hossein761

{
    "family": "",
    "taskRoleArn": "",
    "networkMode": "",
    "containerDefinitions": [
  {
    "memory": 1024,
    "cpu": 100,
    "links": ["mysql"],    
    "portMappings": [

hossein761

median = high_low_dif_df['high_low_dif'].divide(pnd_df['low']).multiply(100).median()

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high_low_dif = pnd_df['high'].sub(pnd_df['low'], axis=0)
high_low_dif_df = pd.DataFrame(high_low_dif_df['high_low_dif'].divide(pnd_df['low']).multiply(100), columns=['high_low_dif'])

high_low_dif_df.plot.hist(alpha=0.5)

hossein761

pnd_df = candles_df.loc[candles_df['volume'] >= 5000]
pnd_volume = pnd_df['volume'].values
pnd_high = pnd_df['high'].values
pnd_low = pnd_df['low'].values

fig1 = plt.figure()
ax1 = fig1.add_subplot(111, projection='3d')
ax1.scatter(pnd_low, pnd_high, pnd_volume, s=4, cmap='viridis')

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# train the models
gmm = GMM(n_components=5).fit(candles_df[['low', 'high', 'volume']])
# get the lables
labels = gmm.predict(candles_df[['low', 'high', 'volume']])

# visualise the mixture components (clusters)
volume = candles_df['volume'].values
high = candles_df['high'].values
low = candles_df['low'].values
fig = plt.figure()

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# load data
candles_df = pd.read_csv('bitfinex_btcusd_1h.csv', header=0)

# show scatter matrix
scatter_matrix(candles_df[['open', 'close', 'low', 'high', 'volume']], alpha=0.2, figsize=(6, 6), diagonal='kde')

hossein761

hossein761

<configuration>

    <conversionRule conversionWord="coloredLevel" converterClass="play.api.Logger$ColoredLevel" />

    <appender name="FILE" class="ch.qos.logback.core.FileAppender">
        <file>${application.home}/logs/application.log</file>
        <encoder>
            <pattern>%date - [%level] - from %logger in %thread %n%message%n%xException%n</pattern>
        </encoder>
    </appender>