def test_populates_sell_trend(result):
dataframe = populate_sell_trend(populate_indicators(result))
assert 'sell' in dataframe.columns
def test_populates_buy_trend(result):
dataframe = populate_buy_trend(populate_indicators(result))
assert 'buy' in dataframe.columns
assert 'buy_price' in dataframe.columns
return dataframe
# define paths
base_path = os.path.join(os.path.expanduser('~'), 'freqtrade')
ml_path = os.path.join(base_path, 'ml_dev')
data_path = os.path.join(ml_path, 'data')
# define exchange
exchange = 'hitbtc'
# load back in
master = pd.read_csv(
os.path.join('/Users/dan/python-hitbtc/data/master',
'master_ethbtc_1min_v2.csv'))
# print(pd.to_datetime(master.date, format="%m/%d/%y %H:%M"))
# master['date'] = pd.to_datetime(master.date, format="%m/%d/%y %H:%M")
# populate indicators
master = populate_indicators(master)
# # get price percentage change at t+1
# percentage_change = (master['close'][entry + 1] - master['open'][entry + 1]) / master['open'][entry + 1]
# print(percentage_change)
# master['percent_change'][entry] = percentage_change
#
# save master
# master.to_csv(os.path.join(data_path, exchange, 'master.csv'), index=False)
master.to_csv(os.path.join(data_path, exchange, 'master_ethbtc_1min_v2.csv'),
index=False)
def preprocess(tickerdata: Dict[str, List]) -> Dict[str, DataFrame]:
"""Creates a dataframe and populates indicators for given ticker data"""
return {
pair: populate_indicators(parse_ticker_dataframe(pair_data))
for pair, pair_data in tickerdata.items()
}
def preprocess(backdata) -> Dict[str, DataFrame]:
processed = {}
for pair, pair_data in backdata.items():
processed[pair] = populate_indicators(
parse_ticker_dataframe(pair_data))
return processed
def preprocess(tickerdata: Dict[str, List]) -> Dict[str, DataFrame]:
"""Creates a dataframe and populates indicators for given ticker data"""
return {pair: populate_indicators(parse_ticker_dataframe(pair_data))
for pair, pair_data in tickerdata.items()}
def test_populates_sell_trend(result):
dataframe = populate_sell_trend(populate_indicators(result))
assert 'sell' in dataframe.columns
def test_populates_buy_trend(result):
dataframe = populate_buy_trend(populate_indicators(result))
assert 'buy' in dataframe.columns
while True:
# check time
t = datetime.utcnow()
s = t.second
m = t.minute
if s == 0 and m % trade_interval == 0:
# get OHLC candles
period = 'M' + str(trade_interval)
resp = hitbtc.get_candles(market, limit=1000, period=period)
# parse response into data frame and add technical indicators
df = parse_ticker_dataframe(resp, exchange_name='HitBTC')
df = populate_indicators(df)
# add ask, bid, and last
ticker = hitbtc.get_tickers(market)
df['ask'] = ticker['ask']
df['bid'] = ticker['bid']
df['last'] = ticker['last']
# save the last row of data frame
file_name = list(str(df['date'][len(df) - 1]))
file_name = "".join(file_name).replace(' ', '_').replace(':',
'-') + '.csv'
save_path = os.path.join(market_path, file_name)
df.tail(1).to_csv(save_path, index=False)
else: