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matplotAnim.py
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import random
import matplotlib.pyplot as plt
import matplotlib.animation as animation
from kite_trade import *
import time
from collections import deque
from datetime import datetime
from pathlib import Path
import pandas as pd
with open('enctoken.txt', 'r') as wr:
token = wr.read()
kite = KiteApp(enctoken=token)
scriptName = "NFO:BANKNIFTY24JANFUT"
r = 25
v1 = deque([0] * r, maxlen=r)
v2 = deque([0] * r, maxlen=r)
v3 = deque([0] * r, maxlen=r)
x = [i for i in range(r)]
def giveyrange(ary,pmin,pmax):
min_value = ary[-1]*pmin
max_value = ary[-1]*pmax
return min_value, max_value
# Function to calculate percentage change
def calculate_percentage_change(current, previous):
if previous == 0:
return 0
pc = ((current - previous) / previous) * 100
pc = round(pc, 3)
return pc
global rangei,v1min,v1max,v2min,v2max,v3min,v3max
rangei=0
data_buffer = []
outcsv = R'D:\marketDepthIndi\marketDepth.csv'
# Combined update function
def update_raw_data(frame):
global rangei,v1min,v1max,v2min,v2max,v3min,v3max,data_buffer
myquote = kite.quote([scriptName])
curbuy = float(myquote[scriptName]['buy_quantity'])
curSell = float(myquote[scriptName]['sell_quantity'])
curVol = float(myquote[scriptName]['volume'])
v1.append(curbuy)
v2.append(curSell)
v3.append(curVol)
curTime = datetime.now()
data_buffer.append([scriptName,curTime,curbuy,curSell,curVol])
if len(data_buffer)>3:
dataToStoredf = pd.DataFrame(data_buffer,columns=['Script','Cur_Time','BuyQ','SellQ','Vol'])
if Path(outcsv).exists():
dataToStoredf.to_csv('marketDepth_values.csv',index=False,mode='a',header=False)
else:
dataToStoredf.to_csv('marketDepth_values.csv',index=False)
data_buffer = []
# Ensure deques have the same length as x
len_x = len(x)
v1_data = list(v1)[-len_x:]
v2_data = list(v2)[-len_x:]
v3_data = list(v3)[-len_x:]
# Adding new data to each dataset
# Clear the previous plot
ax1_raw.clear()
ax2_raw.clear()
ax3_raw.clear()
if rangei==0:
v1min,v1max = giveyrange(v1_data,0.6,1.4)
v2min,v2max = giveyrange(v2_data,0.6,1.4)
v3min,v3max = giveyrange(v3_data,0.99,1.01)
rangei+=1
if v1_data[-1] < v1min or v1_data[-1] > v1max:
v1min,v1max = giveyrange(v1_data,0.6,1.4)
if v2_data[-1] < v2min or v2_data[-1] > v2max:
v2min,v2max = giveyrange(v2_data,0.6,1.4)
if v3_data[-1] < v3min or v3_data[-1] > v3max:
v3min,v3max = giveyrange(v3_data,0.99,1.01)
# print(f'min:{v3min} | max:{v3max}')
# Plotting raw data for v1, v2, and v3
ax1_raw.plot(x, v1_data, 'green')
# ax1_raw.set_ylabel('v1', color='green')
# ax1_raw.tick_params(axis='y', labelcolor='green')
ax1_raw.set_ylim(v1min,v1max)
ax2_raw.plot(x, v2_data, 'red')
# ax2_raw.set_ylabel('v2', color='red')
# ax2_raw.tick_params(axis='y', labelcolor='red')
ax2_raw.set_ylim(v2min,v2max)
ax3_raw.plot(x, v3_data, 'black')
# ax3_raw.set_ylabel('v3', color='black')
# ax3_raw.tick_params(axis='y', labelcolor='black')
ax3_raw.set_ylim(v3min,v3max)
# Function to update percentage change
def update_percentage_change(frame):
# Ensure deques have the same length as x
len_x = len(x)
v1_data = list(v1)[-len_x:]
v2_data = list(v2)[-len_x:]
v3_data = list(v3)[-len_x:]
# Calculating percentage change for each dataset
v1_pc = [calculate_percentage_change(v1_data[i], v1_data[i-1]) for i in range(1, len(v1_data))]
v2_pc = [calculate_percentage_change(v2_data[i], v2_data[i-1]) for i in range(1, len(v2_data))]
v3_pc = [calculate_percentage_change(v3_data[i], v3_data[i-1]) for i in range(1, len(v3_data))]
# Clear the previous plot
ax1_pc.clear()
ax2_pc.clear()
ax3_pc.clear()
x_adjusted = x[1:]
# Plotting percentage change for v1, v2, and v3
ax1_pc.plot(x_adjusted, v1_pc[-len(x):], 'green')
# ax1_pc.set_ylabel('v1 % Change', color='green')
# ax1_pc.tick_params(axis='y', labelcolor='green')
ax1_pc.set_ylim(-50, 50)
ax2_pc.plot(x_adjusted, v2_pc[-len(x):], 'red')
# ax2_pc.set_ylabel('v2 % Change', color='red')
# ax2_pc.tick_params(axis='y', labelcolor='red')
ax2_pc.set_ylim(-50, 50)
ax3_pc.plot(x_adjusted, v3_pc[-len(x):], 'black')
# ax3_pc.set_ylabel('v3 % Change', color='black')
# ax3_pc.tick_params(axis='y', labelcolor='black')
ax3_pc.set_ylim(-0.5, 0.5)
# Creating two separate figures for raw data and percentage change
fig_raw, ax1_raw = plt.subplots(figsize=(5, 3))
plt.get_current_fig_manager().set_window_title("BSV")
ax2_raw = ax1_raw.twinx()
ax3_raw = ax1_raw.twinx()
fig_pc, ax1_pc = plt.subplots(figsize=(5, 3))
plt.get_current_fig_manager().set_window_title("PercentageChange")
ax2_pc = ax1_pc.twinx()
ax3_pc = ax1_pc.twinx()
# Creating two separate animations
ani_raw = animation.FuncAnimation(fig_raw, update_raw_data, interval=500)
ani_pc = animation.FuncAnimation(fig_pc, update_percentage_change, interval=500)
plt.show()