量化合约开发（源码），量化合约系统开发（策略及分析），量化合约系统

　　量化系统是一种基于区块链技术的交易系统，它利用智能合约来自动化交易操作，将交易信息和数据记录在区块链上，保证交易的公开透明和数据的可靠性。

　　The characteristics of quantitative trading robots:

　　1.The most obvious feature of quantitative trading is to reduce the impact of investor sentiment fluctuations,avoid making irrational investment decisions in extremely fanatical or pessimistic market situations,and avoid subjective assumptions.Quantitative trading robots use programs to turn their ideas into quantifiable strategies,using computers to only calculate and buy and sell strategies;开发策略唯：MrsFu123

　　2.Historical backtesting,implemented using computer programs,can verify the rationality of trading strategies by quantifying trading ideas;

　　3.Able to ensure the execution of transactions/profits,especially quantitative analysis at medium and low frequencies,without any supervision;

　　from __future__ import absolute_import,print_function,division

　　import os

　　from rknn.api import RKNN

　　#onnx_model='./resource/onnx/model_256x256_max_mscf_0.924553.onnx'G:/6666Ground_segmentation0813

　　onnx_model='G:/6666Ground_segmentation0813/onnx/model0124.onnx'#onnx路径

　　save_rknn_dir='G:/6666Ground_segmentation0813/rknn'#rknn保存路径

　　if __name__=='__main__':

　　#Create RKNN object

　　rknn=RKNN()

　　#pre-process config

　　print('-->Config model')

　　rknn.config(mean_values=[[83.0535,94.095,82.1865]],std_values=[[53.856,54.774,53.9325]],reorder_channel='2 1 0',target_platform=['rk1808'],batch_size=1,quantized_dtype='asymmetric_quantized-u8')#需要输入为RGB#####需要转化一下均值和归一化的值

　　#rknn.config(mean_values=[[0.0,0.0,0.0]],std_values=[[255,255,255]],reorder_channel='2 1 0',target_platform=['rv1126'],batch_size=1)#需要输入为RGB

　　print('done')

　　model_name=onnx_model[onnx_model.rfind('/')+1:]

　　#Load ONNX model

　　print('-->Loading model%s'%model_name)

　　ret=rknn.load_onnx(model=onnx_model)

　　if ret!=0:

　　print('Load%s failed!'%model_name)

　　exit(ret)

　　print('done')

　　#Build model

　　print('-->Building model')

　　#ret=rknn.build(do_quantization=False,dataset='./quantization_dataset.txt',pre_compile=True)###路哥的版本pre_compil=True离线预编译

　　ret=rknn.build(do_quantization=True,dataset='G:/6666Ground_segmentation0813/rknntxt.txt',pre_compile=False)

　　#do_quantization是否对模型进行量化，datase量化校正数据集，pre_compil模型预编译开关，预编译RKNN模型可以减少模型初始化时间，但是无法通过模拟器进行推理或性能评估

　　if ret!=0:

　　print('Build net failed!')

　　exit(ret)

　　print('done')

　　#Export RKNN model

　　print('-->Export RKNN model')

　　#save_name=model_name.replace(os.path.splitext(model_name)[-1],"_no_quant.rknn")

　　save_name=model_name.replace(os.path.splitext(model_name)[-1],"_quant.rknn")

　　ret=rknn.export_rknn(os.path.join(save_rknn_dir,save_name))

　　if ret!=0:

　　print('Export rknn failed!')

　　exit(ret)

　　print('done')

　　rknn.release()