def getData(self):
# define URL
base = 'https://api.binance.com'
endpoint = '/api/v1/klines'
params = '?&symbol=' + self.symbol + '&interval=1h'
url = base + endpoint + params
# download data
data = requests.get(url)
dictionary = json.loads(data.text)
# put in dataframe and clean-up
df = pd.DataFrame.from_dict(dictionary)
df = df.drop(range(6, 12), axis=1)
# rename columns
col_names = ['time', 'open', 'high', 'low', 'close', 'volume']
df.columns = col_names
# transform values from strings to floats
for col in col_names:
df[col] = df[col].astype(float)
# add the moving averages
df['fast_sma'] = sma(df['close'].tolist(), 10)
df['slow_sma'] = sma(df['close'].tolist(), 30)
return df
def getData(self):
#get url to data from kraken api
pair = self.pair
interval = str(self.interval)
base = 'https://api.kraken.com'
endpoint = '/0/public/OHLC'
params = '?pair=' + pair + '&interval=' + interval
url = base + endpoint + params
#download the data
json_string = requests.get(url)
dictionary = json.loads(json_string.text)
dict_len = len(dictionary['result'][pair])
self.dflength = dict_len
#creat pandas df
col_names = ['time', 'open', 'high', 'low', 'close', 'volume', 'sma20', 'sma40', 'rsi', 'obv','hma20']
df = pd.DataFrame(columns = col_names)
#creat df cause the import stuff would work
for x in range(dict_len):
temp = dictionary['result'][pair][x]
df = df.append({col_names[0]: temp[0], col_names[1]: temp[1], col_names[2]: temp[2], col_names[3]: temp[3], col_names[4]: temp[4], col_names[5]: temp[5]}, ignore_index=True)
#turn df into floats
for col in col_names:
df[col] = df[col].astype(float)
#add techinical indicatiors to the df
df['time'] = [datetime.fromtimestamp(x) for x in df['time']]
df['sma20'] = sma(df['close'].tolist(), 20)
df['sma40'] = sma(df['close'].tolist(), 50)
df['hma20'] = hma(df['close'].tolist(), 200)
df['rsi'] = rsi(df['close'].tolist(), 10)
df['obv'] = obv(df['close'].tolist(), df['volume'].tolist())
return df
def getData(self):
# get url
base = 'https://api.binance.com'
endpoint = '/api/v3/klines'
params = '?&symbol=' + self.symbol + '&interval=1h'
url = base + endpoint + params
print("Fetching data from " + url)
# fetch data
data = requests.get(url)
dictionary = json.loads(data.text)
# clean up
df = pd.DataFrame.from_dict(dictionary).drop(range(6, 12), axis=1)
col_names = ['time', 'open', 'high', 'low', 'close', 'volume']
df.columns = col_names
print(df)
for col in col_names:
df[col] = df[col].astype(float)
# calculate SMAs
df['fast_sma'] = sma(df['close'].tolist(), 10)
df['slow_sma'] = sma(df['close'].tolist(), 30)
return df
def collect_data(symbol, time):
base = 'https://api.binance.com'
endpoint = '/api/v1/klines'
params = '?&symbol=' + symbol + '&interval=' + time
data = requests.get(base + endpoint + params)
dictionary = json.loads(data.text)
dataframe = pandas.DataFrame.from_dict(dictionary)
dataframe = dataframe.drop(range(6, 12), axis=1)
dataframe.columns = ['time', 'open', 'high', 'low', 'close', 'volume']
for col in dataframe.columns:
dataframe[col] = dataframe[col].astype(float)
dataframe['fast_sma'] = sma(dataframe['close'].tolist(), 10)
dataframe['slow_sma'] = sma(dataframe['close'].tolist(), 30)
dataframe.plot(x='time',
y='fast_sma',
kind='line',
title='fast sma in the last 10 hours')
dataframe.plot(x='time',
y='slow_sma',
kind='line',
title='slow sma in the last 30 hours')
plt.show()
def __init__(self, symbol):
self.symbol = symbol
self.exchange = bi.Binance()
self.df = self.exchange.GetSymbolData(symbol, '4h')
self.last_price = self.df['close'][len(self.df['close'])-1]
self.buy_signals = []
try:
self.df['fast_sma'] = sma(self.df['close'].tolist(), 10)
self.df['slow_sma'] = sma(self.df['close'].tolist(), 30)
self.df['low_boll'] = lbb(self.df['close'].tolist(), 14)
self.df['up_boll'] = ubb(self.df['close'].tolist(), 14)
self.df['vwap'] = (self.df['volume']*(self.df['close'])).cumsum() / self.df['volume'].cumsum()
#self.df['vwap'] = (self.df['volume']*(self.df['high']+self.df['low'])/2).cumsum() / self.df['volume'].cumsum()
self.df['vwma'] = self.vwma(14)
# From Indicators
self.df = ind.macd(self.df)
self.df = ind.money_flow_index(self.df)
self.df = ind.rsi(self.df)
#From Finta
vw_macd = TA.VW_MACD(self.df)
self.df['vw_macd'] = vw_macd['MACD']
self.df['vw_macd_sig'] = vw_macd['SIGNAL']
except Exception as e:
print(" Exception raised when trying to compute indicators on "+self.symbol)
print(e)
return None
def getData(self):
base = 'https://api.binance.com'
endpoint = '/api/v1/klines'
params = '?&symbol=' + self.symbol + '&interval=1d'
url = base + endpoint + params
#JSON LOADS AND DOWNLOAD DATA
data = requests.get(url)
dictionary = json.loads(data.text)
#PUT IN DATAFRAME AND CLEAN UP
df = pd.DataFrame.from_dict(dictionary)
df = df.drop(range(6, 12), axis=1)
#NAMING COLUMNS
typeOfcol = ['time', 'open', 'high', 'low', 'close', 'volume']
df.columns = typeOfcol
for col in typeOfcol:
df[col] = df[col].astype(float)
valueONE = 10
valueTWO = 21
valueTHREE = 50
valueFOUR = 200
#ADDING SMA'S
df['first_sma'] = sma(df['close'].tolist(), valueONE)
df['second_sma'] = sma(df['close'].tolist(), valueTWO)
df['third_sma'] = sma(df['close'].tolist(), valueTHREE)
df['fourth_sma'] = sma(df['close'].tolist(), valueFOUR)
return df
def getData(self): # definition de l'URL: obtention des donnée à partir de binance.com base = 'https://api.binance.com' endpoint = '/api/v1/klines' params = '?&symbol='+self.symbol+'&interval=1h' url = base + endpoint + params # téléchargement des data data = requests.get(url) dictionary = json.loads(data.text) # on met les données dans une dataframe df = pd.DataFrame.from_dict(dictionary) df = df.drop(range(6, 12), axis=1) # On renomme correctement les colonnes col_names = ['time', 'open', 'high', 'low', 'close', 'volume'] df.columns = col_names # transform values from strings to floats for col in col_names: df[col] = df[col].astype(float) # add the moving averages df['fast_sma'] = sma(df['close'].tolist(), 10) df['slow_sma'] = sma(df['close'].tolist(), 30) return df
def __init__(self, symbol):
self.symbol = symbol
self.exchange = Binance()
self.df = self.exchange.GetSymbolData(symbol, '4h')
self.last_price = self.df['close'][len(self.df['close'])-1]
self.buy_signals = []
try:
self.df['fast_sma'] = sma(self.df['close'].tolist(), 10)
self.df['slow_sma'] = sma(self.df['close'].tolist(), 30)
self.df['low_boll'] = lbb(self.df['close'].tolist(), 14)
except Exception as e:
print(" Exception raised when trying to compute indicators on "+self.symbol)
print(e)
return None
def __init__(self, symbol, timeframe: str = '4h'):
self.symbol = symbol
self.exchange = Binance()
self.df = self.exchange.get_symbol_data(symbol, timeframe)
self.last_price = self.df['close'][len(self.df['close']) - 1]
try:
self.df['short_sma'] = sma(self.df['close'].tolist(), 10)
self.df['long_sma'] = sma(self.df['close'].tolist(), 30)
self.df['low_boll'] = lbb(self.df['close'].tolist(), 14)
except Exception as e:
print(' Exception when trying to computer indicators on: ' +
self.symbol)
print(e)
return None
def __init__(self, symbol, timeframe='4h'):
self.symbol = symbol
self.timeframe = timeframe
self.exchange = Binance()
self.df = self.exchange.GetSymbolData(symbol, timeframe)
self.last_price = self.df['close'][len(self.df['close']) - 1]
try:
self.df['fast_sma'] = sma(self.df['close'].tolist(), 10)
self.df['slow_sma'] = sma(self.df['close'].tolist(), 30)
self.df['low_boll'] = lbb(self.df['close'].tolist(), 14)
except Exception as e:
print(
"Exception occurred when attempting to compute indicators on "
+ self.symbol)
print(e)
return None
def process_data(candlestick_data):
"""
Processes the candlestick data in order to be able to apply our trading strategy on it.
First, we will rename the columns of our data frame and convert all data types from string to floats in order
to be able to perform calculations. Then, we will add the slow moving average and the fast moving average to our
market data. Each of them will represent a new column within our candlestick df. At last, we will another
additional column holding the time in datetime format so we not only have the time or our data in timestamp
format.
Parameter:
- candlestick_data: DataFrame - Candlestick market data that we will process
Return:
- df: DataFrame - The adjusted and processed candlestick data frame holding our market data
"""
df = candlestick_data
df = df.drop(range(
6, 12), axis=1) # Drop the last six columns as they are not needed
# Rename the columns
col_names = ["time", "open", "high", "low", "close", "volume"]
df.columns = col_names
# Cast values from strings to floats
for col in col_names:
df[col] = df[col].astype(float)
# Add the moving averages to the data frame
df["fast_sma"] = sma(
df["close"].tolist(),
10) # fast simple moving average follows prices closely
df["slow_sma"] = sma(
df["close"].tolist(),
30) # slow simple moving average follows prices less closely
# Add date in datetime format to the data frame so we can se real dates on the plot and not just timestamps
df["datetime"] = pd.to_datetime(df["time"] * 1000000,
infer_datetime_format=True)
# Return result
return df
def getData(self):
#get url to data from kraken api
data = pd.read_csv("Kraken_BTCUSD_1h.csv")
#creat pandas df
col_names = [
'time', 'symbol', 'open', 'high', 'low', 'close', 'volume',
'volume usd', 'sma20', 'sma40', 'rsi', 'obv'
]
df = pd.DataFrame(data, columns=col_names)
print df #turn df into floats
for col in col_names:
df[col] = df[col].astype(float)
#add techinical indicatiors to the df
#df['time'] = [datetime.fromtimestamp(x) for x in df['time']]
df['sma20'] = sma(df['close'].tolist(), 20)
df['sma40'] = sma(df['close'].tolist(), 40)
df['rsi'] = rsi(df['close'].tolist(), 14)
df['obv'] = obv(df['close'].tolist(), df['volume'].tolist())
return df
class TradingModel:
def __init__(self, symbol):
self.symbol = symbol
self.df = self.getData()
def getData(self):
# define URL
base_url = 'Your API URL'
api_key_id = 'Your API Key'
api_secret = 'Your API Secret'
api = tradeapi.REST(
base_url=base_url,
key_id=api_key_id,
secret_key=api_secret)
session = requests.session()
# download data
data = requests.get(url)
dictionary = json.loads(data.text)
# put in dataframe and clean-up
df = pd.DataFrame.from_dict(dictionary)
df = df.drop(range(6, 12), axis=1)
# rename columns
col_names = ['time', 'open', 'high', 'low', 'close', 'volume']
df.columns = col_names
# transform values from strings to floats
for col in col_names:
df[col] = df[col].astype(float)
# add the moving averages
df['fast_sma'] = sma(df['close'].tolist(), 10)
df['slow_sma'] = sma(df['close'].tolist(), 30)
return df
def add_sma(df_or_lst, period):
if isinstance(df_or_lst, list) == True:
return sma(df_or_lst, period)
else:
data_frame['sma ' + str(period)] = sma(df_or_lst['close'].tolist(),
period)
