def btconvert(inputfile=None, inputobject=None, outputfile=None, linekey=None):
if inputobject:
import collections
export = collections.OrderedDict(inputobject)
elif inputfile:
f = open(inputfile)
export = json.load(f)
f.close()
else:
raise UnboundLocalError("No input to convert")
with open(outputfile, "w", newline='') as csv_out:
writer = csv.writer(csv_out)
count = 0
dates = list(reversed(sorted(dates_from_keys(export.keys()))))
# import pdb; pdb.set_trace()
for date in dates[::
-1]: # this could be date in dates and reverse here if needed
if count == 0:
if linekey:
header = [linekey]
else:
header = [
'Date', 'Open', 'High', 'Low', 'Close', 'Adj Close',
'Volume'
]
# header.extend(export[line].keys())
writer.writerow(header)
count += 1
row = [date]
row.extend(list(export[date].values())[0:4])
row.extend([export[date]['4. close'], export[date]['5. volume']])
# print(date)
writer.writerow(row)
def include_fund_specific_meta_data(fundata):
if fundata:
dates = dates_from_keys(fundata.keys())
if len(dates):
if too_old(dates[-1]) or not ("meta" in fundata.keys()):
fundata["meta"] = {}
fundata["meta"]["start"] = dates[0]
fundata["meta"]["starting price"] = fundata[
dates[0]]["4. close"]
fundata["meta"]["end"] = dates[-1]
fundata["meta"]["ending price"] = fundata[
dates[-1]]["4. close"]
fundata["meta"]["number_of_days"] = (
parser.parse(dates[-1]) - parser.parse(dates[0])).days
fundata["meta"]["market_days"] = len(dates)
fundata["meta"]["return"] = round(
(float(fundata["meta"]["ending price"]) -
float(fundata["meta"]["starting price"])) /
float(fundata["meta"]["starting price"]), 3)
fundata["meta"]["averaged_annualized_CAGR"] = round(
36500 *
(calc_cagr(float(fundata["meta"]["starting price"]),
float(fundata["meta"]["ending price"]),
fundata["meta"]["number_of_days"])), 4)
else:
fundata["meta"] = {}
fundata["meta"]["note"] = "No data in range for "
return fundata
def build_6_year_returns(fundata, etf, six_year_returns):
dates = list(reversed(dates_from_keys(
fundata.keys()))) # descending order, new to old
for date in dates:
if not (date in six_year_returns.keys()):
six_year_returns[date] = {}
six_year_returns[date][etf] = {
"return": fundata[date]["6_year_return"]
}
return six_year_returns
def append_2_wk_change_to_files():
for etf in fidelity + list(set(symbols) - set(fidelity)):
fundata = get_fund_price_data(etf)
dates = dates_from_keys(fundata.keys())
if len(dates) > 15:
for i in range(15, len(dates)):
delta10 = (float(fundata[dates[i]]["4. close"]) -
float(fundata[dates[i - 10]]["4. close"])) / float(
fundata[dates[i - 10]]["4. close"])
fundata[dates[i]]["delta10"] = delta10
write_prices(fundata, etf)
def get_fund_price_data(symbol):
filename = pricefile(symbol)
fundata = read_json(filename)
if fundata:
if too_old(dates_from_keys(fundata.keys())[-1]):
fundata = get_prices(symbol, "compact") # get data
write_prices(fundata, symbol)
else:
fundata = get_prices(symbol) # get data
write_prices(fundata, symbol)
return fundata
def get_fund_price_data(etf):
pricefile = "./json/prices/" + etf + ".json"
fundprices = read_json(pricefile)
if fundprices:
if too_old(dates_from_keys(fundprices.keys())[-1]):
print("old 62")
fundata = get_fundata(etf)
fundprices = build_fundprices(etf, fundata, fundprices)
else:
import pdb
pdb.set_trace()
fundata = get_fundata(etf)
fundprices = build_fundprices(etf, fundata)
return fundprices
def append_delta1825_and_delta14_to_files():
for etf in fidelity + list(set(symbols) - set(fidelity)):
fundata = get_fund_price_data(etf)
dates = dates_from_keys(fundata.keys())
if len(dates) > 1839:
for i in range(1825, len(dates)):
delta1825 = (float(fundata[dates[i]]["4. close"]) - float(
fundata[dates[i - 1825]]["4. close"])) / float(
fundata[dates[i - 1825]]["4. close"])
fundata[dates[i]]["delta1825"] = delta1825
for i in range(len(dates) - 10):
delta14 = (float(fundata[dates[i + 10]]["4. close"]) -
float(fundata[dates[i]]["4. close"])) / float(
fundata[dates[i]]["4. close"])
fundata[dates[i]]["delta14"] = delta14
write_prices(fundata, etf)
def merge_fund_indicator_returns(symbol, strategy, fund_data):
fundata = {}
if fund_data:
dates = dates_from_keys(fund_data.keys())
# calculate return values by fund using given strategy
fireturns = calc_returns(fund_data, strategy)
# merge with api data
if "meta" in fund_data.keys():
fundata["meta"] = {**fireturns["meta"], **fund_data["meta"]}
for date in dates:
if date in fireturns.keys():
fundata[date] = {**fireturns[date], **fund_data[date]}
else:
fundata[date] = fund_data[date]
return fundata
def top_100():
deltas = []
for etf in fidelity + list(set(symbols) - set(fidelity)):
fundata = get_fund_price_data(etf)
dates = dates_from_keys(fundata.keys())
period = 5 * 365 # from runs of predict_two_weeks_from_now(); we learned that the jum at 11 years was due to narrowing the field
if len(dates) > period:
delta = (float(fundata[dates[period]]["4. close"]) -
float(fundata[dates[0]]["4. close"])) / float(
fundata[dates[period]]["4. close"])
deltas.append({"etf": etf, "delta": delta})
deltas = sorted(deltas, key=lambda x: x["delta"]).reverse()
convert_list(inputfile=None,
inputobject=deltas,
outputfile='top_100.csv',
linekey=None)
def all_indicators_only(fun_data):
if fun_data:
dates = dates_from_keys(fun_data.keys())
# remove all the dates that have incomplete data
for date in sorted(list(fun_data.keys())):
for indicator in all_the_keys:
if not (indicator in fun_data[date].keys()):
if date in dates:
# print (date)
# print (indicator)
dates.remove(date)
# copy values to a new object using keys that remain
fundata = {}
for date in dates:
fundata[date] = fun_data[date]
return fundata
def create_csv_price_files():
''' these files get moved to backtrader. maybe link?'''
# for etf in fidelity + list(set(symbols) - set(fidelity)): # ["AADR"]: #
for etf in etfs_to_process:
# update fundata files
fundata = get_fund_price_data(etf)
# this ordering may not be necessary
import collections
rfundata = collections.OrderedDict(fundata)
dates = dates_from_keys(fundata.keys()) # ascending
for date in dates:
# print(date)
rfundata[date] = fundata[date]
pricefile = "./csv/prices/" + etf + ".txt"
btconvert(inputfile=None,
inputobject=rfundata,
outputfile=pricefile,
linekey=None)
def get_fundata(symbol, indicators=[], start=None, end=None):
filename = "./json/raw/" + symbol + ".json"
# read from file if there is one; call api if there isn't
# if symbol == 'ALFA':
# import pdb; pdb.set_trace()
try:
f = open(filename)
fundata = json.load(f)
f.close()
except Exception as e:
print(e)
fundata = no_file(symbol, filename)
if not fundata:
fundata = no_file(symbol, filename)
#update if "too old"
if fundata and too_old(dates_from_keys(fundata.keys())[-1]):
api_data = call_api(symbol, 'compact')
# import pdb; pdb.set_trace()
fundata = build_data_object(symbol, api_data)
with open(filename, "w") as writeJSON:
json.dump(fundata, writeJSON)
# toss unwanted dates
''' 8/27 I'm pulling out fund_data_in_range because too often I'm seeing it result
in empty files. It will need to be replaced where needed. (after processing)
and all_indicators_only(fundata) 9/24
fundata = limit_fundata(fundata)
fundata = all_indicators_only(fundata) '''
# include the symbol name for human readability
if fundata:
if "meta" in fundata.keys():
fundata["meta"]["symbol"] = symbol
# note says "No data in range for ". should probably be moved into meta?
else:
if "note" in fundata.keys():
fundata["meta"]["note"] = fundata["note"] + symbol
del (fundata["note"])
with open(filename, "w") as writeJSON:
json.dump(fundata, writeJSON)
return fundata
def rank_and_value():
# append_delta1825_and_delta14_to_files()
# import sys; sys.stdout = open('file.txt', 'w')
daily_rankings = {}
summary = {}
for etf in fidelity + list(
set(symbols) - set(fidelity)): # ['PTH', "QQQ", "FPX", "ONEQ"]:
# print (etf)
fundata = get_fund_price_data(etf)
dates = dates_from_keys(fundata.keys())
for date in dates:
if ("delta1825" in fundata[date].keys()
and "delta14" in fundata[date].keys()):
obj = {
"etf": etf,
"delta1825": fundata[date]["delta1825"],
"delta14": fundata[date]["delta14"]
}
# print(date, obj)
# if date == '2014-07-14':
# import pdb; pdb.set_trace()
if not date in daily_rankings.keys():
daily_rankings[date] = [obj]
if daily_rankings[date] == None:
daily_rankings[date] = [obj]
if not obj in daily_rankings[date]:
daily_rankings[date].append(obj)
# print(daily_rankings[date])
# import pdb; pdb.set_trace()
for date in daily_rankings:
lst = daily_rankings[date]
# sorted(list_of_dict, key=lambda d: d['c'], reverse=True)
daily_rankings[date] = list(
reversed(sorted(daily_rankings[date],
key=lambda x: x["delta1825"])))
# print(daily_rankings[dates[-1][1]])
with open("rank.json", "w") as writeJSON:
json.dump(daily_rankings, writeJSON, sort_keys=True)
def set_6_year_return(fundata, symbol):
dates = dates_from_keys(fundata.keys()) # ascending order, old to new
period = 6 * 261
for index in range(len(dates) - 1, -1, -1): # descending order, new to old
# import pdb; pdb.set_trace()
'''fundata['2018-07-16'] = 0.0!
fundata['2010-07-21'] = dne '''
if "6_year_return" in fundata[dates[index]].keys():
return fundata
if index >= period:
fundata[dates[index]]["6_year_return"] = (
(float(fundata[dates[index]]["4. close"]) -
float(fundata[dates[index - period]]["4. close"])) /
float(fundata[dates[index - period]]["4. close"]))
# now - then / then
else: # now - oldest / oldest
fundata[dates[index]]["6_year_return"] = (
(float(fundata[dates[index]]["4. close"]) -
float(fundata[dates[0]]["4. close"])) /
float(fundata[dates[0]]["4. close"]))
write_prices(fundata, symbol)
return fundata
def btconvert_with_rank(inputfile=None,
inputobject=None,
outputfile=None,
linekey=None):
if inputobject:
export = inputobject
elif inputfile:
f = open(inputfile)
export = json.load(f)
f.close()
else:
raise UnboundLocalError("No input to convert")
with open(outputfile, "w", newline='') as csv_out:
writer = csv.writer(csv_out)
count = 0
dates = list(sorted(dates_from_keys(
export.keys()))) # ascending old to new
# import pdb; pdb.set_trace()
for index, date in enumerate(dates):
if index == 0:
if linekey:
header = [linekey]
else:
header = [
'Date', 'Open', 'High', 'Low', 'Close', 'Adj Close',
'Volume', 'rank2', 'rank20', 'rank130'
]
writer.writerow(header)
else: # we skip the first date since it has no rank value.
row = [date]
row.extend(list(export[date].values())[0:4])
row.extend([
export[date]['4. close'], export[date]['5. volume'],
export[date]['rank2'], export[date]['rank20'],
export[date]['rank130']
])
writer.writerow(row)
def add_more_indicators():
# import pdb; pdb.set_trace()
# marshall api data
for symbol in etfs_to_process:
filename = "./json/raw/" + symbol + ".json"
# read from file if there is one; call api if there isn't
try:
f = open(filename)
fundata = json.load(f)
f.close()
except Exception as e:
print(e)
api_data = call_api(symbol)
fundata = build_data_object(symbol, api_data)
with open(filename, "w") as writeJSON:
json.dump(fundata, writeJSON)
dates = dates_from_keys(fundata.keys())
for ma in ["sma", "ema"]: # change ma to new_indicator
if not (ma.upper() in fundata[dates[-1]]):
api_data = add_ma(symbol, ma) # a response object set
fundata = update_data_object(fundata, symbol, api_data)
with open(filename, "w") as writeJSON:
json.dump(fundata, writeJSON)
def predict_two_weeks_from_now():
results = {}
summary = {}
for etf in fidelity + list(set(symbols) - set(fidelity)):
fundata = get_fund_price_data(etf)
# why do this separately from rank, etc?
dates = dates_from_keys(fundata.keys())
results[etf] = {}
period_range = range(len(fundata) - 10)
for period in period_range: # each performance period
# over the data range
deltas = []
poscount, negcount, possible = 0, 0, 0
last_index_value = len(fundata) - 10 - period
if not period in summary.keys():
summary[period] = {
"poscount": 0,
"possible": 0,
"N": 0,
"points": 0
}
if not period in results[etf].keys():
results[etf][period] = {
"poscount": 0,
"possible": 0,
"points": 0
}
summary[period]["N"] += 1
for i in range(last_index_value):
return_over_preceding_period = float(
fundata[dates[i + period]]["4. close"]) - float(
fundata[dates[i]]["4. close"])
return_in_two_weeks = float(
fundata[dates[i + period + 10]]["4. close"]) - float(
fundata[dates[i + period]]["4. close"])
deltas.append({
"i":
i,
"dates": ([dates[i]], [dates[i + period]]),
"deltas":
(return_over_preceding_period, return_in_two_weeks)
})
if return_over_preceding_period >= 0:
possible += 1
if return_in_two_weeks > 0:
poscount += 1
if return_over_preceding_period < 0 and return_in_two_weeks < 0:
negcount += 1
results[etf][period]["poscount"] += poscount
results[etf][period]["possible"] += possible
results[etf][period]["points"] += last_index_value
summary[period]["poscount"] += results[etf][period]["poscount"]
summary[period]["possible"] += results[etf][period]["possible"]
summary[period]["points"] += results[etf][period]["points"]
for period in summary:
summary[period]["probability"] = round(
summary[period]['poscount'] / summary[period]['possible'], 4)
convert(inputfile=None,
inputobject=summary,
outputfile='results.csv',
linekey="period")
def set_6_year_rank(fundata, etf, six_year_returns):
dates = dates_from_keys(fundata.keys()) # ascending order, old to new
for index in range(len(dates) - 1, 0, -1): # descending order, new to old
fundata[dates[index]]["rank"] = six_year_returns[
dates[index]][etf]["rank"]
return fundata
