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 prepare_data(symbol):
coin = symbol
coin_1 = crypto_data(coin)
df = coin_1
df['SMA_20'] = sma(df['Close'], 20)
df['SMA_50'] = sma(df['Close'], 50)
df['EMA_20'] = ema(df['Close'], 20)
df['EMA_50'] = ema(df['Close'], 50)
df['MACD'] = macd(df['Close'], 26, 12)
df['per_k_stoch_10'] = percent_k(df['Close'], 10)
df['per_d_stoch_10'] = percent_d(df['Close'], 10)
df['OBV'] = obv(df['Close'], df['Volume'])
fp = []
for price in df['Close']:
fp.append(price)
fp.pop(0)
fp.append(df['Close'].mean())
df['FP'] = fp
df_predict = df.tail(1)
df.drop(df.tail(1).index, inplace=True)
label = []
for i in range(len(df)):
if df['FP'][i] > df['Close'][i]:
label.append(1)
else:
label.append(0)
df['goes_up'] = label
df = df.drop(['FP'], axis=1)
df = df.fillna(df.mean())
df_predict = df_predict.drop(['FP'], axis=1)
return df, df_predict
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