Plotting Market Data

from kraken.spot import Market

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd

---

1. Create the unauthenticated market client

market = Market()

2. Get the OHLC data of the XBTUSD pair, create the dataframe, and set the time index

df = pd.DataFrame(
    market.get_ohlc(pair='XBTUSD', interval=60)['XXBTZUSD'],
    columns=['time', 'open', 'high', 'low', 'close', 'vwap', 'volume', 'count']
).astype(float)
# df = df.set_index('time')
df['time'] = pd.to_datetime(df['time'], unit='s')
df = df.sort_values(by='time')
df

	time	open	high	low	close	vwap	volume	count
0	2024-03-02 12:00:00	61917.8	62223.7	61911.4	62142.4	62048.6	15.260659	616.0
1	2024-03-02 13:00:00	62142.4	62142.4	61900.0	61900.0	62006.8	50.357137	727.0
2	2024-03-02 14:00:00	61900.1	61971.8	61663.3	61891.7	61832.5	281.512303	1538.0
3	2024-03-02 15:00:00	61891.8	62032.2	61810.0	61836.8	61941.1	38.815926	886.0
4	2024-03-02 16:00:00	61836.8	62022.3	61714.9	61930.8	61839.0	181.980102	1261.0
...	...	...	...	...	...	...	...	...
715	2024-04-01 07:00:00	69680.1	69788.8	69562.0	69671.7	69676.0	40.187037	767.0
716	2024-04-01 08:00:00	69671.8	69671.8	69244.7	69450.0	69445.2	91.352301	1017.0
717	2024-04-01 09:00:00	69450.1	69613.4	69448.6	69448.7	69510.3	46.275460	731.0
718	2024-04-01 10:00:00	69448.7	69604.4	69353.0	69601.1	69454.0	23.874609	556.0
719	2024-04-01 11:00:00	69601.2	69850.1	69601.1	69666.1	69732.8	20.962756	455.0

720 rows × 8 columns

3. Compute some indicatoes based on the loaded data

# compute ema
df['ema21'] = df['close'].ewm(span=21, adjust=False, min_periods=21).mean()
df['ema50'] = df['close'].ewm(span=50, adjust=False, min_periods=50).mean()
df['ema200'] = df['close'].ewm(span=200, adjust=False, min_periods=200).mean()

def support_and_resistance(df: pd.DataFrame, lookback: int=200, levels: int=3) -> dict:
    ''' Returns up to 3 support and resistance levels by given dataframe '''
    high = df['high'][-lookback:].max()
    low = df['low'][-lookback:].min()
    close = df['close'][-lookback:].iloc[-1]

    pp = (high + low + close) / 3
    s1 = 2 * pp - high
    r1 = 2 * pp - low
    if levels >= 2:
        s2 = pp - (high - low)
        r2 = pp + (high - low)
        if levels >= 3:
            s3 = low - 2 * (high -pp)
            r3 = high + 2 * (pp - low)
            return { 's1': s1, 's2': s2, 's3': s3, 'r1': r1, 'r2': r2, 'r3': r3 }
        return { 's1': s1, 's2': s2, 'r1': r1, 'r2': r2 }
    return { 's1': s1,  'r2': r1 }

# compute support and resistance levels
for i, row in enumerate(df.index):
    try:
        srlevels = support_and_resistance(df.iloc[:i], lookback=50, levels=2)
        df.at[row, 's1'] = srlevels['s1']
        df.at[row, 'r1'] = srlevels['r1']
        df.at[row, 's2'] = srlevels['s2']
        df.at[row, 'r2'] = srlevels['r2']
    except:
        df.at[row, 's1'] = np.NaN
        df.at[row, 'r1'] = np.NaN
        df.at[row, 's2'] = np.NaN
        df.at[row, 'r2'] = np.NaN

# isn't that a beauty?:
df

	time	open	high	low	close	vwap	volume	count	ema21	ema50	ema200	s1	r1	s2	r2
0	2024-03-02 12:00:00	61917.8	62223.7	61911.4	62142.4	62048.6	15.260659	616.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN
1	2024-03-02 13:00:00	62142.4	62142.4	61900.0	61900.0	62006.8	50.357137	727.0	NaN	NaN	NaN	61961.300000	62273.600000	61780.200000	62404.800000
2	2024-03-02 14:00:00	61900.1	61971.8	61663.3	61891.7	61832.5	281.512303	1538.0	NaN	NaN	NaN	61792.100000	62115.800000	61684.200000	62331.600000
3	2024-03-02 15:00:00	61891.8	62032.2	61810.0	61836.8	61941.1	38.815926	886.0	NaN	NaN	NaN	61628.766667	62189.166667	61365.833333	62486.633333
4	2024-03-02 16:00:00	61836.8	62022.3	61714.9	61930.8	61839.0	181.980102	1261.0	NaN	NaN	NaN	61592.166667	62152.566667	61347.533333	62468.333333
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
715	2024-04-01 07:00:00	69680.1	69788.8	69562.0	69671.7	69676.0	40.187037	767.0	70380.082934	70315.531093	69369.034227	68659.500000	71012.200000	67639.000000	72344.400000
716	2024-04-01 08:00:00	69671.8	69671.8	69244.7	69450.0	69445.2	91.352301	1017.0	70295.529940	70281.588697	69369.839856	68653.966667	71006.666667	67636.233333	72341.633333
717	2024-04-01 09:00:00	69450.1	69613.4	69448.6	69448.7	69510.3	46.275460	731.0	70218.545400	70248.926395	69370.624534	68506.166667	70858.866667	67562.333333	72267.733333
718	2024-04-01 10:00:00	69448.7	69604.4	69353.0	69601.1	69454.0	23.874609	556.0	70162.414000	70223.521439	69372.917823	68505.300000	70858.000000	67561.900000	72267.300000
719	2024-04-01 11:00:00	69601.2	69850.1	69601.1	69666.1	69732.8	20.962756	455.0	70117.294545	70201.661774	69375.835058	68606.900000	70959.600000	67612.700000	72318.100000

720 rows × 15 columns

4. Plot the results

fig = plt.figure(figsize=(10,5), dpi=300)
ax = plt.gca()
df.plot(x='time', y=['close', 'ema21', 'ema50', 'ema200'], ax=ax, color=['black', 'green', 'orange', 'gray'])

xmin, xmax = df['time'].iloc[0], df['time'].iloc[-1]
ax.hlines(y=df['s1'].iloc[-1], xmin=xmin, xmax=xmax, color='green', linestyle=':', label='S1')
ax.hlines(y=df['s2'].iloc[-1], xmin=xmin, xmax=xmax, color='green', linestyle='--', label='S2')
ax.hlines(y=df['r1'].iloc[-1], xmin=xmin, xmax=xmax, color='red', linestyle=':', label='R1')
ax.hlines(y=df['r2'].iloc[-1], xmin=xmin, xmax=xmax, color='red', linestyle='--', label='R2')
ax.set_ylabel('Price in Dollar')
plt.legend()
plt.title('XBT/USD');

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