import pandas as pd
import seaborn as sns
import fmz

df = fmz.get_bars('MA001', start='2018-01-01 08:00:00', end='2019-10-31') 
returns = df.pct_change().dropna()
sns.distplot(returns.iloc[:,0:1])

<matplotlib.axes._subplots.AxesSubplot at 0x7fd1b8018278>
<Figure size 432x288 with 1 Axes>

df1 = fmz.get_bars('btc_usd_bitfinex', start='2017-01-01', end='2018-01-31') 
df2 = fmz.get_bars('eth_usd_bitfinex', start='2017-01-01', end='2018-01-31')

returns_a = df1.pct_change().dropna()
returns_b = df2.pct_change().dropna()

sns.jointplot(df1['open'], df2['open'], kind='reg', height=12)

<seaborn.axisgrid.JointGrid at 0x7f9fda15b198>
<Figure size 864x864 with 3 Axes>

returns = df1.pct_change().dropna()
sns.distplot(returns.iloc[:,0:1])

<matplotlib.axes._subplots.AxesSubplot at 0x7f5250283208>
<Figure size 432x288 with 1 Axes>

sns.violinplot(returns,size=24)

<matplotlib.axes._subplots.AxesSubplot at 0x7f524dc3a320>
<Figure size 432x288 with 1 Axes>

sns.pairplot(returns, diag_kind='kde', size=2.4)

<seaborn.axisgrid.PairGrid at 0x7f51d6bd6be0>
<Figure size 864x864 with 30 Axes>

sns.heatmap(returns.corr())

<matplotlib.axes._subplots.AxesSubplot at 0x7f51d5d98470>
<Figure size 432x288 with 2 Axes>

sns.clustermap(returns.corr())

<seaborn.matrix.ClusterGrid at 0x7f51d5cd6978>
<Figure size 720x720 with 4 Axes>

import fmz
bars = fmz.get_bars("MA888")
bars

            open  high   low  close   volume
2019-03-03  2638  2694  2608   2662  3044544
2019-03-04  2668  2671  2627   2654  2070936
2019-03-05  2656  2677  2623   2658  2340822
2019-03-06  2654  2671  2575   2584  2700718
2019-03-07  2591  2596  2552   2578  2342280
...          ...   ...   ...    ...      ...
2019-09-22  2320  2382  2320   2377  1976592
2019-09-23  2374  2391  2343   2379  1994652
2019-09-24  2376  2415  2354   2404  2295352
2019-09-25  2404  2422  2345   2377  2693284
2019-09-26  2365  2378  2323   2341  2004090

[143 rows x 5 columns]

import talib
talib.EMA(bars['close'])

2019-03-03            NaN
2019-03-04            NaN
2019-03-05            NaN
2019-03-06            NaN
2019-03-07            NaN
                 ...     
2019-09-22    2239.369039
2019-09-23    2248.377488
2019-09-24    2258.417650
2019-09-25    2266.068124
2019-09-26    2270.902439
Length: 143, dtype: float64