def toscheme(self, val, shallow=False):
"Convert a Python value to a Scheme value."
if callable(val):
val = self._wrap_python_callable(val, shallow)
return expressions.makePrimitiveProcedure(val)
if val is True:
return symbol.true
if val is False:
return symbol.false
if type(val) is schemepy.types.Symbol:
return symbol.Symbol(val.name)
if type(val) is schemepy.types.Cons:
car = val.car
cdr = val.cdr
if not shallow:
car = self.toscheme(car)
cdr = self.toscheme(cdr)
return pair.cons(car, cdr)
if isinstance(val, list):
lst = pair.NIL
for el in reversed(val):
if not shallow:
el = self.toscheme(el)
lst = pair.cons(el, lst)
return lst
if isinstance(val, dict):
lst = pair.NIL
for key, value in val.items():
key = self.toscheme(key)
if not shallow:
value = self.toscheme(value)
lst = pair.cons(pair.cons(key, value), lst)
return lst
return val
def d_m_avg_s(my_symbol, m_avg_len):
start_date = datetime.strptime(
self.trading_days[0], "%Y%M%d") - timedelta(days=300)
mysymbol = symbol.Symbol(my_symbol, start_date,
self.trading_days[-1])
ts = mysymbol.close.derivative_m_avg(date, date, m_avg_len)
return ts.data[0]
def roc_s(my_symbol, hist_len):
my_symbol = symbol.Symbol(my_symbol, self.trading_days[0],
self.trading_days[-1])
end_index = self.trading_days.index(date)
start_index = end_index - hist_len
if start_index >= 0:
return my_symbol.close.roc(date, hist_len)
def load_symbol_object(interval):
'''Load stock historicl prices with default initial values'''
# Default values initialised
input_file_name = '.\\..\\..\\Data Sets\\Yahoo\\nifty50list.csv'
symbol_list = pd.read_csv(input_file_name)
# Create a symbol object with the historical data of the stocks in input file
symbol = s.Symbol(symbol_list=symbol_list, interval=interval)
return symbol
def load_symbol_object(interval):
'''Load stock historicl prices with default initial values'''
# Default values initialised
symbols_file = '.\\..\\..\\Github\\Data Sets\\Quandl\\NSE100_Symbols.csv'
symbol_list = pd.read_csv(symbols_file)
# Create a symbol object with the historical data of the stocks in input file
symbol = s.Symbol(symbol_list=symbol_list, interval=interval)
return symbol
def GenerateSymbols(self):
i = 0
while i < len(self.values):
#make new symbol object and pass the value,frequency and Probability to it
self.symbols.append(
symbol.Symbol(
self.values[i], self.message.count(self.values[i]),
self.message.count(self.values[i]) / len(self.message)))
i = i + 1
def test_get_price_alphavantage_json(self):
symb_vt = symbol.Symbol('vt')
self.assertEqual(symb_vt.get_price('2018-06-14'), (76.07, 76.1790, 75.87, 75.97))
self.assertEqual(symb_vt.get_price('20180614'), (76.07, 76.1790, 75.87, 75.97))
self.assertIsNone(symb_vt.get_price('20180630'))
self.assertEqual(symb_vt.get_price(('20180601', '20180615')), (76.26, 76.2800, 75.28, 75.65))
self.assertEqual(symb_vt.get_price(('20180402', '20180614')), (73.39, 76.2800, 71.60, 75.97))
self.assertIsNone(symb_vt.get_price(('20180614', '20180630')))
def close_m_avg_up(my_symbol, m_avg, hist_len):
m_avg_symbol = symbol.Symbol(my_symbol,
self.trading_days[0],
self.trading_days[-1])
end_index = self.trading_days.index(date)
start_index = end_index - max([m_avg, hist_len])
if start_index >= 0:
close_m_avg = m_avg_symbol.close.m_avg(
self.trading_days[start_index], date, m_avg)
return close_m_avg.monotonous_up(date, hist_len)
def HuffmanCodes(self, arr):
print('huffman hufman codes')
self.symbarr = arr
heapq.heapify(self.symbarr)
while (len(self.symbarr) != 1):
#get the two smallest symbols' frequencies
l = heapq.heappop(self.symbarr)
r = heapq.heappop(self.symbarr)
#construct a new symbol with frequency = to the summation of the two symbols
#value is 'a' dummy valuue just to know that it is a constructed symbol
node = symbol.Symbol('a', l.frequency + r.frequency,
l.probability + r.probability) # cheeeeeck
#keep the two symbols in the left and the right of the new node
node.left = l
node.right = r
#push the new node
heapq.heappush(self.symbarr, node)
printCodes(self.map, self.symbarr[0], "")
def makeAnd(clauses):
return pair.cons(symbol.Symbol("AND"), clauses)
data['document'] = ''
elif len(data['document']) > 0:
if os.path.isfile(data['document']):
data['document'] = os.path.join(
document_prefix, data['document'])
else:
print "Warning: '" + data[
'document'] + "' not found"
data['document'] = ''
if 'section' not in data:
data['section'] = ''
firstElement = True
sym = symbol.Symbol(data['name'], data['reference'],
data['footprint'], data['alias'],
data['description'],
data['keywords'], data['document'])
last_name = data['name']
if 'unit' not in data:
data['unit'] = "0"
unit = int(data['unit'])
if os.path.isfile(template_file):
sym.load(template_file, unit,
symbol.representation.normal, data,
firstElement)
elif os.path.isfile(table_file):
sym.fromCSV(table_file, unit, data['section'],
unit != 0)
#if not unit and 'value' in data:
def gen_ts_fixture():
# time for methods to start at
m_start = '20030102'
# time series end time
end = '20091009'
# open database
db = anydbm.open('test_time_series.db', 'c')
# add time series to database
security = symbol.Symbol('AMZN', '20000103', end)
ts = security.close
db[ 'time_series_1' ] = pickle.dumps(ts)
# add dates to database
db[ 'm_start' ] = m_start
db[ 'end' ] = end
# add method returns to database
db['max'] = pickle.dumps(ts.max(m_start, end))
db['min'] = pickle.dumps(ts.min(m_start, end))
db['sum'] = pickle.dumps(ts.sum(m_start, end))
db['m_sum'] = pickle.dumps(ts.m_sum(m_start, end, 10))
db['avg'] = pickle.dumps(ts.avg(m_start, end))
db['m_avg'] = pickle.dumps(ts.m_avg(m_start, end, 10))
db['exp_m_avg'] = pickle.dumps(ts.exp_m_avg(m_start, end, 10, None))
db['hist_changes'] = pickle.dumps(ts.hist_changes(m_start, end, 1))
db['hist_adv'] = pickle.dumps(ts.hist_adv(m_start, end, 1))
db['hist_dec'] = pickle.dumps(ts.hist_dec(m_start, end, 1))
db['rsi'] = pickle.dumps(ts.rsi(m_start, end, 10))
db['exp_rsi'] = pickle.dumps(ts.exp_rsi(m_start, end, 10))
db['stdev'] = pickle.dumps(ts.stdev(m_start, end, 10))
db['b_bands'] = pickle.dumps(ts.b_bands(m_start, end, 10, 2))
db['low'] = pickle.dumps(ts.low(m_start, end, 1))
db['high'] = pickle.dumps(ts.high(m_start, end, 1))
low = security.low.low(m_start, end, 10)
high = security.high.high(m_start, end, 10)
db['fsto_k'] = pickle.dumps(ts.fsto_k(low, high, m_start, end, 10))
db['mfi'] = pickle.dumps(ts.mfi(security.high,
security.low,
security.close,
security.volume,
m_start,
end,
10))
db['monotonous_up'] = pickle.dumps(ts.monotonous_up(end, 10))
db['monotonous_down'] = pickle.dumps(ts.monotonous_down(end, 10))
db['historic_spread'] = pickle.dumps(ts.historic_spread(end, 10))
db['roc'] = pickle.dumps(ts.roc(end, 10))
db['derivative'] = pickle.dumps(ts.derivative(m_start, end))
db['derivative_m_avg'] = pickle.dumps(ts.derivative_m_avg(m_start, end, 10))
def test_get_price_cnyes_csv(self):
symb_50 = symbol.Symbol('0050')
self.assertEqual(symb_50.get_price('20160726'), (69.0, 69.4, 68.85, 69.4))
self.assertEqual(symb_50.get_price('20180615'), (81.60, 81.95, 81.20, 81.95))
self.assertEqual(symb_50.get_price(('20160726', '20160801')), (69.0, 69.95, 68.85, 69.7))
self.assertEqual(symb_50.get_price(('20160726', '20180614')), (69.00, 83.10, 68.85, 81.75))
info = ['Market Cap: ',
'P/E: ',
'EPS: ']
if (self.ts != None):
ts_info = ['Enter Signal: ' + str(self.ts.enter_signal),
'Exit Signal: ' + str(self.ts.exit_signal),
'Trading Commission: $' + str(self.ts.trading_commission),
'Performance Index: ' + str(self.ts.performance_index[ 'p_index' ].data[-1]) ]
for i in ts_info:
info.append(i)
for i in range (0, len(info)):
fig.text(padding, v_start - i*v_space
,info[i], fontsize=text_size)
if __name__ == '__main__':
import symbol
k=symbol.Symbol('GOOG', '20050901', '20090924')
m=Chart(k)
m.add_main_plot(k.close)
m.add_subplot(k.close.rsi('20060905', '20090924', 14))
#m.add_subplot(k.close.rsi('20060905', '20090924', 6))
m.show()
def get_symbol_by_index(idx):
sbl = None
if idx == 0:
# DOT
sbl = symbol.SymbolDot()
elif idx == 1:
# KEY_SIGNATURE_1_#
sbl = symbol.SymbolKeySignature('KEY_SIGNATURE_1_#', 1)
elif idx == 2:
# NOTE_QUARTER_UP
sbl = symbol.SymbolSingleNote('NOTE_QUARTER_UP', 1 / 4, 'up', 37,
False)
elif idx == 3:
# NOTE_HALF_UP
sbl = symbol.SymbolSingleNote('NOTE_HALF_UP', 1 / 2, 'up', 37,
False)
elif idx == 4:
# TIME_SIGNATURE_3_4
sbl = symbol.SymbolTimeSignature('TIME_SIGNATURE_3_4', 3, 4)
elif idx == 5:
# BAR
sbl = symbol.SymbolBar('BAR_SINGLE', 'single')
elif idx == 6:
# NOTE_QUARTER_DOWN
sbl = symbol.SymbolSingleNote('NOTE_QUARTER_DOWN', 1 / 4, 'down',
0, False)
elif idx == 7:
# BEAM_2_EIGHTH_NOTES_UP
sbl = symbol.SymbolBeamNote('BEAM_2_EIGHTH_NOTES_UP',
[1 / 8, 1 / 8], 'up', [36, 36])
elif idx == 8:
# BEAM_2_EIGHTH_NOTES_DOWN
sbl = symbol.SymbolBeamNote('BEAM_2_EIGHTH_NOTES_DOWN',
[1 / 8, 1 / 8], 'down', [0, 0])
elif idx == 9:
# FINAL_BAR
sbl = symbol.SymbolBar('BAR_DOUBLE', 'double')
elif idx == 10:
# REST_QUARTER
sbl = symbol.SymbolRest('REST_QUARTER', 1 / 4)
elif idx == 11:
# NOTE_HALF_DOWN
sbl = symbol.SymbolSingleNote('NOTE_HALF_DOWN', 1 / 2, 'down', 0,
False)
elif idx == 12:
# NOTE_EIGHTH_DOWN
sbl = symbol.SymbolSingleNote('NOTE_EIGHTH_DOWN', 1 / 8, 'down', 0,
False)
elif idx == 13:
# KEY_SIGNATURE_2_#
sbl = symbol.SymbolKeySignature('KEY_SIGNATURE_2_#', 2)
elif idx == 14:
# CLEF_TREBLE
sbl = symbol.SymbolClef('CLEF_TREBLE', 'treble')
elif idx == 15:
# NOTE_EIGHTH_UP
sbl = symbol.SymbolSingleNote('NOTE_EIGHTH_UP', 1 / 8, 'up', 37,
False)
elif idx == 16:
# NOTE_HALF_UP_WITH_DOT
sbl = symbol.SymbolSingleNote('NOTE_HALF_UP_WITH_DOT', 1 / 2, 'up',
37, True)
elif idx == 17:
# TIE
sbl = symbol.SymbolTie()
elif idx == 18:
# TIME_SIGNATURE_4_4
sbl = symbol.SymbolTimeSignature('TIME_SIGNATURE_4_4', 4, 4)
elif idx == 19:
# NOTE_QUARTER_UP_WITH_DOT
sbl = symbol.SymbolSingleNote('NOTE_QUARTER_UP_WITH_DOT', 1 / 4,
'up', 37, True)
elif idx == 20:
# NOTE_WHOLE
sbl = symbol.SymbolSingleNote('NOTE_WHOLE', 1, 'up', 0, False)
elif idx == 21:
# NOTE_QUARTER_DOWN_WITH_DOT
sbl = symbol.SymbolSingleNote('NOTE_QUARTER_DOWN_WITH_DOT', 1 / 4,
'down', 1.5, True)
elif idx == 22:
# KEY_SIGNATURE_1_b
sbl = symbol.SymbolKeySignature('KEY_SIGNATURE_1_b', -1)
elif idx == 23:
# TIME_SIGNATURE_2_4
sbl = symbol.SymbolTimeSignature('TIME_SIGNATURE_2_4', 2, 4)
pass
else:
sbl = symbol.Symbol('DEFAULT')
print('!FAIL Symbol recognition - getting index:', idx)
return sbl
def runNonInteractive(interp, args):
"""Executes the interpreter on args[0]."""
interp.eval(pair.list(symbol.Symbol("load"), args[0]))
def __init__(self, symbol_list, symbol_blacklist, start_date, end_date):
self.symbols = self.__expand_list(symbol_list, symbol_blacklist)
self.components = {}
for csym in self.symbols:
self.components[ csym ] = symbol.Symbol(csym, start_date, end_date)
def makeOr(clauses):
return pair.cons(symbol.Symbol("OR"), clauses)
import sys
import parser
import code
import symbol
name = sys.argv[1][:-4]
name = name + ".hack"
f = open(sys.argv[1],"r")
contents = f.readlines()
f.closed
p = parser.Parser()
trimmed_instructions = p.read_in(contents)
s = symbol.Symbol()
symbols_replaced = s.replace_symbols(trimmed_instructions)
c = code.Code();
output = c.convert_to_binary(symbols_replaced)
with open(name, 'w') as the_file:
for item in output:
the_file.write("%s\n" % item)