How do I make Order Book Depth Chart in Matplotlib?
Question:
I am trying to create a "Depth Chart" from the Order book which will look like the below:
I figured out that histogram will be used but unable to set the proper bins hence producing an improper chart. 'ASK/QTY' are given below:
[49579.41, 49580.66, 49581.39, 49585.42, 49585.43, 49586.99, 49588.0, 49588.74, 49589.62, 49589.8, 49589.81, 49590.0, 49590.37, 49590.38, 49591.38, 49591.87, 49592.84, 49593.32, 49593.86, 49594.17, 49594.36, 49595.32, 49595.54, 49595.92, 49595.97, 49596.87, 49596.88, 49596.99, 49597.06, 49597.2, 49597.21, 49597.52, 49597.64, 49597.79, 49598.08, 49598.46, 49598.69, 49598.98, 49599.52, 49600.0, 49600.14, 49600.26, 49600.34, 49600.47, 49600.62, 49600.73, 49601.0, 49601.25, 49601.3, 49601.71, 49601.85, 49601.93, 49602.2, 49602.7, 49602.93, 49603.12, 49603.15, 49603.23, 49603.56, 49604.27, 49604.31, 49604.32, 49604.89, 49604.9, 49605.12, 49605.21, 49605.5, 49605.59, 49606.0, 49606.13, 49606.39, 49606.62, 49607.2, 49607.5, 49607.68, 49607.69, 49607.71, 49607.76, 49608.05, 49608.11, 49608.67, 49609.71, 49610.0, 49610.07, 49610.18, 49610.53, 49610.54, 49611.72, 49612.1, 49612.25, 49612.42, 49612.71, 49612.99, 49613.0, 49613.42, 49613.45, 49613.53, 49613.62, 49613.84, 49613.92]
[4.514622, 1.0, 1.0, 0.014002, 0.04, 0.012384, 0.005441, 0.04, 0.254507, 0.219849, 0.559163, 0.14, 0.219849, 0.02, 0.010085, 0.399503, 0.50407, 0.05, 0.133538, 0.000952, 0.03, 0.023, 0.1205, 0.063524, 0.038323, 0.371299, 0.03, 0.041181, 0.182644, 0.088, 0.022, 0.040338, 0.137032, 0.464753, 0.05, 0.893, 1.248, 0.022, 0.3, 4.683597, 0.797, 0.004717, 0.504112, 0.009785, 0.03, 0.006436, 0.005641, 0.2937, 0.116174, 0.0183, 0.853, 0.041181, 0.03, 0.005512, 0.4, 0.24, 0.040373, 0.364929, 0.008, 0.005515, 0.163396, 0.03, 0.00377, 0.03943, 0.02386, 0.765352, 0.46, 0.115285, 0.251659, 0.049, 0.111, 0.4, 0.4, 0.364734, 0.022, 0.319989, 0.141003, 0.426611, 0.022, 0.06, 0.626486, 0.5, 0.004243, 0.059612, 0.3, 4.747239, 0.04, 0.2406, 0.021046, 0.237078, 0.428943, 0.000626, 0.000932, 0.001094, 1.906807, 0.03, 0.349154, 0.040366, 0.004549, 0.038309]
My code is given below:
import matplotlib
from utilities import get_order_book
import matplotlib.pyplot as plt
if __name__ == '__main__':
pair = 'BTC/USDT'
asks, bids = get_order_book(pair)
ask_x = []
ask_y = []
for ask in asks:
ask_x.append(ask[0])
ask_y.append(ask[1])
print(ask_x)
print(ask_y)
plt.bar(ask_x, ask_y, 2)
plt.hist(ask_x, bins=len(ask_x))
plt.show()
Answers:
If you are not interested in coloring the area under the curve, you can use matplotlib.pyplot.step (pay attention to where parameter, in this case I used the default pre value):
x = [49579.41, 49580.66, 49581.39, 49585.42, 49585.43, 49586.99, 49588.0, 49588.74, 49589.62, 49589.8, 49589.81, 49590.0, 49590.37, 49590.38, 49591.38, 49591.87, 49592.84, 49593.32, 49593.86, 49594.17, 49594.36, 49595.32, 49595.54, 49595.92, 49595.97, 49596.87, 49596.88, 49596.99, 49597.06, 49597.2, 49597.21, 49597.52, 49597.64, 49597.79, 49598.08, 49598.46, 49598.69, 49598.98, 49599.52, 49600.0, 49600.14, 49600.26, 49600.34, 49600.47, 49600.62, 49600.73, 49601.0, 49601.25, 49601.3, 49601.71, 49601.85, 49601.93, 49602.2, 49602.7, 49602.93, 49603.12, 49603.15, 49603.23, 49603.56, 49604.27, 49604.31, 49604.32, 49604.89, 49604.9, 49605.12, 49605.21, 49605.5, 49605.59, 49606.0, 49606.13, 49606.39, 49606.62, 49607.2, 49607.5, 49607.68, 49607.69, 49607.71, 49607.76, 49608.05, 49608.11, 49608.67, 49609.71, 49610.0, 49610.07, 49610.18, 49610.53, 49610.54, 49611.72, 49612.1, 49612.25, 49612.42, 49612.71, 49612.99, 49613.0, 49613.42, 49613.45, 49613.53, 49613.62, 49613.84, 49613.92]
y = [4.514622, 1.0, 1.0, 0.014002, 0.04, 0.012384, 0.005441, 0.04, 0.254507, 0.219849, 0.559163, 0.14, 0.219849, 0.02, 0.010085, 0.399503, 0.50407, 0.05, 0.133538, 0.000952, 0.03, 0.023, 0.1205, 0.063524, 0.038323, 0.371299, 0.03, 0.041181, 0.182644, 0.088, 0.022, 0.040338, 0.137032, 0.464753, 0.05, 0.893, 1.248, 0.022, 0.3, 4.683597, 0.797, 0.004717, 0.504112, 0.009785, 0.03, 0.006436, 0.005641, 0.2937, 0.116174, 0.0183, 0.853, 0.041181, 0.03, 0.005512, 0.4, 0.24, 0.040373, 0.364929, 0.008, 0.005515, 0.163396, 0.03, 0.00377, 0.03943, 0.02386, 0.765352, 0.46, 0.115285, 0.251659, 0.049, 0.111, 0.4, 0.4, 0.364734, 0.022, 0.319989, 0.141003, 0.426611, 0.022, 0.06, 0.626486, 0.5, 0.004243, 0.059612, 0.3, 4.747239, 0.04, 0.2406, 0.021046, 0.237078, 0.428943, 0.000626, 0.000932, 0.001094, 1.906807, 0.03, 0.349154, 0.040366, 0.004549, 0.038309]
fig, ax = plt.subplots()
ax.step(x, y)
plt.show()
Otherwise, if you want to color the area under the curve, you can use matplotlib.pyplot.stairs, as already suggested by BigBen in the comment. Pay attention to the fact you need to add a value to x array, because this time it represents edges, not data points.
x.append(x[-1])
fig, ax = plt.subplots()
ax.stairs(edges = x, values = y, fill = True)
plt.show()
It seems the two plots are different: this because in matplotlib.pyplot.step I used the default value for where parameter; on the contrary, matplotlib.pyplot.stairs uses a post interpolation:
x.append(x[-1])
fig, ax = plt.subplots()
ax.step(x[:-1], y, where = 'post')
ax.stairs(edges = x, values = y, fill = True)
plt.show()
The accepted answer gets you part of the way there, but an actual depth chart is monotonically decreasing/increasing for bids/asks because it includes a cumulative summation of the limit orders. The rationale is that the depth chart will show you "how many units are available for a given price or better" :
For example, say you have the following bids for TKR:
Price
Quantity
$10
3
$5
4
$3
10
If you place a sell order TKR at $5, then you will be able to sell 7 units at that price; 4 will be bought at $5, and 3 (which were willing to be bought at $10) will definitely be bought at the more appealing $5. Therefore, the depth chart at Price=5 should show a quantity of 7, not 4. Likewise, at Price=3, the quantity would be 17.
Going back to your question/example, the easiest way that I’ve found (all credit goes to this notebook/article) would be to leverage seaborn’s ecdfplot as follows:
import random
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
# your original data
ask_price = [49579.41, 49580.66, 49581.39, 49585.42, 49585.43, 49586.99, 49588.0, 49588.74, 49589.62, 49589.8, 49589.81, 49590.0, 49590.37, 49590.38, 49591.38, 49591.87, 49592.84, 49593.32, 49593.86, 49594.17, 49594.36, 49595.32, 49595.54, 49595.92, 49595.97, 49596.87, 49596.88, 49596.99, 49597.06, 49597.2, 49597.21, 49597.52, 49597.64, 49597.79, 49598.08, 49598.46, 49598.69, 49598.98, 49599.52, 49600.0, 49600.14, 49600.26, 49600.34, 49600.47, 49600.62, 49600.73, 49601.0, 49601.25, 49601.3, 49601.71, 49601.85, 49601.93, 49602.2, 49602.7, 49602.93, 49603.12, 49603.15, 49603.23, 49603.56, 49604.27, 49604.31, 49604.32, 49604.89, 49604.9, 49605.12, 49605.21, 49605.5, 49605.59, 49606.0, 49606.13, 49606.39, 49606.62, 49607.2, 49607.5, 49607.68, 49607.69, 49607.71, 49607.76, 49608.05, 49608.11, 49608.67, 49609.71, 49610.0, 49610.07, 49610.18, 49610.53, 49610.54, 49611.72, 49612.1, 49612.25, 49612.42, 49612.71, 49612.99, 49613.0, 49613.42, 49613.45, 49613.53, 49613.62, 49613.84, 49613.92]
ask_qty = [4.514622, 1.0, 1.0, 0.014002, 0.04, 0.012384, 0.005441, 0.04, 0.254507, 0.219849, 0.559163, 0.14, 0.219849, 0.02, 0.010085, 0.399503, 0.50407, 0.05, 0.133538, 0.000952, 0.03, 0.023, 0.1205, 0.063524, 0.038323, 0.371299, 0.03, 0.041181, 0.182644, 0.088, 0.022, 0.040338, 0.137032, 0.464753, 0.05, 0.893, 1.248, 0.022, 0.3, 4.683597, 0.797, 0.004717, 0.504112, 0.009785, 0.03, 0.006436, 0.005641, 0.2937, 0.116174, 0.0183, 0.853, 0.041181, 0.03, 0.005512, 0.4, 0.24, 0.040373, 0.364929, 0.008, 0.005515, 0.163396, 0.03, 0.00377, 0.03943, 0.02386, 0.765352, 0.46, 0.115285, 0.251659, 0.049, 0.111, 0.4, 0.4, 0.364734, 0.022, 0.319989, 0.141003, 0.426611, 0.022, 0.06, 0.626486, 0.5, 0.004243, 0.059612, 0.3, 4.747239, 0.04, 0.2406, 0.021046, 0.237078, 0.428943, 0.000626, 0.000932, 0.001094, 1.906807, 0.03, 0.349154, 0.040366, 0.004549, 0.038309]
# generate fake bid data for illustration purposes
bid_price = [p-(0.001*p) for p in ask_price]
bid_qty = [q*(2*random.random()) for q in ask_qty]
ask_df = pd.DataFrame({'price': ask_price, 'quantity': ask_qty})
bid_df = pd.DataFrame({'price': bid_price, 'quantity': bid_qty})
fig, ax = plt.subplots()
ax.set_title(f"Order Book Depth Chart")
sns.ecdfplot(x="price", weights="quantity", stat="count",
data=ask_df, ax=ax, color="red")
sns.ecdfplot(x="price", weights="quantity", stat="count",
complementary=True, data=bid_df, ax=ax, color="green")
# complementary=True allows reflects that lower bids are "better"
ax.set_xlabel("Price")
ax.set_ylabel("Quantity")
with the following result:
I am trying to create a "Depth Chart" from the Order book which will look like the below:
I figured out that histogram will be used but unable to set the proper bins hence producing an improper chart. 'ASK/QTY' are given below:
[49579.41, 49580.66, 49581.39, 49585.42, 49585.43, 49586.99, 49588.0, 49588.74, 49589.62, 49589.8, 49589.81, 49590.0, 49590.37, 49590.38, 49591.38, 49591.87, 49592.84, 49593.32, 49593.86, 49594.17, 49594.36, 49595.32, 49595.54, 49595.92, 49595.97, 49596.87, 49596.88, 49596.99, 49597.06, 49597.2, 49597.21, 49597.52, 49597.64, 49597.79, 49598.08, 49598.46, 49598.69, 49598.98, 49599.52, 49600.0, 49600.14, 49600.26, 49600.34, 49600.47, 49600.62, 49600.73, 49601.0, 49601.25, 49601.3, 49601.71, 49601.85, 49601.93, 49602.2, 49602.7, 49602.93, 49603.12, 49603.15, 49603.23, 49603.56, 49604.27, 49604.31, 49604.32, 49604.89, 49604.9, 49605.12, 49605.21, 49605.5, 49605.59, 49606.0, 49606.13, 49606.39, 49606.62, 49607.2, 49607.5, 49607.68, 49607.69, 49607.71, 49607.76, 49608.05, 49608.11, 49608.67, 49609.71, 49610.0, 49610.07, 49610.18, 49610.53, 49610.54, 49611.72, 49612.1, 49612.25, 49612.42, 49612.71, 49612.99, 49613.0, 49613.42, 49613.45, 49613.53, 49613.62, 49613.84, 49613.92]
[4.514622, 1.0, 1.0, 0.014002, 0.04, 0.012384, 0.005441, 0.04, 0.254507, 0.219849, 0.559163, 0.14, 0.219849, 0.02, 0.010085, 0.399503, 0.50407, 0.05, 0.133538, 0.000952, 0.03, 0.023, 0.1205, 0.063524, 0.038323, 0.371299, 0.03, 0.041181, 0.182644, 0.088, 0.022, 0.040338, 0.137032, 0.464753, 0.05, 0.893, 1.248, 0.022, 0.3, 4.683597, 0.797, 0.004717, 0.504112, 0.009785, 0.03, 0.006436, 0.005641, 0.2937, 0.116174, 0.0183, 0.853, 0.041181, 0.03, 0.005512, 0.4, 0.24, 0.040373, 0.364929, 0.008, 0.005515, 0.163396, 0.03, 0.00377, 0.03943, 0.02386, 0.765352, 0.46, 0.115285, 0.251659, 0.049, 0.111, 0.4, 0.4, 0.364734, 0.022, 0.319989, 0.141003, 0.426611, 0.022, 0.06, 0.626486, 0.5, 0.004243, 0.059612, 0.3, 4.747239, 0.04, 0.2406, 0.021046, 0.237078, 0.428943, 0.000626, 0.000932, 0.001094, 1.906807, 0.03, 0.349154, 0.040366, 0.004549, 0.038309]
My code is given below:
import matplotlib
from utilities import get_order_book
import matplotlib.pyplot as plt
if __name__ == '__main__':
pair = 'BTC/USDT'
asks, bids = get_order_book(pair)
ask_x = []
ask_y = []
for ask in asks:
ask_x.append(ask[0])
ask_y.append(ask[1])
print(ask_x)
print(ask_y)
plt.bar(ask_x, ask_y, 2)
plt.hist(ask_x, bins=len(ask_x))
plt.show()
If you are not interested in coloring the area under the curve, you can use matplotlib.pyplot.step (pay attention to where parameter, in this case I used the default pre value):
x = [49579.41, 49580.66, 49581.39, 49585.42, 49585.43, 49586.99, 49588.0, 49588.74, 49589.62, 49589.8, 49589.81, 49590.0, 49590.37, 49590.38, 49591.38, 49591.87, 49592.84, 49593.32, 49593.86, 49594.17, 49594.36, 49595.32, 49595.54, 49595.92, 49595.97, 49596.87, 49596.88, 49596.99, 49597.06, 49597.2, 49597.21, 49597.52, 49597.64, 49597.79, 49598.08, 49598.46, 49598.69, 49598.98, 49599.52, 49600.0, 49600.14, 49600.26, 49600.34, 49600.47, 49600.62, 49600.73, 49601.0, 49601.25, 49601.3, 49601.71, 49601.85, 49601.93, 49602.2, 49602.7, 49602.93, 49603.12, 49603.15, 49603.23, 49603.56, 49604.27, 49604.31, 49604.32, 49604.89, 49604.9, 49605.12, 49605.21, 49605.5, 49605.59, 49606.0, 49606.13, 49606.39, 49606.62, 49607.2, 49607.5, 49607.68, 49607.69, 49607.71, 49607.76, 49608.05, 49608.11, 49608.67, 49609.71, 49610.0, 49610.07, 49610.18, 49610.53, 49610.54, 49611.72, 49612.1, 49612.25, 49612.42, 49612.71, 49612.99, 49613.0, 49613.42, 49613.45, 49613.53, 49613.62, 49613.84, 49613.92]
y = [4.514622, 1.0, 1.0, 0.014002, 0.04, 0.012384, 0.005441, 0.04, 0.254507, 0.219849, 0.559163, 0.14, 0.219849, 0.02, 0.010085, 0.399503, 0.50407, 0.05, 0.133538, 0.000952, 0.03, 0.023, 0.1205, 0.063524, 0.038323, 0.371299, 0.03, 0.041181, 0.182644, 0.088, 0.022, 0.040338, 0.137032, 0.464753, 0.05, 0.893, 1.248, 0.022, 0.3, 4.683597, 0.797, 0.004717, 0.504112, 0.009785, 0.03, 0.006436, 0.005641, 0.2937, 0.116174, 0.0183, 0.853, 0.041181, 0.03, 0.005512, 0.4, 0.24, 0.040373, 0.364929, 0.008, 0.005515, 0.163396, 0.03, 0.00377, 0.03943, 0.02386, 0.765352, 0.46, 0.115285, 0.251659, 0.049, 0.111, 0.4, 0.4, 0.364734, 0.022, 0.319989, 0.141003, 0.426611, 0.022, 0.06, 0.626486, 0.5, 0.004243, 0.059612, 0.3, 4.747239, 0.04, 0.2406, 0.021046, 0.237078, 0.428943, 0.000626, 0.000932, 0.001094, 1.906807, 0.03, 0.349154, 0.040366, 0.004549, 0.038309]
fig, ax = plt.subplots()
ax.step(x, y)
plt.show()
Otherwise, if you want to color the area under the curve, you can use matplotlib.pyplot.stairs, as already suggested by BigBen in the comment. Pay attention to the fact you need to add a value to x array, because this time it represents edges, not data points.
x.append(x[-1])
fig, ax = plt.subplots()
ax.stairs(edges = x, values = y, fill = True)
plt.show()
It seems the two plots are different: this because in matplotlib.pyplot.step I used the default value for where parameter; on the contrary, matplotlib.pyplot.stairs uses a post interpolation:
x.append(x[-1])
fig, ax = plt.subplots()
ax.step(x[:-1], y, where = 'post')
ax.stairs(edges = x, values = y, fill = True)
plt.show()
The accepted answer gets you part of the way there, but an actual depth chart is monotonically decreasing/increasing for bids/asks because it includes a cumulative summation of the limit orders. The rationale is that the depth chart will show you "how many units are available for a given price or better" :
For example, say you have the following bids for TKR:
| Price | Quantity |
|---|---|
| $10 | 3 |
| $5 | 4 |
| $3 | 10 |
If you place a sell order TKR at $5, then you will be able to sell 7 units at that price; 4 will be bought at $5, and 3 (which were willing to be bought at $10) will definitely be bought at the more appealing $5. Therefore, the depth chart at Price=5 should show a quantity of 7, not 4. Likewise, at Price=3, the quantity would be 17.
Going back to your question/example, the easiest way that I’ve found (all credit goes to this notebook/article) would be to leverage seaborn’s ecdfplot as follows:
import random
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
# your original data
ask_price = [49579.41, 49580.66, 49581.39, 49585.42, 49585.43, 49586.99, 49588.0, 49588.74, 49589.62, 49589.8, 49589.81, 49590.0, 49590.37, 49590.38, 49591.38, 49591.87, 49592.84, 49593.32, 49593.86, 49594.17, 49594.36, 49595.32, 49595.54, 49595.92, 49595.97, 49596.87, 49596.88, 49596.99, 49597.06, 49597.2, 49597.21, 49597.52, 49597.64, 49597.79, 49598.08, 49598.46, 49598.69, 49598.98, 49599.52, 49600.0, 49600.14, 49600.26, 49600.34, 49600.47, 49600.62, 49600.73, 49601.0, 49601.25, 49601.3, 49601.71, 49601.85, 49601.93, 49602.2, 49602.7, 49602.93, 49603.12, 49603.15, 49603.23, 49603.56, 49604.27, 49604.31, 49604.32, 49604.89, 49604.9, 49605.12, 49605.21, 49605.5, 49605.59, 49606.0, 49606.13, 49606.39, 49606.62, 49607.2, 49607.5, 49607.68, 49607.69, 49607.71, 49607.76, 49608.05, 49608.11, 49608.67, 49609.71, 49610.0, 49610.07, 49610.18, 49610.53, 49610.54, 49611.72, 49612.1, 49612.25, 49612.42, 49612.71, 49612.99, 49613.0, 49613.42, 49613.45, 49613.53, 49613.62, 49613.84, 49613.92]
ask_qty = [4.514622, 1.0, 1.0, 0.014002, 0.04, 0.012384, 0.005441, 0.04, 0.254507, 0.219849, 0.559163, 0.14, 0.219849, 0.02, 0.010085, 0.399503, 0.50407, 0.05, 0.133538, 0.000952, 0.03, 0.023, 0.1205, 0.063524, 0.038323, 0.371299, 0.03, 0.041181, 0.182644, 0.088, 0.022, 0.040338, 0.137032, 0.464753, 0.05, 0.893, 1.248, 0.022, 0.3, 4.683597, 0.797, 0.004717, 0.504112, 0.009785, 0.03, 0.006436, 0.005641, 0.2937, 0.116174, 0.0183, 0.853, 0.041181, 0.03, 0.005512, 0.4, 0.24, 0.040373, 0.364929, 0.008, 0.005515, 0.163396, 0.03, 0.00377, 0.03943, 0.02386, 0.765352, 0.46, 0.115285, 0.251659, 0.049, 0.111, 0.4, 0.4, 0.364734, 0.022, 0.319989, 0.141003, 0.426611, 0.022, 0.06, 0.626486, 0.5, 0.004243, 0.059612, 0.3, 4.747239, 0.04, 0.2406, 0.021046, 0.237078, 0.428943, 0.000626, 0.000932, 0.001094, 1.906807, 0.03, 0.349154, 0.040366, 0.004549, 0.038309]
# generate fake bid data for illustration purposes
bid_price = [p-(0.001*p) for p in ask_price]
bid_qty = [q*(2*random.random()) for q in ask_qty]
ask_df = pd.DataFrame({'price': ask_price, 'quantity': ask_qty})
bid_df = pd.DataFrame({'price': bid_price, 'quantity': bid_qty})
fig, ax = plt.subplots()
ax.set_title(f"Order Book Depth Chart")
sns.ecdfplot(x="price", weights="quantity", stat="count",
data=ask_df, ax=ax, color="red")
sns.ecdfplot(x="price", weights="quantity", stat="count",
complementary=True, data=bid_df, ax=ax, color="green")
# complementary=True allows reflects that lower bids are "better"
ax.set_xlabel("Price")
ax.set_ylabel("Quantity")
with the following result: