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test.py
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test.py
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# import numpy as np
# ask = np.load("/homes/80/kang/AlphaTrade/ask.npy")
# bid = np.load("/homes/80/kang/AlphaTrade/bid.npy")
# getBestAsksQtys = lambda x: x[:, np.argmin(np.where(x[:, :, 0] >= 0, x[:, :, 0], jnp.inf), axis=1), 1][:,0]
# # getBestBidsQtys = lambda x: x[:, np.argmax(x[:, 0, :], axis=1), 1]
# getBestBidsQtys = lambda x: x[:, np.argmax(x[:, :, 0], axis=0), 1]
# # bestAsksQtys, bestBidsQtys = map(lambda func, orders: func(orders), [getBestAsksQtys, getBestBidsQtys], [state.ask_raw_orders, state.bid_raw_orders])
# bestAsksQtys = getBestAsksQtys(ask)
# bestBidsQtys = getBestBidsQtys(bid)
# bestBidsQtys
# lst= []
# for arr in bid:
# lst.append(arr[np.argmax(arr[:,0]),1])
# print(lst)
# # ================================================================================
import jax
import jax.numpy as jnp
executed_ = jnp.array(
[[ 31272500, 20, -9001, -8999, 43200, 1922666],
[ 31271100, 1, -9003, 201017109, 43201, 432381457],
[ 31269600, 14, -9005, 201017109, 43201, 432381457],
[ 31268700, 1, -9007, 201017109, 43201, 432381457],
[ 31267900, 1, -9009, 201017109, 43201, 432381457],
[ 31267000, 1, 201446565, 201017109, 43201, 432381457],
[ 31267000, 1, 201447253, 201017109, 43201, 432381457],
[ 31266900, 1, -9011, 197160893, 43201, 432413666],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0]])
# # a = jnp.array([0, 1, 0, 0, 0, 0])
# # b = jnp.ones(100,dtype=jnp.int32).T
# # @jax.jit
# # def get1():
# # E = executed.T @ executed
# # e = executed @ a
# # esum= e @ b
# # vwap = E[0, 1] / esum
# # return vwap
# # @jax.jit
# # def get2():
# # E = executed.T @ executed
# # e = executed @ a
# # vwap = E[0, 1] / e.sum()
# # return vwap
# # @jax.jit
# # def get3():
# # vwap = (executed[:, 0] * executed[:, 1]).sum() / executed[:1].sum()
# # return vwap
# # import jax
# # import jax.numpy as jnp
# # import timeit
# # execution_time0 = timeit.timeit(lambda: jax.device_get(get1().block_until_ready()), number=10)
# # # print("Execution time for get1():", execution_time, "seconds")
# # # Measure the execution time for get2()
# # execution_time0 = timeit.timeit(lambda: jax.device_get(get2().block_until_ready()), number=10)
# # # print("Execution time for get2():", execution_time, "seconds")
# # # Measure the execution time for get3()
# # execution_time0 = timeit.timeit(lambda: jax.device_get(get3().block_until_ready()), number=10)
# # # Measure the execution time for get1()
# # execution_time = timeit.timeit(lambda: jax.device_get(get1().block_until_ready()), number=100000)
# # print("Execution time for get1():", execution_time, "seconds")
# # # Measure the execution time for get2()
# # execution_time = timeit.timeit(lambda: jax.device_get(get2().block_until_ready()), number=100000)
# # print("Execution time for get2():", execution_time, "seconds")
# # # Measure the execution time for get3()
# # execution_time = timeit.timeit(lambda: jax.device_get(get3().block_until_ready()), number=100000)
# # print("Execution time for get3():", execution_time, "seconds")
# # # # Perform the computation and measure the time
# # ================================================================================
# import jax
# import jax.numpy as jnp
# import timeit
# @jax.jit
# def get_agent1():
# e = jnp.sign(executed)
# e1 = e-1
# e2 = jnp.sign(-1 * jnp.bitwise_and(e1 , e) )
# e3=jnp.multiply(e2, executed)
# e4= jnp.sign(jnp.bitwise_and(e3, jnp.sign(e3 + 9000)))
# e5 = jnp.bitwise_and(e4,jnp.sign(1+e4))
# return e5
# # e6 = jnp.any(e5 == 1, axis=1)[:, jnp.newaxis]
# # e7 = e6*executed
# # return e7
# @jax.jit
# def get_agent2():
# mask2 = ((-9000 < executed[:, 2]) & (executed[:, 2] < 0)) | ((-9000 < executed[:, 3]) & (executed[:, 3] < 0))
# agentTrades = jnp.where(mask2[:, jnp.newaxis], executed, 0)
# return agentTrades
# execution_time0 = timeit.timeit(lambda: jax.device_get(get_agent1().block_until_ready()), number=1)
# execution_time0 = timeit.timeit(lambda: jax.device_get(get_agent2().block_until_ready()), number=1)
# execution_time0 = timeit.timeit(lambda: jax.device_get(get_agent1().block_until_ready()), number=1)
# print("Execution time for get1():", execution_time0, "seconds")
# # Measure the execution time for get2()
# execution_time0 = timeit.timeit(lambda: jax.device_get(get_agent2().block_until_ready()), number=1)
# print("Execution time for get2():", execution_time0, "seconds")
# # Measure the execution time for get1()
# execution_time = timeit.timeit(lambda: jax.device_get(get_agent1().block_until_ready()), number=100000)
# print("Execution time for get1():", execution_time, "seconds")
# # Measure the execution time for get2()
# execution_time = timeit.timeit(lambda: jax.device_get(get_agent2().block_until_ready()), number=100000)
# print("Execution time for get2():", execution_time, "seconds")
# # ================================================================================
import jax
import jax.numpy as jnp
import timeit
trades = jnp.array([
[ 31272500, 20, -9001, -8999, 43200, 1922666],
[ 31271100, 1, -9003, 201017109, 43201, 432381457],
[ 31269600, 14, -9005, 201017109, 43201, 432381457],
[ 31268700, 1, -9007, 201017109, 43201, 432381457],
[ 31267900, 1, -9009, 201017109, 43201, 432381457],
[ 31267000, 1, 201446565, 201017109, 43201, 432381457],
[ 31267000, 1, 201447253, 201017109, 43201, 432381457],
[ 31266900, 1, -9011, 197160893, 43201, 432413666],
[ -1, -1, -1, -1, -1, -1],
[ -1, -1, -1, -1, -1, -1],
[ -1, -1, -1, -1, -1, -1],
[ -1, -1, -1, -1, -1, -1],
[ -1, -1, -1, -1, -1, -1],
[ -1, -1, -1, -1, -1, -1],
[ -1, -1, -1, -1, -1, -1],
[ -1, -1, -1, -1, -1, -1],
[ -1, -1, -1, -1, -1, -1],
[ -1, -1, -1, -1, -1, -1],
[ -1, -1, -1, -1, -1, -1],
[ -1, -1, -1, -1, -1, -1],
[ -1, -1, -1, -1, -1, -1],
[ -1, -1, -1, -1, -1, -1],
[ -1, -1, -1, -1, -1, -1],
[ -1, -1, -1, -1, -1, -1],
[ -1, -1, -1, -1, -1, -1],
[ -1, -1, -1, -1, -1, -1],
[ -1, -1, -1, -1, -1, -1],
[ -1, -1, -1, -1, -1, -1],
[ -1, -1, -1, -1, -1, -1],
[ -1, -1, -1, -1, -1, -1],
[ -1, -1, -1, -1, -1, -1],
[ -1, -1, -1, -1, -1, -1],
[ -1, -1, -1, -1, -1, -1],
[ -1, -1, -1, -1, -1, -1],
[ -1, -1, -1, -1, -1, -1],
[ -1, -1, -1, -1, -1, -1],
[ -1, -1, -1, -1, -1, -1],
[ -1, -1, -1, -1, -1, -1],
[ -1, -1, -1, -1, -1, -1],
[ -1, -1, -1, -1, -1, -1],
[ -1, -1, -1, -1, -1, -1],
[ -1, -1, -1, -1, -1, -1],
[ -1, -1, -1, -1, -1, -1],
[ -1, -1, -1, -1, -1, -1],
[ -1, -1, -1, -1, -1, -1],
[ -1, -1, -1, -1, -1, -1],
[ -1, -1, -1, -1, -1, -1],
[ -1, -1, -1, -1, -1, -1],
[ -1, -1, -1, -1, -1, -1],
[ -1, -1, -1, -1, -1, -1],
[ -1, -1, -1, -1, -1, -1],
[ -1, -1, -1, -1, -1, -1],
[ -1, -1, -1, -1, -1, -1],
[ -1, -1, -1, -1, -1, -1],
[ -1, -1, -1, -1, -1, -1],
[ -1, -1, -1, -1, -1, -1],
[ -1, -1, -1, -1, -1, -1],
[ -1, -1, -1, -1, -1, -1],
[ -1, -1, -1, -1, -1, -1],
[ -1, -1, -1, -1, -1, -1],
[ -1, -1, -1, -1, -1, -1],
[ -1, -1, -1, -1, -1, -1],
[ -1, -1, -1, -1, -1, -1],
[ -1, -1, -1, -1, -1, -1],
[ -1, -1, -1, -1, -1, -1],
[ -1, -1, -1, -1, -1, -1],
[ -1, -1, -1, -1, -1, -1],
[ -1, -1, -1, -1, -1, -1],
[ -1, -1, -1, -1, -1, -1],
[ -1, -1, -1, -1, -1, -1],
[ -1, -1, -1, -1, -1, -1],
[ -1, -1, -1, -1, -1, -1],
[ -1, -1, -1, -1, -1, -1],
[ -1, -1, -1, -1, -1, -1],
[ -1, -1, -1, -1, -1, -1],
[ -1, -1, -1, -1, -1, -1],
[ -1, -1, -1, -1, -1, -1],
[ -1, -1, -1, -1, -1, -1],
[ -1, -1, -1, -1, -1, -1],
[ -1, -1, -1, -1, -1, -1],
[ -1, -1, -1, -1, -1, -1],
[ -1, -1, -1, -1, -1, -1],
[ -1, -1, -1, -1, -1, -1],
[ -1, -1, -1, -1, -1, -1],
[ -1, -1, -1, -1, -1, -1],
[ -1, -1, -1, -1, -1, -1],
[ -1, -1, -1, -1, -1, -1],
[ -1, -1, -1, -1, -1, -1],
[ -1, -1, -1, -1, -1, -1],
[ -1, -1, -1, -1, -1, -1],
[ -1, -1, -1, -1, -1, -1],
[ -1, -1, -1, -1, -1, -1],
[ -1, -1, -1, -1, -1, -1],
[ -1, -1, -1, -1, -1, -1],
[ -1, -1, -1, -1, -1, -1],
[ -1, -1, -1, -1, -1, -1],
[ -1, -1, -1, -1, -1, -1],
[ -1, -1, -1, -1, -1, -1],
[ -1, -1, -1, -1, -1, -1],
[ -1, -1, -1, -1, -1, -1]])
agentTrades_ = jnp.array(
[ [31272500, 20, -9001, -8999, 43200, 1922666],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0]])
rewardValue_ = jnp.array(2.6544957e+08, dtype=jnp.float32)
# # reward=self.get_reward(state, params)
# executed = jnp.where((trades[:, 0] > 0)[:, jnp.newaxis], trades, 0)
# mask2 = ((-9000 < executed[:, 2]) & (executed[:, 2] < 0)) | ((-9000 < executed[:, 3]) & (executed[:, 3] < 0))
# agentTrades = jnp.where(mask2[:, jnp.newaxis], executed, 0)
# vwap = (executed[:,0] * executed[:,1]).sum()/ executed[:1].sum()
# advantage = (agentTrades[:,0] * agentTrades[:,1]).sum() - vwap * agentTrades[:,1].sum()
# Lambda = 0.5 # FIXME shoud be moved to EnvState or EnvParams
# drift = agentTrades[:,1].sum() * (vwap - 36000000)
# rewardValue = advantage + Lambda * drift
# reward = jnp.sign(agentTrades[0,0]) * rewardValue # if no value agentTrades then the reward is set to be zero
# reward=jnp.nan_to_num(reward)
# a = jnp.array([0, 1, 0, 0, 0, 0])
# b = jnp.ones(100,dtype=jnp.int32).T
# @jax.jit
# def get1(trades):
# # reward=self.get_reward(state, params)
# executed = jnp.where((trades[:, 0] > 0)[:, jnp.newaxis], trades, 0)
# mask2 = ((-9000 < executed[:, 2]) & (executed[:, 2] < 0)) | ((-9000 < executed[:, 3]) & (executed[:, 3] < 0))
# agentTrades = jnp.where(mask2[:, jnp.newaxis], executed, 0)
# return agentTrades
# @jax.jit
# def get2(executed, agentTrades):
# E = executed.T @ executed
# e = executed @ a
# esum= e @ b
# vwap = E[0, 1] / esum
# # vwap = (executed[:,0] * executed[:,1]).sum()/ executed[:1].sum()
# advantage = (agentTrades[:,0] * agentTrades[:,1]).sum() - vwap * agentTrades[:,1].sum()
# Lambda = 0.5 # FIXME shoud be moved to EnvState or EnvParams
# drift = agentTrades[:,1].sum() * (vwap - 36000000)
# rewardValue = advantage + Lambda * drift
# return rewardValue
# @jax.jit
# def get3(agentTrades, rewardValue):
# reward = jnp.sign(agentTrades[0,0]) * rewardValue # if no value agentTrades then the reward is set to be zero
# reward=jnp.nan_to_num(reward)
# return reward
# @jax.jit
# def get4(trades):
# return get3(agentTrades_,get2(executed_, get1(trades)))
# # execution_time0 = timeit.timeit(lambda: jax.device_get(get1(trades).block_until_ready()), number=1)
# # execution_time0 = timeit.timeit(lambda: jax.device_get(get2(executed_, agentTrades_).block_until_ready()), number=1)
# # execution_time0 = timeit.timeit(lambda: jax.device_get(get3(agentTrades_, rewardValue_).block_until_ready()), number=1)
# execution_time0 = timeit.timeit(lambda: jax.device_get(get4(trades).block_until_ready()), number=1)
# # execution_time0 = timeit.timeit(lambda: jax.device_get(get1(trades).block_until_ready()), number=1)
# # print("Execution time for get1():", execution_time0, "seconds")
# # execution_time0 = timeit.timeit(lambda: jax.device_get(get2(executed_, agentTrades_).block_until_ready()), number=1)
# # print("Execution time for get2():", execution_time0, "seconds")
# # execution_time0 = timeit.timeit(lambda: jax.device_get(get3(agentTrades_, rewardValue_).block_until_ready()), number=1)
# # print("Execution time for get3():", execution_time0, "seconds")
# execution_time0 = timeit.timeit(lambda: jax.device_get(get4(trades).block_until_ready()), number=1)
# print("Execution time for get4():", execution_time0, "seconds")
# # #------------------------ testing ------------------------
# # # Measure the execution time for get1()
# # execution_time = timeit.timeit(lambda: jax.device_get(get1(trades).block_until_ready()), number=100000)
# # print("Execution time for get1():", execution_time, "seconds")
# # # Measure the execution time for get2()
# # execution_time = timeit.timeit(lambda: jax.device_get(get2(executed_, agentTrades_).block_until_ready()), number=100000)
# # print("Execution time for get2():", execution_time, "seconds")
# # # Measure the execution time for get3()
# # execution_time = timeit.timeit(lambda: jax.device_get(get3(agentTrades_, rewardValue_).block_until_ready()), number=100000)
# # print("Execution time for get3():", execution_time, "seconds")
# # # # Perform the computation and measure the time
# # # Measure the execution time for get3()
# # execution_time = timeit.timeit(lambda: jax.device_get(get4(trades).block_until_ready()), number=100000)
# # print("Execution time for get4():", execution_time, "seconds")
# # # # Perform the computation and measure the time
# # ================================================================================
trades_big = jnp.tile(trades,(1000,1))
@jax.jit
def get1(trades):
# reward=self.get_reward(state, params)
executed = jnp.where((trades[:, 0] > 0)[:, jnp.newaxis], trades, 0)
mask2 = ((-9000 < executed[:, 2]) & (executed[:, 2] < 0)) | ((-9000 < executed[:, 3]) & (executed[:, 3] < 0))
agentTrades = jnp.where(mask2[:, jnp.newaxis], executed, 0)
return agentTrades
execution_time0 = timeit.timeit(lambda: jax.device_get(get1(trades).block_until_ready()), number=1)
execution_time0 = timeit.timeit(lambda: jax.device_get(get1(trades).block_until_ready()), number=1)
print("Execution time for get1_sml():", execution_time0, "seconds")
execution_time0 = timeit.timeit(lambda: jax.device_get(get1(trades_big).block_until_ready()), number=1)
print("Execution time for get1_big():", execution_time0, "seconds")
# #------------------------ testing ------------------------
# Measure the execution time for get1()
execution_time = timeit.timeit(lambda: jax.device_get(get1(trades).block_until_ready()), number=100*1000)
print("Execution time for get1_sml():", execution_time, "seconds")
# Measure the execution time for get2()
execution_time = timeit.timeit(lambda: jax.device_get(get1(trades_big).block_until_ready()), number=100*1000)
print("Execution time for get1_big():", execution_time, "seconds")
# # ================================================================================
import jax
import jax.numpy as jnp
done = jnp.array([False, True, False])
done = jnp.array(
[[False] * 510 , [True] * 510, [False] * 510]
)
obs_re = jnp.ones((3,510))
obs_st = jnp.zeros((3,510))
jax.lax.select(done, obs_re, obs_st)
import jax.numpy as jnp
import jax
agentTrades = \
jnp.array([[ 31272500, 9, -8999, -8999, 43200, 1922666],
[ 31278200, 13, -8998, -8998, 43200, 1922666],
[ 31279400, 1, 201543641, -8997, 43200, 1922666],
[ 31279400, 1, 201543925, -8996, 43200, 1922666],
[ 31285900, 25, -8996, -8995, 43204, 55146332],
[ 31285900, 3, -8996, -8994, 43204, 55146332],
[ 31283900, 8, -8997, -8994, 43204, 55146332],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0]])
# quantities = agentTrades[:, 1]
# cut_idx = jnp.argmax(jnp.cumsum(quantities) > remainQuant)
# # Handle special case when all quantities are smaller than remainQuant
# cut_idx = jnp.where(cut_idx == 0, len(quantities), cut_idx)
# remaining_in_cut_idx = remainQuant - jnp.where(cut_idx > 0, jnp.cumsum(quantities)[cut_idx - 1], 0)
# quantities = agentTrades[:, 1]
# cumsum_quantities = jnp.cumsum(quantities)
# cut_idx = jnp.argmax(cumsum_quantities > remainQuant)
# # Handle special case when all quantities are smaller than remainQuant
# cut_idx = jnp.where(cut_idx == 0, len(quantities), cut_idx)
# remaining_in_cut_idx = remainQuant - jnp.where(cut_idx > 0, cumsum_quantities[cut_idx - 1], 0)
# new_second_col = jnp.where(jnp.arange(len(quantities)) == cut_idx, remaining_in_cut_idx, quantities)
# new_second_col = jnp.where(jnp.arange(len(quantities)) > cut_idx, 0, new_second_col)
# truncated_agentTrades = jnp.where(jnp.arange(len(quantities))[:, None] >= cut_idx, 0, agentTrades)
# truncated_agentTrades = truncated_agentTrades.at[:, 1].set(new_second_col)
# def truncate_agent_trades(agentTrades, remainQuant):
# quantities = agentTrades[:, 1]
# total_quant = jnp.sum(quantities)
# # If remaining quantity is greater than total quantity, return original agentTrades
# if remainQuant >= total_quant:
# return agentTrades
# cumsum_quantities = jnp.cumsum(quantities)
# # If remaining quantity is smaller than the first quantity, return immediately.
# if remainQuant <= quantities[0]:
# new_agentTrades = jnp.zeros_like(agentTrades)
# new_agentTrades = new_agentTrades.at[0, :].set(agentTrades[0])
# new_agentTrades = new_agentTrades.at[0, 1].set(remainQuant)
# return new_agentTrades
# cut_idx = jnp.argmax(cumsum_quantities >= remainQuant)
# remaining_in_cut_idx = remainQuant - cumsum_quantities[cut_idx - 1]
# new_second_col = jnp.where(jnp.arange(len(quantities)) < cut_idx, quantities, 0)
# new_second_col = jnp.where(jnp.arange(len(quantities)) == cut_idx, remaining_in_cut_idx, new_second_col)
# truncated_agentTrades = agentTrades.at[:, 1].set(new_second_col)
# zero_row = jnp.zeros_like(agentTrades[0])
# truncated_agentTrades = jnp.where(jnp.arange(len(quantities))[:, None] > cut_idx, zero_row, truncated_agentTrades)
# return truncated_agentTrades
remainQuant = 2
remainQuant = 25
remainQuant = 23
remainQuant = 230
remainQuant = 60
remainQuant = 61
remainQuant = 59
# remainQuant = 52
# def truncate_agent_trades(agentTrades, remainQuant):
# quantities = agentTrades[:, 1]
# if remainQuant >= jnp.sum(quantities):
# return agentTrades
# elif remainQuant <= quantities[0]:
# new_agentTrades = jnp.zeros_like(agentTrades)
# new_agentTrades = new_agentTrades.at[0, :].set(agentTrades[0])
# new_agentTrades = new_agentTrades.at[0, 1].set(remainQuant)
# return new_agentTrades
# elif quantities[0] < remainQuant < jnp.sum(quantities):
# cumsum_quantities = jnp.cumsum(quantities)
# cut_idx = jnp.argmax(cumsum_quantities >= remainQuant)
# remaining_in_cut_idx = remainQuant - cumsum_quantities[cut_idx - 1]
# new_second_col = jnp.where(jnp.arange(len(quantities)) < cut_idx, quantities, 0)
# new_second_col = jnp.where(jnp.arange(len(quantities)) == cut_idx, remaining_in_cut_idx, new_second_col)
# truncated_agentTrades = agentTrades.at[:, 1].set(new_second_col)
# zero_row = jnp.zeros_like(agentTrades[0])
# truncated_agentTrades = jnp.where(jnp.arange(len(quantities))[:, None] > cut_idx, zero_row, truncated_agentTrades)
# return truncated_agentTrades
remainQuant = 2
# remainQuant = 25
remainQuant = 23
remainQuant = 230
remainQuant = 60
# remainQuant = 61
# remainQuant = 59
remainQuant = 52
import jax.numpy as jnp
def truncate_agent_trades(agentTrades, remainQuant):
quantities = agentTrades[:, 1]
cumsum_quantities = jnp.cumsum(quantities)
cut_idx = jnp.argmax(cumsum_quantities >= remainQuant)
truncated_agentTrades = jnp.where(jnp.arange(len(quantities))[:, jnp.newaxis] > cut_idx, jnp.zeros_like(agentTrades[0]), agentTrades.at[:, 1].set(jnp.where(jnp.arange(len(quantities)) < cut_idx, quantities, jnp.where(jnp.arange(len(quantities)) == cut_idx, remainQuant - cumsum_quantities[cut_idx - 1], 0))))
return jnp.where(remainQuant >= jnp.sum(quantities), agentTrades, jnp.where(remainQuant <= quantities[0], jnp.zeros_like(agentTrades).at[0, :].set(agentTrades[0]).at[0, 1].set(remainQuant), truncated_agentTrades))
def truncate_agent_trades(agentTrades, remainQuant):
quantities = agentTrades[:, 1]
cumsum_quantities = jnp.cumsum(quantities)
cut_idx = jnp.argmax(cumsum_quantities >= remainQuant)
truncated_agentTrades = jnp.where(
jnp.arange(len(quantities))[:, jnp.newaxis] > cut_idx,
jnp.zeros_like(agentTrades[0]),
agentTrades.at[:, 1].set(
jnp.where(
jnp.arange(len(quantities)) < cut_idx,
quantities,
jnp.where(
jnp.arange(len(quantities)) == cut_idx,
remainQuant - cumsum_quantities[cut_idx - 1],
0
)
)
)
)
return jnp.where(
remainQuant >= jnp.sum(quantities),
agentTrades,
jnp.where(
remainQuant <= quantities[0],
jnp.zeros_like(agentTrades).at[0, :].set(agentTrades[0]).at[0, 1].set(remainQuant),
truncated_agentTrades
)
)
# def truncate_agent_trades(agentTrades, remainQuant):
# quantities = agentTrades[:, 1]
# new_agentTrades = jnp.zeros_like(agentTrades)
# new_agentTrades = new_agentTrades.at[0, :].set(agentTrades[0])
# new_agentTrades = new_agentTrades.at[0, 1].set(remainQuant)
# cumsum_quantities = jnp.cumsum(quantities)
# cut_idx = jnp.argmax(cumsum_quantities >= remainQuant)
# remaining_in_cut_idx = remainQuant - cumsum_quantities[cut_idx - 1]
# new_second_col = jnp.where(jnp.arange(len(quantities)) < cut_idx, quantities, 0)
# new_second_col = jnp.where(jnp.arange(len(quantities)) == cut_idx, remaining_in_cut_idx, new_second_col)
# truncated_agentTrades = agentTrades.at[:, 1].set(new_second_col)
# zero_row = jnp.zeros_like(agentTrades[0])
# truncated_agentTrades = jnp.where(jnp.arange(len(quantities))[:, None] > cut_idx, zero_row, truncated_agentTrades)
# return jnp.where(remainQuant >= jnp.sum(quantities), agentTrades, jnp.where(remainQuant <= quantities[0], new_agentTrades, truncated_agentTrades))
truncated_agentTrades = truncate_agent_trades(agentTrades, remainQuant)
truncated_agentTrades
agentTrades[:,1].sum()