%m%d")
to = to.strftime("%Y%m%d")
query = self.db.trades.find({'code': self.code, 'datetime': {'$gte':
from_, '$lt': to}}).sort('datetime')
trades = [(datetime.datetime.strptime(trade['datetime'], "%Y%m%d%H%M%S"),
float(trade['open'])) for trade in query]
if len(trades) < 2:
self.clear()
self.axes1.plot()
self.axes2.plot()
self.axes3.plot()
self.redraw()
return
heads = ['DateTime', 'Open',]
r = indicators.get_records(heads, trades)
self.clear()
for ax in self.axes1, self.axes2, self.axes3:
if ax != self.axes3:
for label in ax.get_xticklabels():
label.set_visible(False)
else:
for label in ax.get_xticklabels():
label.set_rotation(40)
label.set_horizontalalignment('right')
ax.xaxis.set_major_formatter(mdates.DateFormatter('%H:%M'))
try:
rsi = indicators.relative_strength(r.open)
fillcolor = 'darkgoldenrod'
textsize = 9
self.axes1.plot(r.datetime, rsi, fillcolor)
self.axes1.fill_between(r.datetime, rsi, 70, where=(rsi>=70), facecolor=fillcolor, edgecolor=fillcolor)
self.axes1.fill_between(r.datetime, rsi, 30, where=(rsi<=30), facecolor=fillcolor, edgecolor=fillcolor)
except:
self.axes1.plot(r.datetime, r.open, color='black', label='Open')
self.axes1.axhline(70, color=fillcolor)
self.axes1.axhline(30, color=fillcolor)
self.axes1.text(0.6, 0.9, '>70 = overbought', va='top',
transform=self.axes1.transAxes, fontsize=textsize)
self.axes1.text(0.6, 0.1, '<30 = oversold', transform=self.axes1.transAxes, fontsize=textsize)
self.axes1.set_ylim(0, 100)
self.axes1.set_yticks([30,70])
self.axes1.text(0.025, 0.95, 'RSI (14)', va='top',
transform=self.axes1.transAxes, fontsize=textsize)
try:
ma = indicators.moving_average(r.open, 10, type_='simple')
self.axes2.plot(r.datetime, ma, color='blue', lw=2, label='MA (10)')
except:
pass
try:
ma = indicators.moving_average(r.open, 20, type_='simple')
self.axes2.plot(r.datetime, ma, color='red', lw=2, label='MA (20)')
except:
pass
self.axes2.plot(r.datetime, r.open, color='black', label='Open')
props = font_manager.FontProperties(size=8)
self.axes2.legend(loc='best', shadow=True, fancybox=True, prop=props)
fillcolor = 'darkslategrey'
nslow, nfast, nema = 26, 12, 9
try:
emaslow, emafast, macd = indicators.moving_average_convergence(r.open, nslow=nslow, nfast=nfast)
ema9 = indicators.moving_average(macd, nema, type_='exponential')
self.axes3.plot(r.datetime, macd, color='black', lw=2)
self.axes3.plot(r.datetime, ema9, color='blue', lw=1)
self.axes3.fill_between(r.datetime, macd-ema9, 0, alpha=0.5, facecolor=fillcolor, edgecolor=fillcolor)
except:
self.axes3.plot(r.datetime, r.open, color='black', label='Open')
self.axes3.text(0.025, 0.95, 'MACD (%d, %d, %d)'%(nfast, nslow, nema), va='top',
transform=self.axes3.transAxes, fontsize=textsize)
self.redraw()
def get_mov_avg(close_price):
mov_avg_20 = indicators.moving_average(close_price, window=20)
mov_avg_60 = indicators.moving_average(close_price, window=60)
mov_avg_100 = indicators.moving_average(close_price, window=100)
return mov_avg_20, mov_avg_60, mov_avg_100