From eb3dc17ee6ab98960e2bfca5b324b86b9f534379 Mon Sep 17 00:00:00 2001 From: infidel Date: Wed, 6 Apr 2022 14:56:09 +0700 Subject: [PATCH] init --- .gitignore | 6 + 20201203-210319.npy | Bin 0 -> 184 bytes Cprofile_model_01.ps | Bin 0 -> 551702 bytes README.md | 0 Readme.md | 5 + Readme.pdf | Bin 0 -> 42519 bytes SHOP.ps | Bin 0 -> 567102 bytes Samples_Cross_Check.tar | Bin 0 -> 5283840 bytes ScreenOutput.xlsx | 0 automatonv1.py | 63 ++++ automatonv2.py | 63 ++++ backtest.py | 14 + blast.sh | 11 + check_gap.py | 9 + djangoBackup | Bin 0 -> 122642 bytes djangoBackup.tar.gz | Bin 0 -> 122642 bytes indicator_MACD.py | 16 + loss.npy | Bin 0 -> 196 bytes loss_animation.mp4 | Bin 0 -> 262192 bytes main_model.py | 615 ++++++++++++++++++++++++++++++++++++ main_model_django.py | 616 ++++++++++++++++++++++++++++++++++++ multiplot.py | 156 ++++++++++ parser.py | 29 ++ plot_end.py | 178 +++++++++++ plot_loss.py | 50 +++ plotterpitch.py | 139 +++++++++ plotterpitch_django.py | 187 +++++++++++ plotterpitch_min.py | 172 ++++++++++ plotterpitch_min_backup.py | 173 ++++++++++ predictor.py | 624 +++++++++++++++++++++++++++++++++++++ predictor_backup.py | 624 +++++++++++++++++++++++++++++++++++++ runner.sh | 5 + screener.py | 176 +++++++++++ screener.py.pdf | Bin 0 -> 107794 bytes screener_2.py | 198 ++++++++++++ stocks.csv | 3 + symbols.txt | 11 + symbols_backup.txt | 39 +++ temp_plotter.py | 127 ++++++++ temp_pred.png | Bin 0 -> 47048 bytes torch_model.py | 589 ++++++++++++++++++++++++++++++++++ 41 files changed, 4898 insertions(+) create mode 100644 .gitignore create mode 100644 20201203-210319.npy create mode 100644 Cprofile_model_01.ps create mode 100644 README.md create mode 100644 Readme.md create mode 100644 Readme.pdf create mode 100644 SHOP.ps create mode 100644 Samples_Cross_Check.tar create mode 100644 ScreenOutput.xlsx create mode 100644 automatonv1.py create mode 100644 automatonv2.py create mode 100644 backtest.py create mode 100755 blast.sh create mode 100644 check_gap.py create mode 100644 djangoBackup create mode 100644 djangoBackup.tar.gz create mode 100644 indicator_MACD.py create mode 100644 loss.npy create mode 100644 loss_animation.mp4 create mode 100644 main_model.py create mode 100644 main_model_django.py create mode 100644 multiplot.py create mode 100644 parser.py create mode 100644 plot_end.py create mode 100644 plot_loss.py create mode 100644 plotterpitch.py create mode 100644 plotterpitch_django.py create mode 100644 plotterpitch_min.py create mode 100644 plotterpitch_min_backup.py create mode 100644 predictor.py create mode 100644 predictor_backup.py create mode 100755 runner.sh create mode 100644 screener.py create mode 100644 screener.py.pdf create mode 100644 screener_2.py create mode 100644 stocks.csv create mode 100644 symbols.txt create mode 100644 symbols_backup.txt create mode 100644 temp_plotter.py create mode 100644 temp_pred.png create mode 100644 torch_model.py diff --git a/.gitignore b/.gitignore new file mode 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HcmV?d00001 diff --git a/README.md b/README.md new file mode 100644 index 0000000..e69de29 diff --git a/Readme.md b/Readme.md new file mode 100644 index 0000000..dbea603 --- /dev/null +++ b/Readme.md @@ -0,0 +1,5 @@ +[#](#) Important Notes + +## Stock Screener (RSI or COPPOCK) + +## Implement Old Filtering diff --git a/Readme.pdf b/Readme.pdf new file mode 100644 index 0000000000000000000000000000000000000000..bebc978d212bc65dfe1ba1d0741314d2df27437e GIT binary patch literal 42519 zcmagEGqf-auxxp3+qP}nwr$(CZQHi-9ox2T>;3o5+-QQSs5XeX`Rbg0xAdU~SFK;q$7-*UQ!yH}+!Yd?S+DuR>Zr?5=V1u6o7IE0S|K(`wkTPOLC2{di1Rb%)^&ZeZQUnmn)XwDp zUF1LaKMxasDz_Q=v)@%6>op1{ zB2iQ>LS=8`f{xx?Xk27oVs-&FxsBb`@wmuLx%J&qlmVH!@o^|=F|tDt7N^FRc4Ss3 zKqp`h9U1^AGCKT7IRG;;F**&V0FZgO^XCH=Pz-ON;{ZCbsysLWGJw(reSwADjnLWk z@sDBH7UuRAU?ty@h;?ocF3l|Mj_=ArA@R5J#e5q>uYi`s$kyKO=2p^y@mUlCS$c8! zgm(8o1}z|pn;U>p0cc`u=73&I0n`de1!Y8KH9!dp>I%v#g#~{UHAh#62KP^VIwGnn zE2I_xMM6(iMgWCeG=jpiqR%sBRExL4y&N=x`p<*Er8DAt`L3Lds)VYvA`Il=d~W~- 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threads + #threadLock.acquire() + #self.status="idle" + while len(symbols)!=0: + #self.status="busy" + self.symbol=symbols.pop(0) + self.job=subprocess.call(['gnome-terminal','-x','taskset','-c',str(self.threadID),'python3','main_model.py',self.symbol,"800"]) + time.sleep(99999) + print("Prediction for %s done"%self.symbol) + print("Job for Thread Number %s done"%self.threadID) + #print_time(self.name, self.counter, 3) + # Free lock to release next thread + #threadLock.release() + +def print_time(threadName, delay, counter): + while counter: + time.sleep(delay) + print("%s: %s" % (threadName, time.ctime(time.time()))) + counter -= 1 + +#threadLock = threading.Lock() +threads = [] + + +for numberID in range(0,len(symbols)-1,1): + thread = myThread(numberID) + thread.start() + threads.append(thread) + #threadID += 1 +# Create new threads +#thread1 = myThread(1, "Thread-1", 1) +#thread2 = myThread(2, "Thread-2", 2) + +# Start new Threads +#thread1.start() +#thread2.start() + +# Add threads to thread list +#threads.append(thread1) +#threads.append(thread2) + +# Wait for all threads to complete +for t in threads: + t.join() +print("Exiting Main Thread") diff --git a/automatonv2.py b/automatonv2.py new file mode 100644 index 0000000..da07df3 --- /dev/null +++ b/automatonv2.py @@ -0,0 +1,63 @@ +import threading +import time +import subprocess + +file_symbol=open("symbols.txt","r") +symbols=file_symbol.read().splitlines() +#print(symbols) +symbols = [x.strip(' ') for x in symbols] + +#symbols=symbols[:-5] +class myThread (threading.Thread): + def __init__(self, threadID): + threading.Thread.__init__(self) + self.threadID = threadID + #self.name = name + #self.counter = counter + def run(self): + print("Starting Thread Number %s"%self.threadID) + # Get lock to synchronize threads + #threadLock.acquire() + #self.status="idle" + while len(symbols)!=0: + #self.status="busy" + self.symbol=symbols.pop(0) + self.job=subprocess.call(['gnome-terminal','-x','taskset','-c',str(self.threadID),'python3','predictor.py',self.symbol,"800"]) + time.sleep(99999) + print("Prediction for %s done"%self.symbol) + print("Job for Thread Number %s done"%self.threadID) + #print_time(self.name, self.counter, 3) + # Free lock to release next thread + #threadLock.release() + +def print_time(threadName, delay, counter): + while counter: + time.sleep(delay) + print("%s: %s" % (threadName, time.ctime(time.time()))) + counter -= 1 + +#threadLock = threading.Lock() +threads = [] + + +for numberID in range(0,len(symbols)-1,1): + thread = myThread(numberID) + thread.start() + threads.append(thread) + #threadID += 1 +# Create new threads +#thread1 = myThread(1, "Thread-1", 1) +#thread2 = myThread(2, "Thread-2", 2) + +# Start new Threads +#thread1.start() +#thread2.start() + +# Add threads to thread list +#threads.append(thread1) +#threads.append(thread2) + +# Wait for all threads to complete +for t in threads: + t.join() +print("Exiting Main Thread") diff --git a/backtest.py b/backtest.py new file mode 100644 index 0000000..8004337 --- /dev/null +++ b/backtest.py @@ -0,0 +1,14 @@ +import matplotlib.pyplot as plt +import pandas as pd +import numpy as np +import matplotlib.animation as animation +import yfinance as yf + +symbol=yf.Ticker("AAPL") +data=symbol.history(interval="1d",period="1y") + +close=data["Close"] + +print(close) +plt.plot(close) +plt.show() diff --git a/blast.sh b/blast.sh new file mode 100755 index 0000000..b48ad75 --- /dev/null +++ b/blast.sh @@ -0,0 +1,11 @@ +#!/bin/bash + +filename="./symbols.txt" +n=1 + +while read line; +do + echo "$line" + taskset -c $((n-1)) python3 ./main_model.py $line 50 & + n=$((n+1)) +done < $filename diff --git a/check_gap.py b/check_gap.py new file mode 100644 index 0000000..ec3dfa9 --- /dev/null +++ b/check_gap.py @@ -0,0 +1,9 @@ +import json + +f = open("./Samples/MELI/2021-01-30/MELI0.vezpal2") + +a = json.load(f) + +print(len(a[0])) 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a/indicator_MACD.py b/indicator_MACD.py new file mode 100644 index 0000000..2c9ffd8 --- /dev/null +++ b/indicator_MACD.py @@ -0,0 +1,16 @@ +import yfinance as yf +import pandas as pd +import matplotlib.pyplot as plt + +def macd_data(data): + tick_history = data + tick_macd = pd.DataFrame() + tick_macd['Close'] = tick_history['Close'] + tick_macd['EMA-12'] = tick_macd['Close'].ewm(span=12, adjust=False).mean() + tick_macd['EMA-26'] = tick_macd['Close'].ewm(span=26, adjust=False).mean() + tick_macd['MACD'] = tick_macd['EMA-12'] - tick_macd['EMA-26'] + tick_macd['Signal'] = tick_macd['MACD'].ewm(span=9, adjust=False).mean() + tick_macd['Gap'] = tick_macd['MACD'] - tick_macd['Signal'] + print(tick_macd.index) + + return tick_macd diff --git a/loss.npy b/loss.npy new file mode 100644 index 0000000000000000000000000000000000000000..79f0d8d041c0afaf453757a09a15d06ba4fde875 GIT binary patch literal 196 zcmbR27wQ`j$;eQ~P_3SlTAW;@Zl$1ZlV+l>qoAIaUsO_*m=~X4l#&V(cT3DEP6dh= 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z#?0>l(3loCsBiC}z9rK>Z33NMs`DYxMmgaI2^bD3-pc7i^6pAAIv<^sO zS0l^RL&%Gwfa1)~Ul#VU|F=Z?G>)bGV2zcrx{B~EC bHmodBp5YQ062IA9G&FG`w2xi6=a%Qdftb4W literal 0 HcmV?d00001 diff --git a/main_model.py b/main_model.py new file mode 100644 index 0000000..a5a712d --- /dev/null +++ b/main_model.py @@ -0,0 +1,615 @@ +import os +import sys +os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' +import tensorflow as tf +import numpy as np +import matplotlib.animation as animation +import matplotlib.pyplot as plt +import seaborn as sns +import pandas as pd +from sklearn.preprocessing import MinMaxScaler +from datetime import datetime +from datetime import timedelta +from tqdm import tqdm +sns.set() +import json +tf.compat.v1.random.set_random_seed(1234) +from matplotlib import style +# import matplotlib.backends.backend_qt5agg +# %matplotlib qt +style.use('ggplot') +import math +import yfinance as yf +import time +from datetime import date, timedelta +os.environ["CUDA_VISIBLE_DEVICES"] = "-1" +tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR) +import cProfile +import pstats + +# In[34]: + +tf.compat.v1.disable_eager_execution() +# INITIAL VARS +test_size = 14 +simulation_size = 1 + +# MODEL VARS +num_layers = 2 +size_layer = 128 +timestamp = 7 +epoch = 20 +dropout_rate = 0.8 +prediction_gap = sys.argv[2] +future_day = test_size +learning_rate = 0.01 +graph_loss = [] + +# In[35]: +# Necessary Dirs +# def date_manage(date1,date2=None): +# if date2 is None: +# date2=date1+timedelta(days=365) +# date_col=[] +# for n in range(int ((date2 - date1).days)+1): +# date_col.append(date1 + timedelta(n)) +# weekdays = [5,6] +# date_result=[] +# for dt in date_col: +# if dt.weekday() not in weekdays: +# dt.strftime("%Y-%m-%d") +# return date_result + + +# In[36]: +def loss_animate(ax, i): + json_loss = pd.DataFrame(total_loss) + ax.plot(i) + return ax + + +def loader(symbol,test_size,date): + # dateparse = lambda dates : pd.datetime.strptime(dates,'%Y-%m') + # df = pd.read_csv('../dataset/IBMCUT.csv',parse_dates=['Date'], index_col = 'Date', date_parser=dateparse) + + df=yf.Ticker(symbol) + # df=df.history(period="1y",interval="1d") + # df=df.history(start=date-timedelta(days=365),end=date,interval="1d") + df=df.history(start=date-timedelta(days=365*3),end=date,interval="1d") + df=df.reset_index(level=0) + + # df=df.drop(columns=['Dividends'], axis=1) + df=df.drop(columns=['Stock Splits'], axis=1) + + df['Up'] = df['High'].ewm(span=6,adjust=False).mean() + 2* df['High'].rolling(window=6).std() + df['Down']= df['Low'].ewm(span=8,adjust=False).mean() - 2* df['Low'].rolling(window=8).std() + + df=df.dropna() + df=df.drop(df.tail(7).index) + + date_ori = pd.to_datetime(df.iloc[:, 0]).tolist() +# for i in range(test_size): +# #date_ori.append(date_ori[-1] + timedelta(days = 1)) +# add=1 +# while ((date_ori[-1]) + timedelta(days = add)).weekday() in [5,6]: +# add=add+1 +# date_ori.append(date_ori[-1] + timedelta(days = add)) + date_ori = pd.Series(date_ori).dt.strftime(date_format = '%Y-%m-%d').tolist() + print(len(df),len(date_ori)) + return df,date_ori + +def trueloader(symbol,test_size,date): + #df2 = pd.read_csv(symbol) + #print("LENDF2:",len(df2)) + + df2 = yf.Ticker(symbol) + # df2 = df2.history(start=date-timedelta(days=365),end=date,interval="1d") + df2 = df2.history(start=date-timedelta(days=365*3),end=date,interval="1d") + df2 = df2.reset_index(level=0) + df2 = df2.drop(columns=['Dividends'], axis=1) + df2 = df2.drop(columns=['Stock Splits'], axis=1) + df2 = df2.drop(df2.head(7).index) + # df2 = df2.drop(df2.tail(test_size).index) + + return df2 + +# In[38]: + +def preproc(df): + minmax = MinMaxScaler().fit(df.iloc[:,1:9].astype('float32')) # Close, Volume, and all + df_log = minmax.transform(df.iloc[:, 1:9].astype('float32')) # Close, Volume, and all + df_log = pd.DataFrame(df_log) + df_log.head() + return df_log,minmax + + +# In[39]: + + +class Model: + def __init__( + self, + learning_rate, + num_layers, + size, + size_layer, + output_size, + forget_bias = 0.1, + ): + def lstm_cell(size_layer): + #print("ASDasdasdasd",len(tf.compat.v1.nn.rnn_cell.LSTMCell(size_layer, state_is_tuple = False)) + return tf.compat.v1.nn.rnn_cell.LSTMCell(size_layer, state_is_tuple = False) + + rnn_cells = tf.compat.v1.nn.rnn_cell.MultiRNNCell( + [lstm_cell(size_layer) for _ in range(num_layers)], + state_is_tuple = False, + ) + self.X = tf.compat.v1.placeholder(tf.float32, (None, None, size)) + self.Y = tf.compat.v1.placeholder(tf.float32, (None, output_size)) +# self.X = tf.keras.Input((None, size),dtype=tf.float32) +# self.Y = tf.keras.Input((output_size),dtype=tf.float32) + drop = tf.compat.v1.nn.rnn_cell.DropoutWrapper( + rnn_cells, output_keep_prob = forget_bias + ) +# print("XXXX:",self.X) +# print("XXXX:",self.X.shape) +# print("XXXX:",self.Y) +# print("XXXX:",self.Y.shape) + #print("LOOOASDSDASD") + self.hidden_layer = tf.compat.v1.placeholder( + tf.float32, (None, num_layers * 2 * size_layer) + ) + self.outputs, self.last_state = tf.compat.v1.nn.dynamic_rnn( + drop, self.X, initial_state = self.hidden_layer, dtype = tf.float32 + ) + #rint("INIDIA",self.outputs) + self.logits = tf.compat.v1.layers.dense(self.outputs[-1], output_size) + self.cost = tf.reduce_mean(tf.square(self.Y - self.logits)) + self.optimizer = tf.compat.v1.train.AdamOptimizer(learning_rate).minimize( + self.cost + ) + #print("cost:",self.cost) + #print("cost:",self.optimizer) + +def calculate_accuracy(real, predict): + real = np.array(real) + 1 + predict = np.array(predict) + 1 + percentage = 1 - np.sqrt(np.mean(np.square((real - predict) / real))) + return percentage * 100 + +def anchor(signal, weight): + buffer = [] + last = signal[0] + for i in signal: + smoothed_val = last * weight + (1 - weight) * i + buffer.append(smoothed_val) + last = smoothed_val + return buffer + + +# In[40]: +def main_train(df_beta, df_train, df, minmax): + + modelnn = Model( + learning_rate, num_layers, df_beta.shape[1], size_layer, df_beta.shape[1], dropout_rate + ) + sess = tf.compat.v1.Session() + sess = tf.compat.v1.Session(config=tf.compat.v1.ConfigProto(intra_op_parallelism_threads=64,inter_op_parallelism_threads=64)) + sess.run(tf.compat.v1.global_variables_initializer()) + pbar = tqdm(range(500), desc = 'Main train loop') + for i in pbar: + init_value = np.zeros((1, num_layers * 2 * size_layer)) + total_loss, total_acc = [], [] + for k in range(0, df_train.shape[0] - 1, timestamp): + index = min(k + timestamp, df_train.shape[0] - 1) + batch_x = np.expand_dims( + df_train.iloc[k : index, :].values, axis = 0 + ) + + batch_y = df_train.iloc[k + 1 : index + 1, :].values + #print("BATCH_X:",batch_x) + #print("BATCH_Y:",batch_y) + logits, last_state,__,loss = sess.run( + [modelnn.logits, modelnn.last_state,modelnn.optimizer, modelnn.cost], + feed_dict = { + modelnn.X: batch_x, + modelnn.Y: batch_y, + modelnn.hidden_layer: init_value, + }, + ) + init_value = last_state + total_loss.append(loss) + total_acc.append(calculate_accuracy(batch_y[:, 0], logits[:, 0])) + # json_loss.to_json("./loss.json") + graph_loss.append(np.mean(total_loss)) + + np.save(loss_file, np.array(graph_loss)) + pbar.set_postfix(cost = np.mean(total_loss), min_acc = np.min(total_acc), mean_acc=np.mean(total_acc)) + + +def forecast(df_beta,df_train,df,minmax): +# print("DF_BETA:",df_beta) +# print("DF_TRAIN:",df_train) + # tf.compat.v1.variable_scope("AAA", reuse=True) + tf.compat.v1.reset_default_graph() + modelnn = Model( + learning_rate, num_layers, df_beta.shape[1], size_layer, df_beta.shape[1], dropout_rate + ) +# print("MODELX: ",modelnn.X) +# print("MODELY: ",modelnn.Y) +# print("MODELLayer: ",modelnn.hidden_layer) + sess = tf.compat.v1.Session() + sess = tf.compat.v1.Session(config=tf.compat.v1.ConfigProto(intra_op_parallelism_threads=64,inter_op_parallelism_threads=64)) + sess.run(tf.compat.v1.global_variables_initializer()) + date_ori = pd.to_datetime(df.iloc[:, 0]).tolist() +# print("INI___!:",df_train.shape[0] - 1, timestamp) +# print(df_train.shape[0]) + pbar = tqdm(range(epoch), desc = 'train loop') + for i in pbar: + # init_value = np.zeros((1, num_layers * 2 * size_layer)) + total_loss, total_acc = [], [] + for k in range(0, df_train.shape[0] - 1, timestamp): + init_value = np.zeros((1, num_layers * 2 * size_layer)) + index = min(k + timestamp, df_train.shape[0] - 1) + batch_x = np.expand_dims( + df_train.iloc[k : index, :].values, axis = 0 + ) + + batch_y = df_train.iloc[k + 1 : index + 1, :].values + #print("BATCH_X:",batch_x) + #print("BATCH_Y:",batch_y) + logits, last_state,__,loss = sess.run( + [modelnn.logits, modelnn.last_state,modelnn.optimizer, modelnn.cost], + feed_dict = { + modelnn.X: batch_x, + modelnn.Y: batch_y, + modelnn.hidden_layer: init_value, + }, + ) + #print("BATCHX:",batch_x) +# print("MODELX: ",modelnn.X) +# print("MODELY: ",modelnn.Y) +# print("MODELLayer: ",modelnn.hidden_layer) + #print("OUTSSS:",len(outs)) + #print("opt:",opt) + #print("outs1",batch_x[0]) + #print("outs2",outs[1]) + #print("outs3",outs[2]) + #print("outs4",outs[3]) + #input() + init_value = last_state + total_loss.append(loss) + #print("LOGITS:",logits[:, 0]) + total_acc.append(calculate_accuracy(batch_y[:, 0], logits[:, 0])) + # json_loss.to_json("./loss.json") + graph_loss.append(np.mean(total_loss)) + + np.save(loss_file, np.array(graph_loss)) + pbar.set_postfix(cost = np.mean(total_loss), min_acc = np.min(total_acc), mean_acc=np.mean(total_acc)) + future_day = test_size + + output_predict = np.zeros((df_train.shape[0] + future_day, df_train.shape[1])) + output_predict[0] = df_train.iloc[0] + upper_b = (df_train.shape[0] // timestamp) * timestamp + init_value = np.zeros((1, num_layers * 2 * size_layer)) + + for k in range(0, (df_train.shape[0] // timestamp) * timestamp, timestamp): + out_logits, last_state = sess.run( + [modelnn.logits, modelnn.last_state], + feed_dict = { + modelnn.X: np.expand_dims( + df_train.iloc[k : k + timestamp], axis = 0 + ), + modelnn.hidden_layer: init_value, + }, + ) + init_value = last_state + output_predict[k + 1 : k + timestamp + 1] = out_logits + + if upper_b != df_train.shape[0]: + out_logits, last_state = sess.run( + [modelnn.logits, modelnn.last_state], + feed_dict = { + modelnn.X: np.expand_dims(df_train.iloc[upper_b:], axis = 0), + modelnn.hidden_layer: init_value, + }, + ) + output_predict[upper_b + 1 : df_train.shape[0] + 1] = out_logits + future_day -= 1 + date_ori.append(date_ori[-1] + timedelta(days = 1)) + + init_value = last_state + + for i in range(future_day): + o = output_predict[-future_day - timestamp + i:-future_day + i] + out_logits, last_state = sess.run( + [modelnn.logits, modelnn.last_state], + feed_dict = { + modelnn.X: np.expand_dims(o, axis = 0), + modelnn.hidden_layer: init_value, + }, + ) + init_value = last_state + output_predict[-future_day + i] = out_logits[-1] + date_ori.append(date_ori[-1] + timedelta(days = 1)) + + output_predict = minmax.inverse_transform(output_predict) + deep_future = anchor(output_predict[:, 0], 0.3) + sess.close() + sess.__del__() + return deep_future + + +# In[41]: + + +def newaccuration(accepted_results,truetrend): + hasilutama=0 + indexbagus=0 + truest=0 + predictest=0 + for i,x in enumerate(accepted_results): + a=x[-(test_size+2):] + + #a=x[:((test_size+2)/2)] + print("a",a) + b=truetrend[-(test_size+1):] + #print("b",b) + hasil=0 + true=[] + predict=[] + for xy in range(1,len((a))): + if a[xy] hasilutama: + hasilutama=hasil + indexbagus=i + truest=true + predictest=predict + salah=[] + for xz in range(len(truest)): + if truest[xz]!=predictest[xz]: + salah.append(xz) + #if xz!=0: + #salah.append(xz-1) + #print("INI:",b) + print("TRUEST",truest) + print("predictest",predictest) + return hasilutama,indexbagus,salah + +# In[42]: + + +def betaforecast(simulationsize,dfx,dftrain,df,df2,minmax): + results = [] + for i in range(simulationsize): + forecast_res = forecast(df,dftrain,dfx,minmax) + results.append(forecast_res) + accepted_results = [] + + while not (np.array(results[0][-test_size:]) < np.min(dfx['Close'])).sum() == 0 and (np.array(results[0][-test_size:]) > np.max(dfx['Close']) * 2).sum() == 0: + print("++++++++++++++++++++++++") + print("Forecast Recalled...") + results[0]=forecast(df,dftrain,dfx,minmax) + return results[0] + + +# In[43]: + + +def interval(p1,p2): + return abs((p1) - (p2)) + + +# In[44]: + + +def checkaccuracy2(true): + avg=[] + for x in range(len(true)-7): + avg.append(interval(true[x],true[x+1])) + average=sum(avg) / len(avg) + return average + + + +# In[45]: + + +def checkaccuracy(predict,true,filterx, test_size): + print("True Length: ",len(true)) + print("Predict Length: ",len(predict)) + +# avg=[] + +# for x in range(len(true)-5): +# avg.append(interval(true[x],predict[x])) +# average=sum(avg) / len(avg) +# print("AVG1:",average) +# print("AVG2:",threshold) + + temp_predict=predict[-test_size:] + temp_true=true[-test_size:] + +# avg2=interval(max(predict),min(predict)) + count=0 + print("------------------------------------") + for x in range(test_size): + # acc_var1 = temp_true[x]-(1/filterx*temp_true[x]) + acc_var1 = temp_true[x]-(filterx/10) + acc_var2 = temp_predict[x] + # acc_var3 = temp_true[x]+(1/filterx*temp_true[x]) + acc_var3 = temp_true[x]+(filterx/10) + acc_condition = acc_var1 <= acc_var2 <= acc_var3 + # print("Var 1 : ",acc_var1) + # print("Var 2 : ",acc_var2) + # print("Var 3 : ",acc_var3) + # print("Day "+str(x+1)+" "+str(int(acc_var1))+" "+str(int(acc_var2))+" "+str(int(acc_var3))+" : ",acc_condition) + print("Day "+str(x+1)+", Price : "+str(int(temp_true[x]))+" ,Gap = "+str(int(abs(temp_predict[x]-temp_true[x])))+" : ",acc_condition) + if (acc_condition): + count=count+1 + print("------------------------------------") + if count>7: + print("Result True") + return True + else: + print("Result False") + return False + +# if average>threshold: +# return False +# else: +# return True + + +# In[46]: + + +def findthreshold(simulationsize,dfx,dftrain,df,df2,minmax): + results=[] + for i in range(simulationsize): + results.append(forecast(df,dftrain,dfx,minmax)) + accepted_results = [] + + for r in results: + + if (np.array(r[-test_size:]) < np.min(dfx['Close'])).sum() == 0 and (np.array(r[-test_size:]) > np.max(dfx['Close']) * 2).sum() == 0: + accepted_results.append(r) + + finalavg=999999 + for o in accepted_results: + avg=[] + for x in range(len(o)-5): + avg.append(interval(o[x],df2[x])) + average=sum(avg) / len(avg) + if average<=finalavg: + finalavg=average + + return finalavg + +def temp_data(date, xi, resultfinal, df2, date_col,x): + print("Called . . . ") + if os.path.isdir("TempData/") == False: + os.mkdir("TempData/") + if os.path.isdir("TempData/%s"%x) == False: + os.mkdir("TempData/%s"%x) + if os.path.isdir("TempData/%s/"%x+str(date)) == False: + os.mkdir("TempData/%s/"%x+str(date)) + with open("TempData/%s/"%x+str(date)+"/"+x+str(xi)+".vezpal2","w+") as oop: + main=[] + main.append(resultfinal) #prediction + main.append(list(df2['Close'])) + main.append(date_col) +# main.append(3) +# main.append([0]) + json.dump(main,oop) + +def automaton(simulationsize,date): + + # symbols=["AAPL"] + # symbols = sys.argv[1:] + symbols = [] + symbols.append(sys.argv[1]) + times=[] + for x in symbols: + temp_time=[] + temp_time.append(x) + counter=0 + validity=0 + df,date_col=loader(x,test_size,date) + #print(type(df)) + dfx=df + #print("ASDSAD") + df2=trueloader(x,test_size,date) + df,minmax=preproc(df) + dftrain=df + wrong=[1,2,3,4,5] + #avg=checkaccuracy2(list(df2["Close"])) + #start=time.time() + #avg=findthreshold(50,dfx,dftrain,df,list(df2["Close"]),minmax) + #temp_time.append(time.time()-start) + start=time.time() + filterx = int(prediction_gap) + able=False + print("============== || Initial Train || =============") + main_train(df,dftrain,dfx,minmax) + for xi in range(5): + decision=False + while (decision==False): + print() + print("====== [ Foreacasting Attempt : "+str(counter+1)+" ] ===========") + print("====== [ Progress : "+str(xi)+"/5 ] ") + resultfinal=betaforecast(simulationsize,dfx,dftrain,df,df2,minmax) + #validity=valid + decision=checkaccuracy(resultfinal,list(df2["Close"]),filterx, test_size) + #wrong=invalid + if decision==True: + able=True + print("ABLE") + print(str(filterx)) + if counter > 100 and decision != True: + counter = 0 + filterx=filterx+1 + print("Filter X reduced to : "+str(filterx)) + print("Decision Status : ", decision) + print("**************************************") +# avg=avg+(1/3*avg) + if filterx>1000: + print("====== [ GG, we gave up] =====") + continue + counter=counter+1 + temp_data(date, xi, resultfinal, df2, date_col, x) + print("[ Loop : "+x+" done ] =========================") + print() + + if os.path.isdir("Samples/") == False: + os.mkdir("Samples/") + if os.path.isdir("Samples/%s"%x) == False: + os.mkdir("Samples/%s"%x) + if os.path.isdir("Samples/%s/"%x+str(date)) == False: + os.mkdir("Samples/%s/"%x+str(date)) + with open("Samples/%s/"%x+str(date)+"/"+x+str(xi)+".vezpal2","w+") as oop: + main=[] + main.append(resultfinal) #prediction + main.append(list(df2['Close'])) + main.append(date_col) +# main.append(3) +# main.append([0]) + json.dump(main,oop) + print("Time for %s :"%x,time.time()-start) + temp_time.append(time.time()-start) + times.append(temp_time) + return times + + + + +current_date=date.today() +if os.path.isdir("Loss/") == False: + os.mkdir("Loss/") +if os.path.isdir("Loss/"+str(current_date)) == False: + os.mkdir("Loss/"+str(current_date)) + +loss_file = time.strftime("%Y%m%d-%H%M%S") +loss_file = "Loss/"+str(date.today())+"/"+loss_file +global_start = time.time() +profile = cProfile.Profile() +main_func = "automaton(simulation_size,current_date)" + +ps = pstats.Stats(profile.run(main_func)) +print("Overall time consumption ", str(time.time()-global_start)) +ps.dump_stats("./Cprofile_model_01.ps") + diff --git a/main_model_django.py b/main_model_django.py new file mode 100644 index 0000000..028cbe4 --- /dev/null +++ b/main_model_django.py @@ -0,0 +1,616 @@ +import os +import sys +os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' +import tensorflow as tf +import numpy as np +import matplotlib.animation as animation +import matplotlib.pyplot as plt +import seaborn as sns +import pandas as pd +from sklearn.preprocessing import MinMaxScaler +from datetime import datetime +from datetime import timedelta +from tqdm import tqdm +sns.set() +import json +tf.compat.v1.random.set_random_seed(1234) +from matplotlib import style +# import matplotlib.backends.backend_qt5agg +# %matplotlib qt +style.use('ggplot') +import math +import yfinance as yf +import time +from datetime import date, timedelta +os.environ["CUDA_VISIBLE_DEVICES"] = "-1" +tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR) +import cProfile +import pstats + +# In[34]: + +tf.compat.v1.disable_eager_execution() +# INITIAL VARS +test_size = 14 +simulation_size = 1 + +# MODEL VARS +num_layers = 2 +size_layer = 128 +timestamp = 7 +epoch = 20 +dropout_rate = 0.8 +prediction_gap = 5000 +future_day = test_size +learning_rate = 0.01 +graph_loss = [] + +# In[35]: +# Necessary Dirs +# def date_manage(date1,date2=None): +# if date2 is None: +# date2=date1+timedelta(days=365) +# date_col=[] +# for n in range(int ((date2 - date1).days)+1): +# date_col.append(date1 + timedelta(n)) +# weekdays = [5,6] +# date_result=[] +# for dt in date_col: +# if dt.weekday() not in weekdays: +# dt.strftime("%Y-%m-%d") +# return date_result + + +# In[36]: +def loss_animate(ax, i): + json_loss = pd.DataFrame(total_loss) + ax.plot(i) + return ax + + +def loader(symbol,test_size,date): + # dateparse = lambda dates : pd.datetime.strptime(dates,'%Y-%m') + # df = pd.read_csv('../dataset/IBMCUT.csv',parse_dates=['Date'], index_col = 'Date', date_parser=dateparse) + + df=yf.Ticker(symbol) + # df=df.history(period="1y",interval="1d") + # df=df.history(start=date-timedelta(days=365),end=date,interval="1d") + df=df.history(start=date-timedelta(days=365*3),end=date,interval="1d") + df=df.reset_index(level=0) + + # df=df.drop(columns=['Dividends'], axis=1) + df=df.drop(columns=['Stock Splits'], axis=1) + + df['Up'] = df['High'].ewm(span=6,adjust=False).mean() + 2* df['High'].rolling(window=6).std() + df['Down']= df['Low'].ewm(span=8,adjust=False).mean() - 2* df['Low'].rolling(window=8).std() + + df=df.dropna() + df=df.drop(df.tail(7).index) + + date_ori = pd.to_datetime(df.iloc[:, 0]).tolist() +# for i in range(test_size): +# #date_ori.append(date_ori[-1] + timedelta(days = 1)) +# add=1 +# while ((date_ori[-1]) + timedelta(days = add)).weekday() in [5,6]: +# add=add+1 +# date_ori.append(date_ori[-1] + timedelta(days = add)) + date_ori = pd.Series(date_ori).dt.strftime(date_format = '%Y-%m-%d').tolist() + print(len(df),len(date_ori)) + return df,date_ori + +def trueloader(symbol,test_size,date): + #df2 = pd.read_csv(symbol) + #print("LENDF2:",len(df2)) + + df2 = yf.Ticker(symbol) + # df2 = df2.history(start=date-timedelta(days=365),end=date,interval="1d") + df2 = df2.history(start=date-timedelta(days=365*3),end=date,interval="1d") + df2 = df2.reset_index(level=0) + df2 = df2.drop(columns=['Dividends'], axis=1) + df2 = df2.drop(columns=['Stock Splits'], axis=1) + df2 = df2.drop(df2.head(7).index) + # df2 = df2.drop(df2.tail(test_size).index) + + return df2 + +# In[38]: + +def preproc(df): + minmax = MinMaxScaler().fit(df.iloc[:,1:9].astype('float32')) # Close, Volume, and all + df_log = minmax.transform(df.iloc[:, 1:9].astype('float32')) # Close, Volume, and all + df_log = pd.DataFrame(df_log) + df_log.head() + return df_log,minmax + + +# In[39]: + + +class Model: + def __init__( + self, + learning_rate, + num_layers, + size, + size_layer, + output_size, + forget_bias = 0.1, + ): + def lstm_cell(size_layer): + #print("ASDasdasdasd",len(tf.compat.v1.nn.rnn_cell.LSTMCell(size_layer, state_is_tuple = False)) + return tf.compat.v1.nn.rnn_cell.LSTMCell(size_layer, state_is_tuple = False) + + rnn_cells = tf.compat.v1.nn.rnn_cell.MultiRNNCell( + [lstm_cell(size_layer) for _ in range(num_layers)], + state_is_tuple = False, + ) + self.X = tf.compat.v1.placeholder(tf.float32, (None, None, size)) + self.Y = tf.compat.v1.placeholder(tf.float32, (None, output_size)) +# self.X = tf.keras.Input((None, size),dtype=tf.float32) +# self.Y = tf.keras.Input((output_size),dtype=tf.float32) + drop = tf.compat.v1.nn.rnn_cell.DropoutWrapper( + rnn_cells, output_keep_prob = forget_bias + ) +# print("XXXX:",self.X) +# print("XXXX:",self.X.shape) +# print("XXXX:",self.Y) +# print("XXXX:",self.Y.shape) + #print("LOOOASDSDASD") + self.hidden_layer = tf.compat.v1.placeholder( + tf.float32, (None, num_layers * 2 * size_layer) + ) + self.outputs, self.last_state = tf.compat.v1.nn.dynamic_rnn( + drop, self.X, initial_state = self.hidden_layer, dtype = tf.float32 + ) + #rint("INIDIA",self.outputs) + self.logits = tf.compat.v1.layers.dense(self.outputs[-1], output_size) + self.cost = tf.reduce_mean(tf.square(self.Y - self.logits)) + self.optimizer = tf.compat.v1.train.AdamOptimizer(learning_rate).minimize( + self.cost + ) + #print("cost:",self.cost) + #print("cost:",self.optimizer) + +def calculate_accuracy(real, predict): + real = np.array(real) + 1 + predict = np.array(predict) + 1 + percentage = 1 - np.sqrt(np.mean(np.square((real - predict) / real))) + return percentage * 100 + +def anchor(signal, weight): + buffer = [] + last = signal[0] + for i in signal: + smoothed_val = last * weight + (1 - weight) * i + buffer.append(smoothed_val) + last = smoothed_val + return buffer + + +# In[40]: +def main_train(df_beta, df_train, df, minmax): + + modelnn = Model( + learning_rate, num_layers, df_beta.shape[1], size_layer, df_beta.shape[1], dropout_rate + ) + sess = tf.compat.v1.Session() + sess = tf.compat.v1.Session(config=tf.compat.v1.ConfigProto(intra_op_parallelism_threads=64,inter_op_parallelism_threads=64)) + sess.run(tf.compat.v1.global_variables_initializer()) + pbar = tqdm(range(10), desc = 'Main train loop') + for i in pbar: + init_value = np.zeros((1, num_layers * 2 * size_layer)) + total_loss, total_acc = [], [] + for k in range(0, df_train.shape[0] - 1, timestamp): + index = min(k + timestamp, df_train.shape[0] - 1) + batch_x = np.expand_dims( + df_train.iloc[k : index, :].values, axis = 0 + ) + + batch_y = df_train.iloc[k + 1 : index + 1, :].values + #print("BATCH_X:",batch_x) + #print("BATCH_Y:",batch_y) + logits, last_state,__,loss = sess.run( + [modelnn.logits, modelnn.last_state,modelnn.optimizer, modelnn.cost], + feed_dict = { + modelnn.X: batch_x, + modelnn.Y: batch_y, + modelnn.hidden_layer: init_value, + }, + ) + init_value = last_state + total_loss.append(loss) + total_acc.append(calculate_accuracy(batch_y[:, 0], logits[:, 0])) + # json_loss.to_json("./loss.json") + graph_loss.append(np.mean(total_loss)) + + np.save(loss_file, np.array(graph_loss)) + pbar.set_postfix(cost = np.mean(total_loss), min_acc = np.min(total_acc), mean_acc=np.mean(total_acc)) + tf.compat.v1.reset_default_graph() + + +def forecast(df_beta,df_train,df,minmax): +# print("DF_BETA:",df_beta) +# print("DF_TRAIN:",df_train) + # tf.compat.v1.variable_scope("AAA", reuse=True) + modelnn = Model( + learning_rate, num_layers, df_beta.shape[1], size_layer, df_beta.shape[1], dropout_rate + ) +# print("MODELX: ",modelnn.X) +# print("MODELY: ",modelnn.Y) +# print("MODELLayer: ",modelnn.hidden_layer) + sess = tf.compat.v1.Session() + sess = tf.compat.v1.Session(config=tf.compat.v1.ConfigProto(intra_op_parallelism_threads=64,inter_op_parallelism_threads=64)) + sess.run(tf.compat.v1.global_variables_initializer()) + date_ori = pd.to_datetime(df.iloc[:, 0]).tolist() +# print("INI___!:",df_train.shape[0] - 1, timestamp) +# print(df_train.shape[0]) + pbar = tqdm(range(epoch), desc = 'train loop') + for i in pbar: + # init_value = np.zeros((1, num_layers * 2 * size_layer)) + total_loss, total_acc = [], [] + for k in range(0, df_train.shape[0] - 1, timestamp): + init_value = np.zeros((1, num_layers * 2 * size_layer)) + index = min(k + timestamp, df_train.shape[0] - 1) + batch_x = np.expand_dims( + df_train.iloc[k : index, :].values, axis = 0 + ) + + batch_y = df_train.iloc[k + 1 : index + 1, :].values + #print("BATCH_X:",batch_x) + #print("BATCH_Y:",batch_y) + logits, last_state,__,loss = sess.run( + [modelnn.logits, modelnn.last_state,modelnn.optimizer, modelnn.cost], + feed_dict = { + modelnn.X: batch_x, + modelnn.Y: batch_y, + modelnn.hidden_layer: init_value, + }, + ) + #print("BATCHX:",batch_x) +# print("MODELX: ",modelnn.X) +# print("MODELY: ",modelnn.Y) +# print("MODELLayer: ",modelnn.hidden_layer) + #print("OUTSSS:",len(outs)) + #print("opt:",opt) + #print("outs1",batch_x[0]) + #print("outs2",outs[1]) + #print("outs3",outs[2]) + #print("outs4",outs[3]) + #input() + init_value = last_state + total_loss.append(loss) + #print("LOGITS:",logits[:, 0]) + total_acc.append(calculate_accuracy(batch_y[:, 0], logits[:, 0])) + # json_loss.to_json("./loss.json") + graph_loss.append(np.mean(total_loss)) + + np.save(loss_file, np.array(graph_loss)) + pbar.set_postfix(cost = np.mean(total_loss), min_acc = np.min(total_acc), mean_acc=np.mean(total_acc)) + future_day = test_size + + output_predict = np.zeros((df_train.shape[0] + future_day, df_train.shape[1])) + output_predict[0] = df_train.iloc[0] + upper_b = (df_train.shape[0] // timestamp) * timestamp + init_value = np.zeros((1, num_layers * 2 * size_layer)) + + for k in range(0, (df_train.shape[0] // timestamp) * timestamp, timestamp): + out_logits, last_state = sess.run( + [modelnn.logits, modelnn.last_state], + feed_dict = { + modelnn.X: np.expand_dims( + df_train.iloc[k : k + timestamp], axis = 0 + ), + modelnn.hidden_layer: init_value, + }, + ) + init_value = last_state + output_predict[k + 1 : k + timestamp + 1] = out_logits + + if upper_b != df_train.shape[0]: + out_logits, last_state = sess.run( + [modelnn.logits, modelnn.last_state], + feed_dict = { + modelnn.X: np.expand_dims(df_train.iloc[upper_b:], axis = 0), + modelnn.hidden_layer: init_value, + }, + ) + output_predict[upper_b + 1 : df_train.shape[0] + 1] = out_logits + future_day -= 1 + date_ori.append(date_ori[-1] + timedelta(days = 1)) + + init_value = last_state + + for i in range(future_day): + o = output_predict[-future_day - timestamp + i:-future_day + i] + out_logits, last_state = sess.run( + [modelnn.logits, modelnn.last_state], + feed_dict = { + modelnn.X: np.expand_dims(o, axis = 0), + modelnn.hidden_layer: init_value, + }, + ) + init_value = last_state + output_predict[-future_day + i] = out_logits[-1] + date_ori.append(date_ori[-1] + timedelta(days = 1)) + + output_predict = minmax.inverse_transform(output_predict) + deep_future = anchor(output_predict[:, 0], 0.3) + sess.close() + sess.__del__() + tf.compat.v1.reset_default_graph() + return deep_future + + +# In[41]: + + +def newaccuration(accepted_results,truetrend): + hasilutama=0 + indexbagus=0 + truest=0 + predictest=0 + for i,x in enumerate(accepted_results): + a=x[-(test_size+2):] + + #a=x[:((test_size+2)/2)] + print("a",a) + b=truetrend[-(test_size+1):] + #print("b",b) + hasil=0 + true=[] + predict=[] + for xy in range(1,len((a))): + if a[xy] hasilutama: + hasilutama=hasil + indexbagus=i + truest=true + predictest=predict + salah=[] + for xz in range(len(truest)): + if truest[xz]!=predictest[xz]: + salah.append(xz) + #if xz!=0: + #salah.append(xz-1) + #print("INI:",b) + print("TRUEST",truest) + print("predictest",predictest) + return hasilutama,indexbagus,salah + +# In[42]: + + +def betaforecast(simulationsize,dfx,dftrain,df,df2,minmax): + results = [] + for i in range(simulationsize): + forecast_res = forecast(df,dftrain,dfx,minmax) + results.append(forecast_res) + accepted_results = [] + + while not (np.array(results[0][-test_size:]) < np.min(dfx['Close'])).sum() == 0 and (np.array(results[0][-test_size:]) > np.max(dfx['Close']) * 2).sum() == 0: + print("++++++++++++++++++++++++") + print("Forecast Recalled...") + results[0]=forecast(df,dftrain,dfx,minmax) + return results[0] + + +# In[43]: + + +def interval(p1,p2): + return abs((p1) - (p2)) + + +# In[44]: + + +def checkaccuracy2(true): + avg=[] + for x in range(len(true)-7): + avg.append(interval(true[x],true[x+1])) + average=sum(avg) / len(avg) + return average + + + +# In[45]: + + +def checkaccuracy(predict,true,filterx, test_size): + print("True Length: ",len(true)) + print("Predict Length: ",len(predict)) + +# avg=[] + +# for x in range(len(true)-5): +# avg.append(interval(true[x],predict[x])) +# average=sum(avg) / len(avg) +# print("AVG1:",average) +# print("AVG2:",threshold) + + temp_predict=predict[-test_size:] + temp_true=true[-test_size:] + +# avg2=interval(max(predict),min(predict)) + count=0 + print("------------------------------------") + for x in range(test_size): + # acc_var1 = temp_true[x]-(1/filterx*temp_true[x]) + acc_var1 = temp_true[x]-(filterx/10) + acc_var2 = temp_predict[x] + # acc_var3 = temp_true[x]+(1/filterx*temp_true[x]) + acc_var3 = temp_true[x]+(filterx/10) + acc_condition = acc_var1 <= acc_var2 <= acc_var3 + # print("Var 1 : ",acc_var1) + # print("Var 2 : ",acc_var2) + # print("Var 3 : ",acc_var3) + # print("Day "+str(x+1)+" "+str(int(acc_var1))+" "+str(int(acc_var2))+" "+str(int(acc_var3))+" : ",acc_condition) + print("Day "+str(x+1)+", Price : "+str(int(temp_true[x]))+" ,Gap = "+str(int(abs(temp_predict[x]-temp_true[x])))+" : ",acc_condition) + if (acc_condition): + count=count+1 + print("------------------------------------") + if count>7: + print("Result True") + return True + else: + print("Result False") + return False + +# if average>threshold: +# return False +# else: +# return True + + +# In[46]: + + +def findthreshold(simulationsize,dfx,dftrain,df,df2,minmax): + results=[] + for i in range(simulationsize): + results.append(forecast(df,dftrain,dfx,minmax)) + accepted_results = [] + + for r in results: + + if (np.array(r[-test_size:]) < np.min(dfx['Close'])).sum() == 0 and (np.array(r[-test_size:]) > np.max(dfx['Close']) * 2).sum() == 0: + accepted_results.append(r) + + finalavg=999999 + for o in accepted_results: + avg=[] + for x in range(len(o)-5): + avg.append(interval(o[x],df2[x])) + average=sum(avg) / len(avg) + if average<=finalavg: + finalavg=average + + return finalavg + +def temp_data(date, xi, resultfinal, df2, date_col,x): + print("Called . . . ") + if os.path.isdir("TempData/") == False: + os.mkdir("TempData/") + if os.path.isdir("TempData/%s"%x) == False: + os.mkdir("TempData/%s"%x) + if os.path.isdir("TempData/%s/"%x+str(date)) == False: + os.mkdir("TempData/%s/"%x+str(date)) + with open("TempData/%s/"%x+str(date)+"/"+x+str(xi)+".vezpal2","w+") as oop: + main=[] + main.append(resultfinal) #prediction + main.append(list(df2['Close'])) + main.append(date_col) +# main.append(3) +# main.append([0]) + json.dump(main,oop) + +def automaton(simulationsize,date, ticker): + + # symbols=["AAPL"] + # symbols = sys.argv[1:] + symbols = [] + symbols.append(ticker) + times=[] + for x in symbols: + temp_time=[] + temp_time.append(x) + counter=0 + validity=0 + df,date_col=loader(x,test_size,date) + #print(type(df)) + dfx=df + #print("ASDSAD") + df2=trueloader(x,test_size,date) + df,minmax=preproc(df) + dftrain=df + wrong=[1,2,3,4,5] + #avg=checkaccuracy2(list(df2["Close"])) + #start=time.time() + #avg=findthreshold(50,dfx,dftrain,df,list(df2["Close"]),minmax) + #temp_time.append(time.time()-start) + start=time.time() + filterx = int(prediction_gap) + able=False + print("============== || Initial Train || =============") + main_train(df,dftrain,dfx,minmax) + for xi in range(5): + decision=False + while (decision==False): + print() + print("====== [ Foreacasting Attempt : "+str(counter+1)+" ] ===========") + print("====== [ Progress : "+str(xi)+"/5 ] ") + resultfinal=betaforecast(simulationsize,dfx,dftrain,df,df2,minmax) + #validity=valid + decision=checkaccuracy(resultfinal,list(df2["Close"]),filterx, test_size) + # wrong=invalid + if decision==True: + able=True + print("ABLE") + print(str(filterx)) + if counter > 100 and decision != True: + counter = 0 + filterx=filterx+1 + print("Filter X reduced to : "+str(filterx)) + print("Decision Status : ", decision) + print("**************************************") + # avg=avg+(1/3*avg) + if filterx>1000: + print("====== [ GG, we gave up] =====") + continue + counter=counter+1 + temp_data(date, xi, resultfinal, df2, date_col, x) + print("[ Loop : "+x+" done ] =========================") + print() + + if os.path.isdir("Samples/") == False: + os.mkdir("Samples/") + if os.path.isdir("Samples/%s"%x) == False: + os.mkdir("Samples/%s"%x) + if os.path.isdir("Samples/%s/"%x+str(date)) == False: + os.mkdir("Samples/%s/"%x+str(date)) + with open("Samples/%s/"%x+str(date)+"/"+x+str(xi)+".vezpal2","w+") as oop: + main=[] + main.append(resultfinal) #prediction + main.append(list(df2['Close'])) + main.append(date_col) +# main.append(3) +# main.append([0]) + json.dump(main,oop) + print("Time for %s :"%x,time.time()-start) + temp_time.append(time.time()-start) + times.append(temp_time) + return times + +def django_call(ticker): + + # tf.compat.v1.reset_default_graph() + automaton(simulation_size, current_date, ticker) + + return "Done" + +current_date=date.today() + +if os.path.isdir("Loss/") == False: + os.mkdir("Loss/") +if os.path.isdir("Loss/"+str(current_date)) == False: + os.mkdir("Loss/"+str(current_date)) + +loss_file = time.strftime("%Y%m%d-%H%M%S") +loss_file = "Loss/"+str(date.today())+"/"+loss_file + +# ticker = input("AAA : ") +# django_call(ticker) diff --git a/multiplot.py b/multiplot.py new file mode 100644 index 0000000..96fdde7 --- /dev/null +++ b/multiplot.py @@ -0,0 +1,156 @@ +import matplotlib.pyplot as plt +import json +from pylab import * + +import os +from datetime import datetime, date, timedelta +from matplotlib.dates import date2num +import yfinance as yf +import sys +import glob +import numpy as np +# path='../Febrian/Bang Nino/Samples/NEE/2020-05-08/' +def return_plot(plotax,ticks,filters): + #my_args = sys.argv[1:] + #tick = my_args[0] + #filter=int(my_args[1]) + tick=ticks + filter=filters + visual=100 + print(tick) + # path='./Samples/'+tick+'/2020-12-07/' + path='./Samples/'+tick+'/' + all_dirs = glob.glob(path+"*") + print(path) + print(all_dirs) + latest_dir = max(all_dirs, key=os.path.getctime) + print("Latest Data = ", latest_dir) + latest_dir = latest_dir + "/" + listsymbol = os.listdir(latest_dir+'/') + print(listsymbol) + + with open(latest_dir+listsymbol[0],'r') as f: + print(latest_dir+listsymbol[0]) + hasil=json.load(f) + # print(hasil) + print(len(hasil[0])) + print(len(hasil[1])) + print(len(hasil[2])) + akhir=datetime.strptime(str(hasil[2][-1]),'%Y-%m-%d').date() + print("Current Log : ",akhir) + extend1=[akhir+timedelta(days=x) for x in range(1,50,1)] + extend=[] + + for x in extend1: + if x.weekday() not in [5,6]: + extend.append(x) + + extend=extend[:14] + extend=[str(x) for x in extend] + print("ASasdasdsa",extend) + # date=date2num(date) + date=hasil[2] + plotax.plot(hasil[1],color='grey',linewidth=3, linestyle='--', marker='x', alpha=0.8, label="Confirmation") + plotax.plot(hasil[2],hasil[1][0:-5],color='b',linewidth=2) + # plt.show() + + symbol=yf.Ticker(tick) + symbol=symbol.history(start=akhir,end=akhir+timedelta(days=30),interval='1d') + # print(symbol) + symbol=symbol.drop(symbol.index[0]) + symbol=symbol.drop(symbol.index[0]) + symbol=symbol['Close'][0:14].tolist() + # plt.plot(extend,symbol,color='g',linewidth=10) + avg=[] + for ex,x in enumerate(listsymbol): + with open(latest_dir+x) as f: + print("#################################################") + print(x) + hasil=json.load(f) + # a=hasil[0][-14:][:14] + a = hasil[0][-14:][:7] + b=hasil[1][-7:] + print("A & B Temp :") + print(a) + print(b) + count=0 + print("Predict - Real ") + for x in range(7): + print("%.2f" % (a[x]-b[x])) + if (b[x]-(filter/10) <= a[x] <= b[x]+(filter/10)): + count=count+1 + if count>5: + print("ACCEPTED . . .") + predict_val = hasil[0][-14:] + avg.append(predict_val) + # avg.append(hasil[0][-10][:14]) + # plt.plot(date+extend,hasil[0], label='Sample %s'%ex, alpha=0.3) + print() + else: + print("NOT ACCEPTED . . .") + print() + # print(avg) + print("#################################################") + print(avg) + print(len(avg)) + print("#################################################") + avg_total=[] + for x in range(len(extend)): + temp=[] + for a in range(len(avg)): + temp.append(avg[a][x]) + # avg_total.append(sum(temp)/len(temp)) + mean_pred = np.mean(temp) + print(mean_pred) + avg_total.append(mean_pred) + + # print(avg_total) + + atas=[x+(1/visual*x) for x in avg_total] + bawah=[x-(1/visual*x) for x in avg_total] + print([hasil[1][0:-5][-1]]+bawah) + print([hasil[1][0:-5][-1]]+atas) + print([date[-1]]+extend) + plotax.fill_between([date[-1]]+extend,[hasil[1][0:-5][-1]]+bawah,[hasil[1][0:-5][-1]]+atas,alpha=0.2,label='Prediction Band') + plotax.plot([date[-1]]+extend,[hasil[1][-5]]+avg_total,color='y',linewidth=1,label='Prediction', marker='x') + plotax.grid() + # plt.show() + + # print("Date Extended ",(date+extend)) + symbol = yf.Ticker(tick) + symbol = symbol.history(start=akhir,end=akhir+timedelta(days=20),interval='1d') + print(symbol) + symbol = symbol.drop(symbol.index[0]) + symbol = symbol.drop(symbol.index[0]) + symbol = symbol['Close'][0:14].tolist() + # symbol = symbol['Close'].tolist() + # print(symbol) + # plt.plot(extend[0:len(symbol)],symbol,color='g',label='Actual',linewidth=1) + # plt.plot(hasil[1],color='r', linestyle='--', label='Confirmation',linewidth=2) + # plt.plot(hasil[2]+extend[0:14],hasil[0],color='b',label='Train',linewidth=2, alpha=0.4) + # plt.plot(symbol, label="Symbol Real") + # plt.plot(hasil[0], label="Real Predictioh") + # print(symbol) + # print(symbol) + # detail = str(akhir)+"\n"+"Prediction :"+str(avg_total[-1:])+"\n"+"Real : " + # plt.text(0.05, 120, detail, color='black', bbox=dict(facecolor='none', edgecolor='black', boxstyle='round,pad=1')) + plotax.legend(loc='upper center', bbox_to_anchor=(0.5, -0.01), + fancybox=True, shadow=True, ncol=7) + plotax.title.set_text(ticks) + #plotax.title(tick+" Date: "+str(hasil[2][-1])+" to "+str(extend[-1])) + # plt.get_xaxis().set_ticks([]) + #plotax.xticks([]) + return plotax + + +#subplots_adjust(hspace=0.000) +#symbolss=os.listdir("Samples") +symbolss=["GOOGL","FB","MA","SHOP","NEE"] +#number_of_subplots=len(os.listdir("Samples")) +number_of_subplots=5 +for i,v in enumerate(range(number_of_subplots)): + v = v+1 + ax1 = subplot(number_of_subplots,1,v) + ax1 = return_plot(ax1,symbolss.pop(0),900) +plt.tight_layout() +plt.show() diff --git a/parser.py b/parser.py new file mode 100644 index 0000000..f3b47b7 --- /dev/null +++ b/parser.py @@ -0,0 +1,29 @@ +#!/bin/python3 + +import json +import numpy as np +import matplotlib.pyplot as plt + +file_json=open("./Febrian_sample/AAPL/2020-05-08/AAPL0.vezpal2") +data = json.loads(file_json.read()) + +x = [] +y = [] +fill_zero = [] +fill_zero = [0,0,0,0,0] +print(len(data)) +x = data[0] +y = data[1]# +fill_zero +z = data[2]# +(fill_zero*2) +# print(x) + +print(len(x)) +print(len(y)) +print(len(z)) +plt.xticks(rotation=90) +# plt.plot(z,y) +# plt.plot(z,x) +# plt.plot(z,y) +plt.plot(x) +plt.plot(y) +plt.show() diff --git a/plot_end.py b/plot_end.py new file mode 100644 index 0000000..17ce13a --- /dev/null +++ b/plot_end.py @@ -0,0 +1,178 @@ +import matplotlib.pyplot as plt +import json +import os +from datetime import datetime, date, timedelta +from matplotlib.dates import date2num +import yfinance as yf +import sys +import glob +import numpy as np +from collections import Counter +# path='../Febrian/Bang Nino/Samples/NEE/2020-05-08/' + +my_args = sys.argv[1:] +tick = my_args[0] +filter=int(my_args[1]) +visual=50 +print(tick) +# path='./Samples/'+tick+'/2020-12-07/' +path='./Samples/'+tick+'/' +all_dirs = glob.glob(path+"*") +print(path) +print(all_dirs) +latest_dir = max(all_dirs, key=os.path.getctime) +print("Latest Data = ", latest_dir) +latest_dir = latest_dir + "/" +listsymbol = os.listdir(latest_dir+'/') +print(listsymbol) + +with open(latest_dir+listsymbol[0],'r') as f: + print(latest_dir+listsymbol[0]) + hasil=json.load(f) + # print(hasil) + print(len(hasil[0])) + print(len(hasil[1])) + print(len(hasil[2])) + akhir=datetime.strptime(str(hasil[2][-1]),'%Y-%m-%d').date() + print("Current Log : ",akhir) + extend1=[akhir+timedelta(days=x) for x in range(1,50,1)] + extend=[] + + for x in extend1: + if x.weekday() not in [5,6]: + extend.append(x) + + extend=extend[:14] + extend=[str(x) for x in extend] + print("ASasdasdsa",extend) + # date=date2num(date) + date=hasil[2] + #plt.plot(([date[-1]]+extend)[-7:],hasil[1][-7:],color='grey',linewidth=3, linestyle='--', marker='x', alpha=0.8, label="Confirmation") + plt.plot(hasil[2][-10:],hasil[1][0:-7][-10:],color='b',linewidth=2) + # plt.show() + +symbol=yf.Ticker(tick) +symbol=symbol.history(start=akhir,end=akhir+timedelta(days=30),interval='1d') +# print(symbol) +symbol=symbol.drop(symbol.index[0]) +symbol=symbol.drop(symbol.index[0]) +symbol=symbol['Close'][0:14].tolist() +# plt.plot(extend,symbol,color='g',linewidth=10) +avg=[] +gap_avg = [] +for ex,x in enumerate(listsymbol): + gap_val = [] + with open(latest_dir+x) as f: + print("#################################################") + print(x) + hasil=json.load(f) + # a=hasil[0][-14:][:14] + a = hasil[0][-14:][:7] + b=hasil[1][-7:] + print("A & B Temp :") + print(a) + print(b) + count=0 + print("Predict - Real ") + for x in range(len(b)): + print("%.2f" % (a[x]-b[x])) + if (b[x]-(filter/10) <= a[x] <= b[x]+(filter/10)): + count=count+1 + if count>4: + print("ACCEPTED . . .") + for i in range(len(b)): + gap_val.append(abs(a[i]-b[i])) + print(len(gap_val)) + predict_val = hasil[0][-14:] + avg.append(predict_val) + # avg.append(hasil[0][-10][:14]) + plt.plot((date+extend)[-20:],hasil[0][-20:], label='Sample %s'%ex, alpha=0.3) + gap_avg.append(gap_val) + print() + else: + print("NOT ACCEPTED . . .") + print() +# print(avg) +print("#################################################") +print(avg) +print(len(avg)) +print(gap_avg) +print(len(gap_avg)) +print("#################################################") +avg_total=[] +good_index=[] +for x in range(len(extend)): + print("NOW ", x) + temp=[] + gap_temp = [] + pred_temp = [] + if x < 7: + for a in range(len(gap_avg)): + # print(gap_temp.size()) + gap_temp.append(gap_avg[a][x]) + print("Gap Temp") + print(gap_temp) + # for b in range(len(gap_avg)): + good_index.append(gap_temp.index(min(gap_temp))) + else: + print("LOL") + c = Counter(good_index) + c = c.most_common(1) + good_index.append(c[0][0]) + + print("Good Index") + print(len(good_index)) + print(good_index) + print() +for c in range(len(extend)): + print(c) + temp.append(avg[int(good_index[c])][c]) + print(temp) + # avg_total.append(sum(temp)/len(temp)) + # mean_pred = np.mean(temp) + # print(mean_pred) +avg_total = temp + +print(avg_total) +# print(avg_total) + +atas=[x+(1/visual*x) for x in avg_total] +bawah=[x-(1/visual*x) for x in avg_total] +print([hasil[1][0:-5][-1]]+bawah) +print([hasil[1][0:-5][-1]]+atas) +print([date[-1]]+extend) +plt.fill_between([date[-1]]+extend,[hasil[1][0:-5][-1]]+bawah,[hasil[1][0:-5][-1]]+atas,alpha=0.2,label='Prediction Band') +plt.plot([date[-1]]+extend,[hasil[1][-5]]+avg_total,color='y',linewidth=3,label='Prediction', marker='x') +plt.plot(([date[-1]]+extend)[-15:][:8],hasil[1][-8:],color='grey',linewidth=3, linestyle='--', marker='x', alpha=0.8, label="Confirmation") +plt.grid() +# plt.show() + +# print("Date Extended ",(date+extend)) +symbol = yf.Ticker(tick) +symbol = symbol.history(start=akhir,end=akhir+timedelta(days=20),interval='1d') +print(symbol) +symbol = symbol.drop(symbol.index[0]) +symbol = symbol.drop(symbol.index[0]) +symbol = symbol['Close'][0:14].tolist() +# symbol = symbol['Close'].tolist() +# print(symbol) +#plt.plot(extend[0:len(symbol)],symbol,color='g',label='Actual',linewidth=1) +# plt.plot(hasil[1],color='r', linestyle='--', label='Confirmation',linewidth=2) +# plt.plot(hasil[2]+extend[0:14],hasil[0],color='b',label='Train',linewidth=2, alpha=0.4) +# plt.plot(symbol, label="Symbol Real") +# plt.plot(hasil[0], label="Real Predictioh") +# print(symbol) +# print(symbol) +# detail = str(akhir)+"\n"+"Prediction :"+str(avg_total[-1:])+"\n"+"Real : " +# plt.text(0.05, 120, detail, color='black', bbox=dict(facecolor='none', edgecolor='black', boxstyle='round,pad=1')) +plt.legend(loc='upper center', bbox_to_anchor=(0.5, -0.01), + fancybox=True, shadow=True, ncol=7) +plt.title(tick+" Date: "+str(hasil[2][-1])+" to "+str(extend[-1])) +# plt.get_xaxis().set_ticks([]) +new=yf.Ticker(tick) +new=new.history(interval="1d",start=([date[-1]]+extend)[-15:][:8][-1],end=datetime.today()) +plt.plot(new.index.astype(str),new["Close"],color='red',linewidth=3, linestyle='--', marker='x', alpha=0.8, label="True Trend") +print(new) +print(extend[-len(new):]) +plt.xticks([]) +plt.show() diff --git a/plot_loss.py b/plot_loss.py new file mode 100644 index 0000000..f2d0f41 --- /dev/null +++ b/plot_loss.py @@ -0,0 +1,50 @@ +import matplotlib.pyplot as plt +import pandas as pd +import numpy as np +import matplotlib.animation as animation +from scipy.interpolate import make_interp_spline, BSpline +from datetime import date +import glob +import os + +data_date = date.today() +loss_dir = "Loss/"+str(data_date)+"/" +list_of_files = glob.glob(loss_dir+"*") +latest_file = max(list_of_files, key=os.path.getctime) +print("Current File : ", latest_file) + +fig = plt.figure() +ax = fig.add_subplot(1,1,1) +props = dict(boxstyle="square", facecolor='wheat', alpha=0.5) +data = np.load(latest_file) + +def init(): + disp_data =round(0.3*len(data)) + ax.set_ylim(0,np.max(data[-disp_data:])*1.5) + ax.set_xlim(0,len(data)) + +def animate(i): + ax.clear() + data = np.load(latest_file) + x = np.arange(len(data)) + smooth_data = np.linspace(x.min(), x.max(), round(len(data)/5)) + print(smooth_data) + disp_data =round(0.2*len(data)) + spl = make_interp_spline(x, data, k=3) + y_smooth = spl(smooth_data) + # y_smooth = spline(x, data, smooth_data) + # last_data = "loss = %.6f" % data[-1] + + ax.set_ylim(0,np.max(data[-disp_data:])*2) + ax.set_xlim(0,len(data)) + # ax.text(0.05, 0.9, last_data, transform=ax.transAxes, bbox=props) + # ax.set_yscale('log') + ax.title.set_text("Loss of "+str(latest_file)) + ax.plot(smooth_data, y_smooth, color='tomato', alpha=0.8) + # ax.plot(data, color='tomato', alpha=0.8) + +ani = animation.FuncAnimation(fig, animate, interval=20, init_func=init) +# ani.save(filename='loss_animation.mp4', fps=30, extra_args=['-vcodec', 'libx264'], dpi=300, bitrate=1800, metadata=dict(artist='Nino')) +plt.show() + + diff --git a/plotterpitch.py b/plotterpitch.py new file mode 100644 index 0000000..dfbe276 --- /dev/null +++ b/plotterpitch.py @@ -0,0 +1,139 @@ +import matplotlib.pyplot as plt +import json +import os +from datetime import datetime, date, timedelta +from matplotlib.dates import date2num +import yfinance as yf +import sys +import glob +import numpy as np +# path='../Febrian/Bang Nino/Samples/NEE/2020-05-08/' + +my_args = sys.argv[1:] +tick = my_args[0] +filter=int(my_args[1]) +visual=100 +print(tick) +# path='./Samples/'+tick+'/2020-12-07/' +path='./Samples_Cross/Samples/'+tick+'/' +all_dirs = glob.glob(path+"*") +print(path) +print(all_dirs) +latest_dir = max(all_dirs, key=os.path.getctime) +print("Latest Data = ", latest_dir) +latest_dir = latest_dir + "/" +listsymbol = os.listdir(latest_dir+'/') +print(listsymbol) + +with open(latest_dir+listsymbol[0],'r') as f: + print(latest_dir+listsymbol[0]) + hasil=json.load(f) + # print(hasil) + print(len(hasil[0])) + print(len(hasil[1])) + print(len(hasil[2])) + akhir=datetime.strptime(str(hasil[2][-1]),'%Y-%m-%d').date() + print("Current Log : ",akhir) + extend1=[akhir+timedelta(days=x) for x in range(1,50,1)] + extend=[] + + for x in extend1: + if x.weekday() not in [5,6]: + extend.append(x) + + extend=extend[:14] + extend=[str(x) for x in extend] + print("ASasdasdsa",extend) + # date=date2num(date) + date=hasil[2] + plt.plot(hasil[1],color='grey',linewidth=3, linestyle='--', marker='x', alpha=0.8, label="Confirmation") + plt.plot(hasil[2],hasil[1][0:-5],color='b',linewidth=2) + # plt.show() + +symbol=yf.Ticker(tick) +symbol=symbol.history(start=akhir,end=akhir+timedelta(days=30),interval='1d') +# print(symbol) +symbol=symbol.drop(symbol.index[0]) +symbol=symbol.drop(symbol.index[0]) +symbol=symbol['Close'][0:14].tolist() +# plt.plot(extend,symbol,color='g',linewidth=10) +avg=[] +for ex,x in enumerate(listsymbol): + with open(latest_dir+x) as f: + print("#################################################") + print(x) + hasil=json.load(f) + # a=hasil[0][-14:][:14] + a = hasil[0][-14:][:7] + b=hasil[1][-7:] + print("A & B Temp :") + print(a) + print(b) + count=0 + print("Predict - Real ") + for x in range(7): + print("%.2f" % (a[x]-b[x])) + if (b[x]-(filter/10) <= a[x] <= b[x]+(filter/10)): + count=count+1 + if count>5: + print("ACCEPTED . . .") + predict_val = hasil[0][-14:] + avg.append(predict_val) + # avg.append(hasil[0][-10][:14]) + # plt.plot(date+extend,hasil[0], label='Sample %s'%ex, alpha=0.3) + print() + else: + print("NOT ACCEPTED . . .") + print() +# print(avg) +print("#################################################") +print(avg) +print(len(avg)) +print("#################################################") +avg_total=[] +for x in range(len(extend)): + temp=[] + for a in range(len(avg)): + temp.append(avg[a][x]) + # avg_total.append(sum(temp)/len(temp)) + mean_pred = np.mean(temp) + print(mean_pred) + avg_total.append(mean_pred) + +# print(avg_total) + +atas=[x+(1/visual*x) for x in avg_total] +bawah=[x-(1/visual*x) for x in avg_total] +print([hasil[1][0:-5][-1]]+bawah) +print([hasil[1][0:-5][-1]]+atas) +print([date[-1]]+extend) +plt.fill_between([date[-1]]+extend,[hasil[1][0:-5][-1]]+bawah,[hasil[1][0:-5][-1]]+atas,alpha=0.2,label='Prediction Band') +plt.plot([date[-1]]+extend,[hasil[1][-5]]+avg_total,color='y',linewidth=1,label='Prediction', marker='x') +plt.grid() +# plt.show() + +# print("Date Extended ",(date+extend)) +symbol = yf.Ticker(tick) +symbol = symbol.history(start=akhir,end=akhir+timedelta(days=20),interval='1d') +print(symbol) +symbol = symbol.drop(symbol.index[0]) +symbol = symbol.drop(symbol.index[0]) +symbol = symbol['Close'][0:14].tolist() +# symbol = symbol['Close'].tolist() +# print(symbol) +# plt.plot(extend[0:len(symbol)],symbol,color='g',label='Actual',linewidth=1) +# plt.plot(hasil[1],color='r', linestyle='--', label='Confirmation',linewidth=2) +# plt.plot(hasil[2]+extend[0:14],hasil[0],color='b',label='Train',linewidth=2, alpha=0.4) +# plt.plot(symbol, label="Symbol Real") +# plt.plot(hasil[0], label="Real Predictioh") +# print(symbol) +# print(symbol) +# detail = str(akhir)+"\n"+"Prediction :"+str(avg_total[-1:])+"\n"+"Real : " +# plt.text(0.05, 120, detail, color='black', bbox=dict(facecolor='none', edgecolor='black', boxstyle='round,pad=1')) +plt.legend(loc='upper center', bbox_to_anchor=(0.5, -0.01), + fancybox=True, shadow=True, ncol=7) +plt.title(tick+" Date: "+str(hasil[2][-1])+" to "+str(extend[-1])) +# plt.get_xaxis().set_ticks([]) +plt.xticks([]) +plt.show() +# plt.savefig("./temp_pred.png") diff --git a/plotterpitch_django.py b/plotterpitch_django.py new file mode 100644 index 0000000..b29639c --- /dev/null +++ b/plotterpitch_django.py @@ -0,0 +1,187 @@ +import matplotlib.pyplot as plt +import json +import os +from datetime import datetime, date, timedelta +from matplotlib.dates import date2num +import yfinance as yf +import sys +import glob +import numpy as np +from collections import Counter +# path='../Febrian/Bang Nino/Samples/NEE/2020-05-08/' + +def django_pass(func_stock, func_param): + # my_args = sys.argv[1:] + my_args = func_stock + tick = my_args + # filter=int(my_args[1]) + filter = func_param + visual=50 + # print(tick) + # path='./Samples/'+tick+'/2020-12-07/' + path='/home/kraken/Stock/v04/Samples/'+tick+'/' + all_dirs = glob.glob(path+"*") + # print(path) + # print(all_dirs) + latest_dir = max(all_dirs, key=os.path.getmtime) + # print("Latest Data = ", latest_dir) + latest_dir = latest_dir + "/" + listsymbol = os.listdir(latest_dir+'/') + # print(listsymbol) + + with open(latest_dir+listsymbol[0],'r') as f: + # print(latest_dir+listsymbol[0]) + hasil=json.load(f) + # print(hasil) + # print(len(hasil[0])) + # print(len(hasil[1])) + # print(len(hasil[2])) + akhir=datetime.strptime(str(hasil[2][-1]),'%Y-%m-%d').date() + # print("Current Log : ",akhir) + extend1=[akhir+timedelta(days=x) for x in range(1,50,1)] + extend=[] + + for x in extend1: + if x.weekday() not in [5,6]: + extend.append(x) + + extend=extend[:14] + extend=[str(x) for x in extend] + # print("ASasdasdsa",extend) + # date=date2num(date) + date=hasil[2] + # plt.plot(hasil[1],color='grey',linewidth=3, linestyle='--', marker='x', alpha=0.8, label="Confirmation") + # plt.plot(hasil[2],hasil[1][0:-7],color='b',linewidth=2) + # plt.show() + + symbol=yf.Ticker(tick) + symbol=symbol.history(start=akhir,end=akhir+timedelta(days=30),interval='1d') + # print(symbol) + symbol=symbol.drop(symbol.index[0]) + symbol=symbol.drop(symbol.index[0]) + symbol=symbol['Close'][0:14].tolist() + # plt.plot(extend,symbol,color='g',linewidth=10) + avg=[] + gap_avg = [] + for ex,x in enumerate(listsymbol): + gap_val = [] + with open(latest_dir+x) as f: + # print("#################################################") + # print(x) + hasil=json.load(f) + # a=hasil[0][-14:][:14] + a = hasil[0][-14:][:7] + b=hasil[1][-7:] + # print("A & B Temp :") + # print(a) + # print(b) + count=0 + # print("Predict - Real ") + for x in range(len(b)): + # print("%.2f" % (a[x]-b[x])) + if (b[x]-(filter/10) <= a[x] <= b[x]+(filter/10)): + count=count+1 + if count>4: + # print("ACCEPTED . . .") + for i in range(len(b)): + gap_val.append(abs(a[i]-b[i])) + # print(len(gap_val)) + predict_val = hasil[0][-14:] + avg.append(predict_val) + # avg.append(hasil[0][-10][:14]) + # plt.plot(date+extend,hasil[0], label='Sample %s'%ex, alpha=0.3) + gap_avg.append(gap_val) + # print() + # else: + # print("NOT ACCEPTED . . .") + # print() + # print(avg) + # print("#################################################") + # print(avg) + # print(len(avg)) + # print(gap_avg) + # print(len(gap_avg)) + # print("#################################################") + # avg_total=[] + + good_index=[] + avg_total=[] + for x in range(len(extend)): + # print("NOW ", x) + temp=[] + gap_temp = [] + pred_temp = [] + if x < 7: + for a in range(len(gap_avg)): + # print(gap_temp.size()) + gap_temp.append(gap_avg[a][x]) + # print("Gap Temp") + # print(gap_temp) + # for b in range(len(gap_avg)): + good_index.append(gap_temp.index(min(gap_temp))) + else: + # print("LOL") + c = Counter(good_index) + c = c.most_common(1) + good_index.append(c[0][0]) + + # print("Good Index") + # print(len(good_index)) + # print(good_index) + # print() + + for c in range(len(extend)): + # print(c) + temp.append(avg[int(good_index[c])][c]) + # print(temp) + # avg_total.append(sum(temp)/len(temp)) + # mean_pred = np.mean(temp) + # print(mean_pred) + avg_total = temp + + # print(avg_total) + + atas=[x+(1/visual*x) for x in avg_total] + bawah=[x-(1/visual*x) for x in avg_total] + # print([hasil[1][0:-5][-1]]+bawah) + # print([hasil[1][0:-5][-1]]+atas) + # print([date[-1]]+extend) + + # plt.fill_between([date[-1]]+extend,[hasil[1][0:-5][-1]]+bawah,[hasil[1][0:-5][-1]]+atas,alpha=0.2,label='Prediction Band') + # plt.plot([date[-1]]+extend,[hasil[1][-5]]+avg_total,color='y',linewidth=3,label='Prediction', marker='x') + # plt.grid() + # plt.show() + # print("Date Extended ",(date+extend)) + symbol = yf.Ticker(tick) + symbol = symbol.history(start=akhir,end=akhir+timedelta(days=20),interval='1d') + #print(symbol) + symbol = symbol.drop(symbol.index[0]) + symbol = symbol.drop(symbol.index[0]) + symbol = symbol['Close'][0:14].tolist() + + # symbol = symbol['Close'].tolist() + ## print(symbol) + # plt.plot(extend[0:len(symbol)],symbol,color='r',label='Actual',linewidth=2) + # plt.plot(hasil[1],color='r', linestyle='--', label='Confirmation',linewidth=2) + # plt.plot(hasil[2]+extend[0:14],hasil[0],color='b',label='Train',linewidth=2, alpha=0.4) + # plt.plot(symbol, label="Symbol Real") + # plt.plot(hasil[0], label="Real Predictioh") + # print(symbol) + # print(symbol) + # detail = str(akhir)+"\n"+"Prediction :"+str(avg_total[-1:])+"\n"+"Real : " + # plt.text(0.05, 120, detail, color='black', bbox=dict(facecolor='none', edgecolor='black', boxstyle='round,pad=1')) + # plt.legend(loc='upper center', bbox_to_anchor=(0.5, -0.01), + # fancybox=True, shadow=True, ncol=7) + # plt.title(tick+" Date: "+str(hasil[2][-1])+" to "+str(extend[-1])) + # plt.get_xaxis().set_ticks([]) + # plt.xticks([]) + # plt.show() + # plt.xlim([len(date)+len(extend)-30, len(date)+len(extend)]) + ret_data = [] + ret_date = [] + ret_date.append(hasil[2]+extend) + ret_data.append(avg_total) + # ret_data.append(hasil[1]) + ret_data.append(hasil[1]) + return ret_data, ret_date + diff --git a/plotterpitch_min.py b/plotterpitch_min.py new file mode 100644 index 0000000..874f299 --- /dev/null +++ b/plotterpitch_min.py @@ -0,0 +1,172 @@ +import matplotlib.pyplot as plt +import json +import os +from datetime import datetime, date, timedelta +from matplotlib.dates import date2num +import yfinance as yf +import sys +import glob +import numpy as np +from collections import Counter +# path='../Febrian/Bang Nino/Samples/NEE/2020-05-08/' + +my_args = sys.argv[1:] +tick = my_args[0] +filter=int(my_args[1]) +visual=50 +print(tick) +# path='./Samples/'+tick+'/2020-12-07/' +path='./Samples/'+tick+'/' +all_dirs = glob.glob(path+"*") +print(path) +print(all_dirs) +latest_dir = max(all_dirs, key=os.path.getctime) +print("Latest Data = ", latest_dir) +latest_dir = latest_dir + "/" +listsymbol = os.listdir(latest_dir+'/') +print(listsymbol) + +with open(latest_dir+listsymbol[0],'r') as f: + print(latest_dir+listsymbol[0]) + hasil=json.load(f) + # print(hasil) + print(len(hasil[0])) + print(len(hasil[1])) + print(len(hasil[2])) + akhir=datetime.strptime(str(hasil[2][-1]),'%Y-%m-%d').date() + print("Current Log : ",akhir) + extend1=[akhir+timedelta(days=x) for x in range(1,50,1)] + extend=[] + + for x in extend1: + if x.weekday() not in [5,6]: + extend.append(x) + + extend=extend[:14] + extend=[str(x) for x in extend] + print("ASasdasdsa",extend) + # date=date2num(date) + date=hasil[2] + plt.plot(hasil[1],color='grey',linewidth=3, linestyle='--', marker='x', alpha=0.8, label="Confirmation") + plt.plot(hasil[2],hasil[1][0:-7],color='b',linewidth=2) + # plt.show() + +symbol=yf.Ticker(tick) +symbol=symbol.history(start=akhir,end=akhir+timedelta(days=30),interval='1d') +# print(symbol) +symbol=symbol.drop(symbol.index[0]) +symbol=symbol.drop(symbol.index[0]) +symbol=symbol['Close'][0:14].tolist() +# plt.plot(extend,symbol,color='g',linewidth=10) +avg=[] +gap_avg = [] +for ex,x in enumerate(listsymbol): + gap_val = [] + with open(latest_dir+x) as f: + print("#################################################") + print(x) + hasil=json.load(f) + # a=hasil[0][-14:][:14] + a = hasil[0][-14:][:7] + b=hasil[1][-7:] + print("A & B Temp :") + print(a) + print(b) + count=0 + print("Predict - Real ") + for x in range(len(b)): + print("%.2f" % (a[x]-b[x])) + if (b[x]-(filter/10) <= a[x] <= b[x]+(filter/10)): + count=count+1 + if count>4: + print("ACCEPTED . . .") + for i in range(len(b)): + gap_val.append(abs(a[i]-b[i])) + print(len(gap_val)) + predict_val = hasil[0][-14:] + avg.append(predict_val) + # avg.append(hasil[0][-10][:14]) + # plt.plot(date+extend,hasil[0], label='Sample %s'%ex, alpha=0.3) + gap_avg.append(gap_val) + print() + else: + print("NOT ACCEPTED . . .") + print() +# print(avg) +print("#################################################") +print(avg) +print(len(avg)) +print(gap_avg) +print(len(gap_avg)) +print("#################################################") +avg_total=[] +good_index=[] +for x in range(len(extend)): + print("NOW ", x) + temp=[] + gap_temp = [] + pred_temp = [] + if x < 7: + for a in range(len(gap_avg)): + # print(gap_temp.size()) + gap_temp.append(gap_avg[a][x]) + print("Gap Temp") + print(gap_temp) + # for b in range(len(gap_avg)): + good_index.append(gap_temp.index(min(gap_temp))) + else: + print("LOL") + c = Counter(good_index) + c = c.most_common(1) + good_index.append(c[0][0]) + + print("Good Index") + print(len(good_index)) + print(good_index) + print() +for c in range(len(extend)): + print(c) + temp.append(avg[int(good_index[c])][c]) + print(temp) + # avg_total.append(sum(temp)/len(temp)) + # mean_pred = np.mean(temp) + # print(mean_pred) +avg_total = temp + +print(avg_total) +# print(avg_total) + +atas=[x+(1/visual*x) for x in avg_total] +bawah=[x-(1/visual*x) for x in avg_total] +print([hasil[1][0:-5][-1]]+bawah) +print([hasil[1][0:-5][-1]]+atas) +print([date[-1]]+extend) +plt.fill_between([date[-1]]+extend,[hasil[1][0:-5][-1]]+bawah,[hasil[1][0:-5][-1]]+atas,alpha=0.2,label='Prediction Band') +plt.plot([date[-1]]+extend,[hasil[1][-5]]+avg_total,color='y',linewidth=3,label='Prediction', marker='x') +plt.grid() +# plt.show() + +# print("Date Extended ",(date+extend)) +symbol = yf.Ticker(tick) +symbol = symbol.history(start=akhir,end=akhir+timedelta(days=20),interval='1d') +print(symbol) +symbol = symbol.drop(symbol.index[0]) +symbol = symbol.drop(symbol.index[0]) +symbol = symbol['Close'][0:14].tolist() +# symbol = symbol['Close'].tolist() +# print(symbol) +plt.plot(extend[0:len(symbol)],symbol,color='g',label='Actual',linewidth=1) +# plt.plot(hasil[1],color='r', linestyle='--', label='Confirmation',linewidth=2) +# plt.plot(hasil[2]+extend[0:14],hasil[0],color='b',label='Train',linewidth=2, alpha=0.4) +# plt.plot(symbol, label="Symbol Real") +# plt.plot(hasil[0], label="Real Predictioh") +# print(symbol) +# print(symbol) +# detail = str(akhir)+"\n"+"Prediction :"+str(avg_total[-1:])+"\n"+"Real : " +# plt.text(0.05, 120, detail, color='black', bbox=dict(facecolor='none', edgecolor='black', boxstyle='round,pad=1')) +plt.legend(loc='upper center', bbox_to_anchor=(0.5, -0.01), + fancybox=True, shadow=True, ncol=7) +plt.title(tick+" Date: "+str(hasil[2][-1])+" to "+str(extend[-1])) +# plt.get_xaxis().set_ticks([]) +plt.xticks([]) +plt.show() diff --git a/plotterpitch_min_backup.py b/plotterpitch_min_backup.py new file mode 100644 index 0000000..4bd4c6a --- /dev/null +++ b/plotterpitch_min_backup.py @@ -0,0 +1,173 @@ +import matplotlib.pyplot as plt +import json +import os +from datetime import datetime, date, timedelta +from matplotlib.dates import date2num +import yfinance as yf +import sys +import glob +import numpy as np +from collections import Counter +# path='../Febrian/Bang Nino/Samples/NEE/2020-05-08/' + +my_args = sys.argv[1:] +tick = my_args[0] +filter=int(my_args[1]) +visual=50 +print(tick) +# path='./Samples/'+tick+'/2020-12-07/' +path='./Samples/'+tick+'/' +all_dirs = glob.glob(path+"*") +print(path) +print(all_dirs) +latest_dir = max(all_dirs, key=os.path.getctime) +print("Latest Data = ", latest_dir) +latest_dir = latest_dir + "/" +listsymbol = os.listdir(latest_dir+'/') +print(listsymbol) + +with open(latest_dir+listsymbol[0],'r') as f: + print(latest_dir+listsymbol[0]) + hasil=json.load(f) + # print(hasil) + print(len(hasil[0])) + print(len(hasil[1])) + print(len(hasil[2])) + akhir=datetime.strptime(str(hasil[2][-1]),'%Y-%m-%d').date() + print("Current Log : ",akhir) + extend1=[akhir+timedelta(days=x) for x in range(1,50,1)] + extend=[] + + for x in extend1: + if x.weekday() not in [5,6]: + extend.append(x) + + extend=extend[:14] + extend=[str(x) for x in extend] + print("ASasdasdsa",extend) + # date=date2num(date) + date=hasil[2] + #plt.plot(([date[-1]]+extend)[-7:],hasil[1][-7:],color='grey',linewidth=3, linestyle='--', marker='x', alpha=0.8, label="Confirmation") + plt.plot(hasil[2][-10:],hasil[1][0:-7][-10:],color='b',linewidth=2) + # plt.show() + +symbol=yf.Ticker(tick) +symbol=symbol.history(start=akhir,end=akhir+timedelta(days=30),interval='1d') +# print(symbol) +symbol=symbol.drop(symbol.index[0]) +symbol=symbol.drop(symbol.index[0]) +symbol=symbol['Close'][0:14].tolist() +# plt.plot(extend,symbol,color='g',linewidth=10) +avg=[] +gap_avg = [] +for ex,x in enumerate(listsymbol): + gap_val = [] + with open(latest_dir+x) as f: + print("#################################################") + print(x) + hasil=json.load(f) + # a=hasil[0][-14:][:14] + a = hasil[0][-14:][:7] + b=hasil[1][-7:] + print("A & B Temp :") + print(a) + print(b) + count=0 + print("Predict - Real ") + for x in range(len(b)): + print("%.2f" % (a[x]-b[x])) + if (b[x]-(filter/10) <= a[x] <= b[x]+(filter/10)): + count=count+1 + if count>4: + print("ACCEPTED . . .") + for i in range(len(b)): + gap_val.append(abs(a[i]-b[i])) + print(len(gap_val)) + predict_val = hasil[0][-14:] + avg.append(predict_val) + # avg.append(hasil[0][-10][:14]) + plt.plot((date+extend)[-20:],hasil[0][-20:], label='Sample %s'%ex, alpha=0.3) + gap_avg.append(gap_val) + print() + else: + print("NOT ACCEPTED . . .") + print() +# print(avg) +print("#################################################") +print(avg) +print(len(avg)) +print(gap_avg) +print(len(gap_avg)) +print("#################################################") +avg_total=[] +good_index=[] +for x in range(len(extend)): + print("NOW ", x) + temp=[] + gap_temp = [] + pred_temp = [] + if x < 7: + for a in range(len(gap_avg)): + # print(gap_temp.size()) + gap_temp.append(gap_avg[a][x]) + print("Gap Temp") + print(gap_temp) + # for b in range(len(gap_avg)): + good_index.append(gap_temp.index(min(gap_temp))) + else: + print("LOL") + c = Counter(good_index) + c = c.most_common(1) + good_index.append(c[0][0]) + + print("Good Index") + print(len(good_index)) + print(good_index) + print() +for c in range(len(extend)): + print(c) + temp.append(avg[int(good_index[c])][c]) + print(temp) + # avg_total.append(sum(temp)/len(temp)) + # mean_pred = np.mean(temp) + # print(mean_pred) +avg_total = temp + +print(avg_total) +# print(avg_total) + +atas=[x+(1/visual*x) for x in avg_total] +bawah=[x-(1/visual*x) for x in avg_total] +print([hasil[1][0:-5][-1]]+bawah) +print([hasil[1][0:-5][-1]]+atas) +print([date[-1]]+extend) +plt.fill_between([date[-1]]+extend,[hasil[1][0:-5][-1]]+bawah,[hasil[1][0:-5][-1]]+atas,alpha=0.2,label='Prediction Band') +plt.plot([date[-1]]+extend,[hasil[1][-5]]+avg_total,color='y',linewidth=3,label='Prediction', marker='x') +plt.plot(([date[-1]]+extend)[-15:][:8],hasil[1][-8:],color='grey',linewidth=3, linestyle='--', marker='x', alpha=0.8, label="Confirmation") +plt.grid() +# plt.show() + +# print("Date Extended ",(date+extend)) +symbol = yf.Ticker(tick) +symbol = symbol.history(start=akhir,end=akhir+timedelta(days=20),interval='1d') +print(symbol) +symbol = symbol.drop(symbol.index[0]) +symbol = symbol.drop(symbol.index[0]) +symbol = symbol['Close'][0:14].tolist() +# symbol = symbol['Close'].tolist() +# print(symbol) +#plt.plot(extend[0:len(symbol)],symbol,color='g',label='Actual',linewidth=1) +# plt.plot(hasil[1],color='r', linestyle='--', label='Confirmation',linewidth=2) +# plt.plot(hasil[2]+extend[0:14],hasil[0],color='b',label='Train',linewidth=2, alpha=0.4) +# plt.plot(symbol, label="Symbol Real") +# plt.plot(hasil[0], label="Real Predictioh") +# print(symbol) +# print(symbol) +# detail = str(akhir)+"\n"+"Prediction :"+str(avg_total[-1:])+"\n"+"Real : " +# plt.text(0.05, 120, detail, color='black', bbox=dict(facecolor='none', edgecolor='black', boxstyle='round,pad=1')) +plt.legend(loc='upper center', bbox_to_anchor=(0.5, -0.01), + fancybox=True, shadow=True, ncol=7) +plt.title(tick+" Date: "+str(hasil[2][-1])+" to "+str(extend[-1])) +# plt.get_xaxis().set_ticks([]) +plt.xticks([]) +plt.show() diff --git a/predictor.py b/predictor.py new file mode 100644 index 0000000..a9dcfed --- /dev/null +++ b/predictor.py @@ -0,0 +1,624 @@ +import os +import sys +os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' +import tensorflow as tf +import numpy as np +import matplotlib.animation as animation +import matplotlib.pyplot as plt +import seaborn as sns +import pandas as pd +from sklearn.preprocessing import MinMaxScaler +from datetime import datetime +from datetime import timedelta +from tqdm import tqdm +sns.set() +import json +tf.compat.v1.random.set_random_seed(1234) +from matplotlib import style +# import matplotlib.backends.backend_qt5agg +# %matplotlib qt +style.use('ggplot') +import math +import yfinance as yf +import time +from datetime import date, timedelta +os.environ["CUDA_VISIBLE_DEVICES"] = "-1" +tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR) +import cProfile +import pstats + +# In[34]: + +symbols=os.listdir() +symbols +tf.compat.v1.disable_eager_execution() +# INITIAL VARS +test_size = 14 +simulation_size = 1 + +# MODEL VARS +num_layers = 2 +size_layer = 128 +timestamp = 7 +epoch = 20 +dropout_rate = 0.8 +prediction_gap = sys.argv[2] +future_day = test_size +learning_rate = 0.01 +graph_loss = [] + +# In[35]: +# Necessary Dirs +# def date_manage(date1,date2=None): +# if date2 is None: +# date2=date1+timedelta(days=365) +# date_col=[] +# for n in range(int ((date2 - date1).days)+1): +# date_col.append(date1 + timedelta(n)) +# weekdays = [5,6] +# date_result=[] +# for dt in date_col: +# if dt.weekday() not in weekdays: +# dt.strftime("%Y-%m-%d") +# return date_result + + +# In[36]: +def loss_animate(ax, i): + json_loss = pd.DataFrame(total_loss) + ax.plot(i) + return ax + + +def loader(symbol,test_size,date): + # dateparse = lambda dates : pd.datetime.strptime(dates,'%Y-%m') + # df = pd.read_csv('../dataset/IBMCUT.csv',parse_dates=['Date'], index_col = 'Date', date_parser=dateparse) + + df=yf.Ticker(symbol) + # df=df.history(period="1y",interval="1d") + # df=df.history(start=date-timedelta(days=365),end=date,interval="1d") + df=df.history(start=date-timedelta(days=365*3),end=date,interval="1d") + df=df.reset_index(level=0) + + # df=df.drop(columns=['Dividends'], axis=1) + df=df.drop(columns=['Stock Splits'], axis=1) + + df['Up'] = df['High'].ewm(span=6,adjust=False).mean() + 2* df['High'].rolling(window=6).std() + df['Down']= df['Low'].ewm(span=8,adjust=False).mean() - 2* df['Low'].rolling(window=8).std() + + df=df.dropna() + df=df.drop(df.tail(5).index) + + date_ori = pd.to_datetime(df.iloc[:, 0]).tolist() +# for i in range(test_size): +# #date_ori.append(date_ori[-1] + timedelta(days = 1)) +# add=1 +# while ((date_ori[-1]) + timedelta(days = add)).weekday() in [5,6]: +# add=add+1 +# date_ori.append(date_ori[-1] + timedelta(days = add)) + date_ori = pd.Series(date_ori).dt.strftime(date_format = '%Y-%m-%d').tolist() + print(len(df),len(date_ori)) + return df,date_ori + +def trueloader(symbol,test_size,date): + # df2 = pd.read_csv(symbol) + # print("LENDF2:",len(df2)) + + df2 = yf.Ticker(symbol) + # df2 = df2.history(start=date-timedelta(days=365),end=date,interval="1d") + df2 = df2.history(start=date-timedelta(days=365*3),end=date,interval="1d") + df2 = df2.reset_index(level=0) + df2 = df2.drop(columns=['Dividends'], axis=1) + df2 = df2.drop(columns=['Stock Splits'], axis=1) + df2 = df2.drop(df2.head(7).index) + # df2 = df2.drop(df2.tail(test_size).index) + + return df2 + +# In[38]: + +def preproc(df): + minmax = MinMaxScaler().fit(df.iloc[:,1:9].astype('float32')) # Close, Volume, and all + df_log = minmax.transform(df.iloc[:, 1:9].astype('float32')) # Close, Volume, and all + df_log = pd.DataFrame(df_log) + df_log.head() + return df_log,minmax + + +# In[39]: + + +class Model: + def __init__( + self, + learning_rate, + num_layers, + size, + size_layer, + output_size, + forget_bias = 0.1, + ): + def lstm_cell(size_layer): + # print("ASDasdasdasd",len(tf.compat.v1.nn.rnn_cell.LSTMCell(size_layer, state_is_tuple = False)) + return tf.compat.v1.nn.rnn_cell.LSTMCell(size_layer, state_is_tuple = False) + + rnn_cells = tf.compat.v1.nn.rnn_cell.MultiRNNCell( + [lstm_cell(size_layer) for _ in range(num_layers)], + state_is_tuple = False, + ) + self.X = tf.compat.v1.placeholder(tf.float32, (None, None, size)) + self.Y = tf.compat.v1.placeholder(tf.float32, (None, output_size)) +# self.X = tf.keras.Input((None, size),dtype=tf.float32) +# self.Y = tf.keras.Input((output_size),dtype=tf.float32) + drop = tf.compat.v1.nn.rnn_cell.DropoutWrapper( + rnn_cells, output_keep_prob = forget_bias + ) +# print("XXXX:",self.X) +# print("XXXX:",self.X.shape) +# print("XXXX:",self.Y) +# print("XXXX:",self.Y.shape) + #print("LOOOASDSDASD") + self.hidden_layer = tf.compat.v1.placeholder( + tf.float32, (None, num_layers * 2 * size_layer) + ) + self.outputs, self.last_state = tf.compat.v1.nn.dynamic_rnn( + drop, self.X, initial_state = self.hidden_layer, dtype = tf.float32 + ) + #rint("INIDIA",self.outputs) + self.logits = tf.compat.v1.layers.dense(self.outputs[-1], output_size) + self.cost = tf.reduce_mean(tf.square(self.Y - self.logits)) + self.optimizer = tf.compat.v1.train.AdamOptimizer(learning_rate).minimize( + self.cost + ) + #print("cost:",self.cost) + #print("cost:",self.optimizer) + +def calculate_accuracy(real, predict): + real = np.array(real) + 1 + predict = np.array(predict) + 1 + percentage = 1 - np.sqrt(np.mean(np.square((real - predict) / real))) + return percentage * 100 + +def anchor(signal, weight): + buffer = [] + last = signal[0] + for i in signal: + smoothed_val = last * weight + (1 - weight) * i + buffer.append(smoothed_val) + last = smoothed_val + return buffer + + +# In[40]: +def main_train(df_beta, df_train, df, minmax): + + modelnn = Model( + learning_rate, num_layers, df_beta.shape[1], size_layer, df_beta.shape[1], dropout_rate + ) + sess = tf.compat.v1.Session() + sess = tf.compat.v1.Session(config=tf.compat.v1.ConfigProto(intra_op_parallelism_threads=64,inter_op_parallelism_threads=64)) + sess.run(tf.compat.v1.global_variables_initializer()) + pbar = tqdm(range(10), desc = 'Main train loop') # Default 500 range + for i in pbar: + init_value = np.zeros((1, num_layers * 2 * size_layer)) + total_loss, total_acc = [], [] + print("Degugging : ") + for k in range(0, df_train.shape[0] - 1, timestamp): + index = min(k + timestamp, df_train.shape[0] - 1) + print(index) + batch_x = np.expand_dims( + df_train.iloc[k : index, :].values, axis = 0 + ) + + batch_y = df_train.iloc[k + 1 : index + 1, :].values + #print("BATCH_X:",batch_x) + #print("BATCH_Y:",batch_y) + logits, last_state,__,loss = sess.run( + [modelnn.logits, modelnn.last_state,modelnn.optimizer, modelnn.cost], + feed_dict = { + modelnn.X: batch_x, + modelnn.Y: batch_y, + modelnn.hidden_layer: init_value, + }, + ) + init_value = last_state + total_loss.append(loss) + total_acc.append(calculate_accuracy(batch_y[:, 0], logits[:, 0])) + # json_loss.to_json("./loss.json") + graph_loss.append(np.mean(total_loss)) + + #np.save(loss_file, np.array(graph_loss)) + pbar.set_postfix(cost = np.mean(total_loss), min_acc = np.min(total_acc), mean_acc=np.mean(total_acc)) + + +def forecast(df_beta,df_train,df,minmax): +# print("DF_BETA:",df_beta) +# print("DF_TRAIN:",df_train) + # tf.compat.v1.variable_scope("AAA", reuse=True) + tf.compat.v1.reset_default_graph() + modelnn = Model( + learning_rate, num_layers, df_beta.shape[1], size_layer, df_beta.shape[1], dropout_rate + ) +# print("MODELX: ",modelnn.X) +# print("MODELY: ",modelnn.Y) +# print("MODELLayer: ",modelnn.hidden_layer) + sess = tf.compat.v1.Session() + sess = tf.compat.v1.Session(config=tf.compat.v1.ConfigProto(intra_op_parallelism_threads=64,inter_op_parallelism_threads=64)) + sess.run(tf.compat.v1.global_variables_initializer()) + date_ori = pd.to_datetime(df.iloc[:, 0]).tolist() +# print("INI___!:",df_train.shape[0] - 1, timestamp) +# print(df_train.shape[0]) + pbar = tqdm(range(epoch), desc = 'train loop') + for i in pbar: + # init_value = np.zeros((1, num_layers * 2 * size_layer)) + total_loss, total_acc = [], [] + print("Degugging : ") + for k in range(0, df_train.shape[0] - 1, timestamp): + init_value = np.zeros((1, num_layers * 2 * size_layer)) + index = min(k + timestamp, df_train.shape[0] - 1) + # print(index) + batch_x = np.expand_dims( + df_train.iloc[k : index, :].values, axis = 0 + ) + + batch_y = df_train.iloc[k + 1 : index + 1, :].values + print("BATCH_X:",batch_x) + print("BATCH_Y:",batch_y) + logits, last_state,__,loss = sess.run( + [modelnn.logits, modelnn.last_state,modelnn.optimizer, modelnn.cost], + feed_dict = { + modelnn.X: batch_x, + modelnn.Y: batch_y, + modelnn.hidden_layer: init_value, + }, + ) + # print("BATCHX:",batch_x) + # print("MODELX: ",modelnn.X) + # print("MODELY: ",modelnn.Y) + # print("MODELLayer: ",modelnn.hidden_layer) + # print("OUTSSS:",len(outs)) + # print("opt:",opt) + # print("outs1",batch_x[0]) + # print("outs2",outs[1]) + # print("outs3",outs[2]) + # print("outs4",outs[3]) + # input() + init_value = last_state + total_loss.append(loss) + #print("LOGITS:",logits[:, 0]) + total_acc.append(calculate_accuracy(batch_y[:, 0], logits[:, 0])) + # json_loss.to_json("./loss.json") + graph_loss.append(np.mean(total_loss)) + + #np.save(loss_file, np.array(graph_loss)) + pbar.set_postfix(cost = np.mean(total_loss), min_acc = np.min(total_acc), mean_acc=np.mean(total_acc)) + future_day = test_size + + output_predict = np.zeros((df_train.shape[0] + future_day, df_train.shape[1])) + output_predict[0] = df_train.iloc[0] + upper_b = (df_train.shape[0] // timestamp) * timestamp + init_value = np.zeros((1, num_layers * 2 * size_layer)) + + for k in range(0, (df_train.shape[0] // timestamp) * timestamp, timestamp): + out_logits, last_state = sess.run( + [modelnn.logits, modelnn.last_state], + feed_dict = { + modelnn.X: np.expand_dims( + df_train.iloc[k : k + timestamp], axis = 0 + ), + modelnn.hidden_layer: init_value, + }, + ) + init_value = last_state + output_predict[k + 1 : k + timestamp + 1] = out_logits + + if upper_b != df_train.shape[0]: + out_logits, last_state = sess.run( + [modelnn.logits, modelnn.last_state], + feed_dict = { + modelnn.X: np.expand_dims(df_train.iloc[upper_b:], axis = 0), + modelnn.hidden_layer: init_value, + }, + ) + output_predict[upper_b + 1 : df_train.shape[0] + 1] = out_logits + future_day -= 1 + date_ori.append(date_ori[-1] + timedelta(days = 1)) + + init_value = last_state + + for i in range(future_day): + o = output_predict[-future_day - timestamp + i:-future_day + i] + out_logits, last_state = sess.run( + [modelnn.logits, modelnn.last_state], + feed_dict = { + modelnn.X: np.expand_dims(o, axis = 0), + modelnn.hidden_layer: init_value, + }, + ) + init_value = last_state + output_predict[-future_day + i] = out_logits[-1] + date_ori.append(date_ori[-1] + timedelta(days = 1)) + + output_predict = minmax.inverse_transform(output_predict) + deep_future = anchor(output_predict[:, 0], 0.3) + sess.close() + sess.__del__() + return deep_future + + +# In[41]: + + +def newaccuration(accepted_results,truetrend): + hasilutama=0 + indexbagus=0 + truest=0 + predictest=0 + for i,x in enumerate(accepted_results): + a=x[-(test_size+2):] + + #a=x[:((test_size+2)/2)] + print("a",a) + b=truetrend[-(test_size+1):] + #print("b",b) + hasil=0 + true=[] + predict=[] + for xy in range(1,len((a))): + if a[xy] hasilutama: + hasilutama=hasil + indexbagus=i + truest=true + predictest=predict + salah=[] + for xz in range(len(truest)): + if truest[xz]!=predictest[xz]: + salah.append(xz) + # if xz!=0: + # salah.append(xz-1) + # print("INI:",b) + print("TRUEST",truest) + print("predictest",predictest) + return hasilutama,indexbagus,salah + +# In[42]: + + +def betaforecast(simulationsize,dfx,dftrain,df,df2,minmax): + results = [] + for i in range(simulationsize): + forecast_res = forecast(df,dftrain,dfx,minmax) + results.append(forecast_res) + accepted_results = [] + + while not (np.array(results[0][-test_size:]) < np.min(dfx['Close'])).sum() == 0 and (np.array(results[0][-test_size:]) > np.max(dfx['Close']) * 2).sum() == 0: + print("++++++++++++++++++++++++") + print("Forecast Recalled...") + results[0]=forecast(df,dftrain,dfx,minmax) + return results[0] + + +# In[43]: + + +def interval(p1,p2): + return abs((p1) - (p2)) + + +# In[44]: + + +def checkaccuracy2(true): + avg=[] + for x in range(len(true)-7): + avg.append(interval(true[x],true[x+1])) + average=sum(avg) / len(avg) + return average + + + +# In[45]: + + +def checkaccuracy(predict,true,filterx, test_size): + print("True Length: ",len(true)) + print("Predict Length: ",len(predict)) + +# avg=[] + +# for x in range(len(true)-5): +# avg.append(interval(true[x],predict[x])) +# average=sum(avg) / len(avg) +# print("AVG1:",average) +# print("AVG2:",threshold) + + temp_predict=predict[-test_size:] + temp_true=true[-test_size:] + +# avg2=interval(max(predict),min(predict)) + count=0 + print("------------------------------------") + for x in range(test_size): + # acc_var1 = temp_true[x]-(1/filterx*temp_true[x]) + acc_var1 = temp_true[x]-(filterx/10) + acc_var2 = temp_predict[x] + # acc_var3 = temp_true[x]+(1/filterx*temp_true[x]) + acc_var3 = temp_true[x]+(filterx/10) + acc_condition = acc_var1 <= acc_var2 <= acc_var3 + # print("Var 1 : ",acc_var1) + # print("Var 2 : ",acc_var2) + # print("Var 3 : ",acc_var3) + # print("Day "+str(x+1)+" "+str(int(acc_var1))+" "+str(int(acc_var2))+" "+str(int(acc_var3))+" : ",acc_condition) + print("Day "+str(x+1)+", Price : "+str(int(temp_true[x]))+" ,Gap = "+str(int(abs(temp_predict[x]-temp_true[x])))+" : ",acc_condition) + if (acc_condition): + count=count+1 + print("------------------------------------") + if count>7: + print("Result True") + return True + else: + print("Result False") + return False + +# if average>threshold: +# return False +# else: +# return True + + +# In[46]: + + +def findthreshold(simulationsize,dfx,dftrain,df,df2,minmax): + results=[] + for i in range(simulationsize): + results.append(forecast(df,dftrain,dfx,minmax)) + accepted_results = [] + + for r in results: + + if (np.array(r[-test_size:]) < np.min(dfx['Close'])).sum() == 0 and (np.array(r[-test_size:]) > np.max(dfx['Close']) * 2).sum() == 0: + accepted_results.append(r) + + finalavg=999999 + for o in accepted_results: + avg=[] + for x in range(len(o)-5): + avg.append(interval(o[x],df2[x])) + average=sum(avg) / len(avg) + if average<=finalavg: + finalavg=average + + return finalavg + +def temp_data(date, xi, resultfinal, df2, date_col,x): + print("Called . . . ") + if os.path.isdir("TempData/") == False: + os.mkdir("TempData/") + if os.path.isdir("TempData/%s"%x) == False: + os.mkdir("TempData/%s"%x) + if os.path.isdir("TempData/%s/"%x+str(date)) == False: + os.mkdir("TempData/%s/"%x+str(date)) + with open("TempData/%s/"%x+str(date)+"/"+x+str(xi)+".vezpal2","w+") as oop: + main=[] + main.append(resultfinal) # prediction + main.append(list(df2['Close'])) + main.append(date_col) +# main.append(3) +# main.append([0]) + json.dump(main,oop) + +def automaton(simulationsize,date): + + # symbols=["AAPL"] + symbols = sys.argv[1] + times=[] + x=symbols + # for x in symbols: + temp_time=[] + temp_time.append(x) + counter=0 + validity=0 + df,date_col=loader(x,test_size,date) + # print(type(df)) + dfx=df + # print("ASDSAD") + df2=trueloader(x,test_size,date) + df,minmax=preproc(df) + dftrain=df + wrong=[1,2,3,4,5] + # avg=checkaccuracy2(list(df2["Close"])) + # start=time.time() + # avg=findthreshold(50,dfx,dftrain,df,list(df2["Close"]),minmax) + # temp_time.append(time.time()-start) + start=time.time() + filterx = int(prediction_gap) + able=False + print("============== || Initial Train || =============") + main_train(df,dftrain,dfx,minmax) + for xi in range(5): + decision=False + while (decision==False): + print() + print("====== [ Foreacasting Attempt : "+str(counter+1)+" ] ===========") + print("====== [ Progress : "+str(xi)+"/5 ] ") + resultfinal=betaforecast(simulationsize,dfx,dftrain,df,df2,minmax) + # validity=valid + decision=checkaccuracy(resultfinal,list(df2["Close"]),filterx, test_size) + # wrong=invalid + if decision==True: + able=True + print("ABLE") + print(str(filterx)) + if counter > 10 and decision != True: + counter = 0 + filterx=filterx+10 + print("Filter X new value : "+str(filterx)) + print("Decision Status : ", decision) + print("**************************************") +# avg=avg+(1/3*avg) + if filterx>1000: + print("====== [ GG, we gave up] =====") + continue + counter=counter+1 + temp_data(date, xi, resultfinal, df2, date_col, x) + print("[ Loop : "+x+" done ] =========================") + print() + + if os.path.isdir("Backtest/") == False: + os.mkdir("Backtest/") + if os.path.isdir("Backtest/%s"%x) == False: + os.mkdir("Backtest/%s"%x) + if os.path.isdir("Backtest/%s/"%x+str(date)) == False: + os.mkdir("Backtest/%s/"%x+str(date)) + with open("Backtest/%s/"%x+str(date)+"/"+x+str(xi)+".vezpal2","w+") as oop: + main=[] + main.append(resultfinal) #prediction + main.append(list(df2['Close'])) + main.append(date_col) +# main.append(3) +# main.append([0]) + json.dump(main,oop) + print("Time for %s :"%x,time.time()-start) + temp_time.append(time.time()-start) + times.append(temp_time) + return times + +def predictor(simulationsize,current): + for x in range(52): + tf.compat.v1.reset_default_graph() + current+=timedelta(days=7) + automaton(simulationsize,current) + +current_date=date(2020,1,1) +if os.path.isdir("Loss/") == False: + os.mkdir("Loss/") +if os.path.isdir("Loss/"+str(current_date)) == False: + os.mkdir("Loss/"+str(current_date)) + +# loss_file = time.strftime("%Y%m%d-%H%M%S") +# loss_file = "Loss/"+str(date.today())+"/"+loss_file +global_start = time.time() +# profile = cProfile.Profile() + +# main_func = "predictor(simulation_size,current_date)" +predictor(simulation_size,current_date) +# ps = pstats.Stats(profile.run(main_func)) +print("Overall time consumption ", str(time.time()-global_start)) +# ps.dump_stats("./Cprofile_model_01.ps") + diff --git a/predictor_backup.py b/predictor_backup.py new file mode 100644 index 0000000..a9dcfed --- /dev/null +++ b/predictor_backup.py @@ -0,0 +1,624 @@ +import os +import sys +os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' +import tensorflow as tf +import numpy as np +import matplotlib.animation as animation +import matplotlib.pyplot as plt +import seaborn as sns +import pandas as pd +from sklearn.preprocessing import MinMaxScaler +from datetime import datetime +from datetime import timedelta +from tqdm import tqdm +sns.set() +import json +tf.compat.v1.random.set_random_seed(1234) +from matplotlib import style +# import matplotlib.backends.backend_qt5agg +# %matplotlib qt +style.use('ggplot') +import math +import yfinance as yf +import time +from datetime import date, timedelta +os.environ["CUDA_VISIBLE_DEVICES"] = "-1" +tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR) +import cProfile +import pstats + +# In[34]: + +symbols=os.listdir() +symbols +tf.compat.v1.disable_eager_execution() +# INITIAL VARS +test_size = 14 +simulation_size = 1 + +# MODEL VARS +num_layers = 2 +size_layer = 128 +timestamp = 7 +epoch = 20 +dropout_rate = 0.8 +prediction_gap = sys.argv[2] +future_day = test_size +learning_rate = 0.01 +graph_loss = [] + +# In[35]: +# Necessary Dirs +# def date_manage(date1,date2=None): +# if date2 is None: +# date2=date1+timedelta(days=365) +# date_col=[] +# for n in range(int ((date2 - date1).days)+1): +# date_col.append(date1 + timedelta(n)) +# weekdays = [5,6] +# date_result=[] +# for dt in date_col: +# if dt.weekday() not in weekdays: +# dt.strftime("%Y-%m-%d") +# return date_result + + +# In[36]: +def loss_animate(ax, i): + json_loss = pd.DataFrame(total_loss) + ax.plot(i) + return ax + + +def loader(symbol,test_size,date): + # dateparse = lambda dates : pd.datetime.strptime(dates,'%Y-%m') + # df = pd.read_csv('../dataset/IBMCUT.csv',parse_dates=['Date'], index_col = 'Date', date_parser=dateparse) + + df=yf.Ticker(symbol) + # df=df.history(period="1y",interval="1d") + # df=df.history(start=date-timedelta(days=365),end=date,interval="1d") + df=df.history(start=date-timedelta(days=365*3),end=date,interval="1d") + df=df.reset_index(level=0) + + # df=df.drop(columns=['Dividends'], axis=1) + df=df.drop(columns=['Stock Splits'], axis=1) + + df['Up'] = df['High'].ewm(span=6,adjust=False).mean() + 2* df['High'].rolling(window=6).std() + df['Down']= df['Low'].ewm(span=8,adjust=False).mean() - 2* df['Low'].rolling(window=8).std() + + df=df.dropna() + df=df.drop(df.tail(5).index) + + date_ori = pd.to_datetime(df.iloc[:, 0]).tolist() +# for i in range(test_size): +# #date_ori.append(date_ori[-1] + timedelta(days = 1)) +# add=1 +# while ((date_ori[-1]) + timedelta(days = add)).weekday() in [5,6]: +# add=add+1 +# date_ori.append(date_ori[-1] + timedelta(days = add)) + date_ori = pd.Series(date_ori).dt.strftime(date_format = '%Y-%m-%d').tolist() + print(len(df),len(date_ori)) + return df,date_ori + +def trueloader(symbol,test_size,date): + # df2 = pd.read_csv(symbol) + # print("LENDF2:",len(df2)) + + df2 = yf.Ticker(symbol) + # df2 = df2.history(start=date-timedelta(days=365),end=date,interval="1d") + df2 = df2.history(start=date-timedelta(days=365*3),end=date,interval="1d") + df2 = df2.reset_index(level=0) + df2 = df2.drop(columns=['Dividends'], axis=1) + df2 = df2.drop(columns=['Stock Splits'], axis=1) + df2 = df2.drop(df2.head(7).index) + # df2 = df2.drop(df2.tail(test_size).index) + + return df2 + +# In[38]: + +def preproc(df): + minmax = MinMaxScaler().fit(df.iloc[:,1:9].astype('float32')) # Close, Volume, and all + df_log = minmax.transform(df.iloc[:, 1:9].astype('float32')) # Close, Volume, and all + df_log = pd.DataFrame(df_log) + df_log.head() + return df_log,minmax + + +# In[39]: + + +class Model: + def __init__( + self, + learning_rate, + num_layers, + size, + size_layer, + output_size, + forget_bias = 0.1, + ): + def lstm_cell(size_layer): + # print("ASDasdasdasd",len(tf.compat.v1.nn.rnn_cell.LSTMCell(size_layer, state_is_tuple = False)) + return tf.compat.v1.nn.rnn_cell.LSTMCell(size_layer, state_is_tuple = False) + + rnn_cells = tf.compat.v1.nn.rnn_cell.MultiRNNCell( + [lstm_cell(size_layer) for _ in range(num_layers)], + state_is_tuple = False, + ) + self.X = tf.compat.v1.placeholder(tf.float32, (None, None, size)) + self.Y = tf.compat.v1.placeholder(tf.float32, (None, output_size)) +# self.X = tf.keras.Input((None, size),dtype=tf.float32) +# self.Y = tf.keras.Input((output_size),dtype=tf.float32) + drop = tf.compat.v1.nn.rnn_cell.DropoutWrapper( + rnn_cells, output_keep_prob = forget_bias + ) +# print("XXXX:",self.X) +# print("XXXX:",self.X.shape) +# print("XXXX:",self.Y) +# print("XXXX:",self.Y.shape) + #print("LOOOASDSDASD") + self.hidden_layer = tf.compat.v1.placeholder( + tf.float32, (None, num_layers * 2 * size_layer) + ) + self.outputs, self.last_state = tf.compat.v1.nn.dynamic_rnn( + drop, self.X, initial_state = self.hidden_layer, dtype = tf.float32 + ) + #rint("INIDIA",self.outputs) + self.logits = tf.compat.v1.layers.dense(self.outputs[-1], output_size) + self.cost = tf.reduce_mean(tf.square(self.Y - self.logits)) + self.optimizer = tf.compat.v1.train.AdamOptimizer(learning_rate).minimize( + self.cost + ) + #print("cost:",self.cost) + #print("cost:",self.optimizer) + +def calculate_accuracy(real, predict): + real = np.array(real) + 1 + predict = np.array(predict) + 1 + percentage = 1 - np.sqrt(np.mean(np.square((real - predict) / real))) + return percentage * 100 + +def anchor(signal, weight): + buffer = [] + last = signal[0] + for i in signal: + smoothed_val = last * weight + (1 - weight) * i + buffer.append(smoothed_val) + last = smoothed_val + return buffer + + +# In[40]: +def main_train(df_beta, df_train, df, minmax): + + modelnn = Model( + learning_rate, num_layers, df_beta.shape[1], size_layer, df_beta.shape[1], dropout_rate + ) + sess = tf.compat.v1.Session() + sess = tf.compat.v1.Session(config=tf.compat.v1.ConfigProto(intra_op_parallelism_threads=64,inter_op_parallelism_threads=64)) + sess.run(tf.compat.v1.global_variables_initializer()) + pbar = tqdm(range(10), desc = 'Main train loop') # Default 500 range + for i in pbar: + init_value = np.zeros((1, num_layers * 2 * size_layer)) + total_loss, total_acc = [], [] + print("Degugging : ") + for k in range(0, df_train.shape[0] - 1, timestamp): + index = min(k + timestamp, df_train.shape[0] - 1) + print(index) + batch_x = np.expand_dims( + df_train.iloc[k : index, :].values, axis = 0 + ) + + batch_y = df_train.iloc[k + 1 : index + 1, :].values + #print("BATCH_X:",batch_x) + #print("BATCH_Y:",batch_y) + logits, last_state,__,loss = sess.run( + [modelnn.logits, modelnn.last_state,modelnn.optimizer, modelnn.cost], + feed_dict = { + modelnn.X: batch_x, + modelnn.Y: batch_y, + modelnn.hidden_layer: init_value, + }, + ) + init_value = last_state + total_loss.append(loss) + total_acc.append(calculate_accuracy(batch_y[:, 0], logits[:, 0])) + # json_loss.to_json("./loss.json") + graph_loss.append(np.mean(total_loss)) + + #np.save(loss_file, np.array(graph_loss)) + pbar.set_postfix(cost = np.mean(total_loss), min_acc = np.min(total_acc), mean_acc=np.mean(total_acc)) + + +def forecast(df_beta,df_train,df,minmax): +# print("DF_BETA:",df_beta) +# print("DF_TRAIN:",df_train) + # tf.compat.v1.variable_scope("AAA", reuse=True) + tf.compat.v1.reset_default_graph() + modelnn = Model( + learning_rate, num_layers, df_beta.shape[1], size_layer, df_beta.shape[1], dropout_rate + ) +# print("MODELX: ",modelnn.X) +# print("MODELY: ",modelnn.Y) +# print("MODELLayer: ",modelnn.hidden_layer) + sess = tf.compat.v1.Session() + sess = tf.compat.v1.Session(config=tf.compat.v1.ConfigProto(intra_op_parallelism_threads=64,inter_op_parallelism_threads=64)) + sess.run(tf.compat.v1.global_variables_initializer()) + date_ori = pd.to_datetime(df.iloc[:, 0]).tolist() +# print("INI___!:",df_train.shape[0] - 1, timestamp) +# print(df_train.shape[0]) + pbar = tqdm(range(epoch), desc = 'train loop') + for i in pbar: + # init_value = np.zeros((1, num_layers * 2 * size_layer)) + total_loss, total_acc = [], [] + print("Degugging : ") + for k in range(0, df_train.shape[0] - 1, timestamp): + init_value = np.zeros((1, num_layers * 2 * size_layer)) + index = min(k + timestamp, df_train.shape[0] - 1) + # print(index) + batch_x = np.expand_dims( + df_train.iloc[k : index, :].values, axis = 0 + ) + + batch_y = df_train.iloc[k + 1 : index + 1, :].values + print("BATCH_X:",batch_x) + print("BATCH_Y:",batch_y) + logits, last_state,__,loss = sess.run( + [modelnn.logits, modelnn.last_state,modelnn.optimizer, modelnn.cost], + feed_dict = { + modelnn.X: batch_x, + modelnn.Y: batch_y, + modelnn.hidden_layer: init_value, + }, + ) + # print("BATCHX:",batch_x) + # print("MODELX: ",modelnn.X) + # print("MODELY: ",modelnn.Y) + # print("MODELLayer: ",modelnn.hidden_layer) + # print("OUTSSS:",len(outs)) + # print("opt:",opt) + # print("outs1",batch_x[0]) + # print("outs2",outs[1]) + # print("outs3",outs[2]) + # print("outs4",outs[3]) + # input() + init_value = last_state + total_loss.append(loss) + #print("LOGITS:",logits[:, 0]) + total_acc.append(calculate_accuracy(batch_y[:, 0], logits[:, 0])) + # json_loss.to_json("./loss.json") + graph_loss.append(np.mean(total_loss)) + + #np.save(loss_file, np.array(graph_loss)) + pbar.set_postfix(cost = np.mean(total_loss), min_acc = np.min(total_acc), mean_acc=np.mean(total_acc)) + future_day = test_size + + output_predict = np.zeros((df_train.shape[0] + future_day, df_train.shape[1])) + output_predict[0] = df_train.iloc[0] + upper_b = (df_train.shape[0] // timestamp) * timestamp + init_value = np.zeros((1, num_layers * 2 * size_layer)) + + for k in range(0, (df_train.shape[0] // timestamp) * timestamp, timestamp): + out_logits, last_state = sess.run( + [modelnn.logits, modelnn.last_state], + feed_dict = { + modelnn.X: np.expand_dims( + df_train.iloc[k : k + timestamp], axis = 0 + ), + modelnn.hidden_layer: init_value, + }, + ) + init_value = last_state + output_predict[k + 1 : k + timestamp + 1] = out_logits + + if upper_b != df_train.shape[0]: + out_logits, last_state = sess.run( + [modelnn.logits, modelnn.last_state], + feed_dict = { + modelnn.X: np.expand_dims(df_train.iloc[upper_b:], axis = 0), + modelnn.hidden_layer: init_value, + }, + ) + output_predict[upper_b + 1 : df_train.shape[0] + 1] = out_logits + future_day -= 1 + date_ori.append(date_ori[-1] + timedelta(days = 1)) + + init_value = last_state + + for i in range(future_day): + o = output_predict[-future_day - timestamp + i:-future_day + i] + out_logits, last_state = sess.run( + [modelnn.logits, modelnn.last_state], + feed_dict = { + modelnn.X: np.expand_dims(o, axis = 0), + modelnn.hidden_layer: init_value, + }, + ) + init_value = last_state + output_predict[-future_day + i] = out_logits[-1] + date_ori.append(date_ori[-1] + timedelta(days = 1)) + + output_predict = minmax.inverse_transform(output_predict) + deep_future = anchor(output_predict[:, 0], 0.3) + sess.close() + sess.__del__() + return deep_future + + +# In[41]: + + +def newaccuration(accepted_results,truetrend): + hasilutama=0 + indexbagus=0 + truest=0 + predictest=0 + for i,x in enumerate(accepted_results): + a=x[-(test_size+2):] + + #a=x[:((test_size+2)/2)] + print("a",a) + b=truetrend[-(test_size+1):] + #print("b",b) + hasil=0 + true=[] + predict=[] + for xy in range(1,len((a))): + if a[xy] hasilutama: + hasilutama=hasil + indexbagus=i + truest=true + predictest=predict + salah=[] + for xz in range(len(truest)): + if truest[xz]!=predictest[xz]: + salah.append(xz) + # if xz!=0: + # salah.append(xz-1) + # print("INI:",b) + print("TRUEST",truest) + print("predictest",predictest) + return hasilutama,indexbagus,salah + +# In[42]: + + +def betaforecast(simulationsize,dfx,dftrain,df,df2,minmax): + results = [] + for i in range(simulationsize): + forecast_res = forecast(df,dftrain,dfx,minmax) + results.append(forecast_res) + accepted_results = [] + + while not (np.array(results[0][-test_size:]) < np.min(dfx['Close'])).sum() == 0 and (np.array(results[0][-test_size:]) > np.max(dfx['Close']) * 2).sum() == 0: + print("++++++++++++++++++++++++") + print("Forecast Recalled...") + results[0]=forecast(df,dftrain,dfx,minmax) + return results[0] + + +# In[43]: + + +def interval(p1,p2): + return abs((p1) - (p2)) + + +# In[44]: + + +def checkaccuracy2(true): + avg=[] + for x in range(len(true)-7): + avg.append(interval(true[x],true[x+1])) + average=sum(avg) / len(avg) + return average + + + +# In[45]: + + +def checkaccuracy(predict,true,filterx, test_size): + print("True Length: ",len(true)) + print("Predict Length: ",len(predict)) + +# avg=[] + +# for x in range(len(true)-5): +# avg.append(interval(true[x],predict[x])) +# average=sum(avg) / len(avg) +# print("AVG1:",average) +# print("AVG2:",threshold) + + temp_predict=predict[-test_size:] + temp_true=true[-test_size:] + +# avg2=interval(max(predict),min(predict)) + count=0 + print("------------------------------------") + for x in range(test_size): + # acc_var1 = temp_true[x]-(1/filterx*temp_true[x]) + acc_var1 = temp_true[x]-(filterx/10) + acc_var2 = temp_predict[x] + # acc_var3 = temp_true[x]+(1/filterx*temp_true[x]) + acc_var3 = temp_true[x]+(filterx/10) + acc_condition = acc_var1 <= acc_var2 <= acc_var3 + # print("Var 1 : ",acc_var1) + # print("Var 2 : ",acc_var2) + # print("Var 3 : ",acc_var3) + # print("Day "+str(x+1)+" "+str(int(acc_var1))+" "+str(int(acc_var2))+" "+str(int(acc_var3))+" : ",acc_condition) + print("Day "+str(x+1)+", Price : "+str(int(temp_true[x]))+" ,Gap = "+str(int(abs(temp_predict[x]-temp_true[x])))+" : ",acc_condition) + if (acc_condition): + count=count+1 + print("------------------------------------") + if count>7: + print("Result True") + return True + else: + print("Result False") + return False + +# if average>threshold: +# return False +# else: +# return True + + +# In[46]: + + +def findthreshold(simulationsize,dfx,dftrain,df,df2,minmax): + results=[] + for i in range(simulationsize): + results.append(forecast(df,dftrain,dfx,minmax)) + accepted_results = [] + + for r in results: + + if (np.array(r[-test_size:]) < np.min(dfx['Close'])).sum() == 0 and (np.array(r[-test_size:]) > np.max(dfx['Close']) * 2).sum() == 0: + accepted_results.append(r) + + finalavg=999999 + for o in accepted_results: + avg=[] + for x in range(len(o)-5): + avg.append(interval(o[x],df2[x])) + average=sum(avg) / len(avg) + if average<=finalavg: + finalavg=average + + return finalavg + +def temp_data(date, xi, resultfinal, df2, date_col,x): + print("Called . . . ") + if os.path.isdir("TempData/") == False: + os.mkdir("TempData/") + if os.path.isdir("TempData/%s"%x) == False: + os.mkdir("TempData/%s"%x) + if os.path.isdir("TempData/%s/"%x+str(date)) == False: + os.mkdir("TempData/%s/"%x+str(date)) + with open("TempData/%s/"%x+str(date)+"/"+x+str(xi)+".vezpal2","w+") as oop: + main=[] + main.append(resultfinal) # prediction + main.append(list(df2['Close'])) + main.append(date_col) +# main.append(3) +# main.append([0]) + json.dump(main,oop) + +def automaton(simulationsize,date): + + # symbols=["AAPL"] + symbols = sys.argv[1] + times=[] + x=symbols + # for x in symbols: + temp_time=[] + temp_time.append(x) + counter=0 + validity=0 + df,date_col=loader(x,test_size,date) + # print(type(df)) + dfx=df + # print("ASDSAD") + df2=trueloader(x,test_size,date) + df,minmax=preproc(df) + dftrain=df + wrong=[1,2,3,4,5] + # avg=checkaccuracy2(list(df2["Close"])) + # start=time.time() + # avg=findthreshold(50,dfx,dftrain,df,list(df2["Close"]),minmax) + # temp_time.append(time.time()-start) + start=time.time() + filterx = int(prediction_gap) + able=False + print("============== || Initial Train || =============") + main_train(df,dftrain,dfx,minmax) + for xi in range(5): + decision=False + while (decision==False): + print() + print("====== [ Foreacasting Attempt : "+str(counter+1)+" ] ===========") + print("====== [ Progress : "+str(xi)+"/5 ] ") + resultfinal=betaforecast(simulationsize,dfx,dftrain,df,df2,minmax) + # validity=valid + decision=checkaccuracy(resultfinal,list(df2["Close"]),filterx, test_size) + # wrong=invalid + if decision==True: + able=True + print("ABLE") + print(str(filterx)) + if counter > 10 and decision != True: + counter = 0 + filterx=filterx+10 + print("Filter X new value : "+str(filterx)) + print("Decision Status : ", decision) + print("**************************************") +# avg=avg+(1/3*avg) + if filterx>1000: + print("====== [ GG, we gave up] =====") + continue + counter=counter+1 + temp_data(date, xi, resultfinal, df2, date_col, x) + print("[ Loop : "+x+" done ] =========================") + print() + + if os.path.isdir("Backtest/") == False: + os.mkdir("Backtest/") + if os.path.isdir("Backtest/%s"%x) == False: + os.mkdir("Backtest/%s"%x) + if os.path.isdir("Backtest/%s/"%x+str(date)) == False: + os.mkdir("Backtest/%s/"%x+str(date)) + with open("Backtest/%s/"%x+str(date)+"/"+x+str(xi)+".vezpal2","w+") as oop: + main=[] + main.append(resultfinal) #prediction + main.append(list(df2['Close'])) + main.append(date_col) +# main.append(3) +# main.append([0]) + json.dump(main,oop) + print("Time for %s :"%x,time.time()-start) + temp_time.append(time.time()-start) + times.append(temp_time) + return times + +def predictor(simulationsize,current): + for x in range(52): + tf.compat.v1.reset_default_graph() + current+=timedelta(days=7) + automaton(simulationsize,current) + +current_date=date(2020,1,1) +if os.path.isdir("Loss/") == False: + os.mkdir("Loss/") +if os.path.isdir("Loss/"+str(current_date)) == False: + os.mkdir("Loss/"+str(current_date)) + +# loss_file = time.strftime("%Y%m%d-%H%M%S") +# loss_file = "Loss/"+str(date.today())+"/"+loss_file +global_start = time.time() +# profile = cProfile.Profile() + +# main_func = "predictor(simulation_size,current_date)" +predictor(simulation_size,current_date) +# ps = pstats.Stats(profile.run(main_func)) +print("Overall time consumption ", str(time.time()-global_start)) +# ps.dump_stats("./Cprofile_model_01.ps") + diff --git a/runner.sh b/runner.sh new file mode 100755 index 0000000..bbb76ff --- /dev/null +++ b/runner.sh @@ -0,0 +1,5 @@ +#!/bin/bash + +python3 ./main_model.py PNLF.JK & +sleep 10 && +python3 ./plot_loss.py & diff --git a/screener.py b/screener.py new file mode 100644 index 0000000..6478b90 --- /dev/null +++ b/screener.py @@ -0,0 +1,176 @@ +from pandas_datareader import data as pdr +import json +from yahoo_fin import stock_info as si +from pandas import ExcelWriter +import numpy as np +import matplotlib.pyplot as plt +import yfinance as yf +import pandas as pd +import requests +import datetime +import time +import sys + + +def roc11(closes): + print(len(closes)) + roc11_val = [] + roc14_val = [] + roc_sum = [] + x = len(closes) + for i in range(x): + if i+11 == x: # Fixed + break + # cur_indx = x - i + # temp_11 = (closes[cur_indx] - closes[cur_indx-11])/closes[cur_indx-11]*100 + temp_11 = (closes[i+11] - closes[i])/closes[i]*100 + roc11_val.append(temp_11) + + for i in range(x): + if i+14 == x: # Fixed + break + temp_14 = (closes[i+14] - closes[i])/closes[i]*100 + roc14_val.append(temp_14) + + for i in range(len(roc14_val)): + roc_sum.append(roc11_val[i+3]+roc14_val[i]) + print("Finished ") + return roc_sum + +def wma10(roc_sum, n=10): + roc_sum = pd.DataFrame(roc_sum, columns=['COPPOCK']) + weights = np.arange(1, n+1) + wmas = roc_sum.rolling(n).apply(lambda x:np.dot(x, weights)/weights.sum(), raw=True) + + print(wmas) + return wmas + + +yf.pdr_override() + +# stocklist = si.tickers_sp500() +stock_file = open("./symbols_backup.txt", "r") +stocklist = stock_file.readlines() +stock_file.close() +index_name = '^GSPC' # S&P 500 + +final = [] +index = [] +n = -1 +f = open("./Samples/SHOP/2020-01-22/SHOP0.vezpal2") +ticker = sys.argv[1] +path='./Samples/'+ticker+'/2021-01-30/'+ticker+'0.vezpal2' +path = open(path) +a = json.load(path) + +pred_stock = a[0] + + + +n += 1 +time.sleep(1) + + +# RS_Rating +start_date = datetime.datetime.now() - datetime.timedelta(days=365) +end_date = datetime.date.today() +# +# df = pdr.get_data_yahoo(stock, start=start_date, end=end_date) +# df['Percent Change'] = df['Adj Close'].pct_change() +# stock_return = df['Percent Change'].sum() * 100 +# +# index_df = pdr.get_data_yahoo(index_name, start=start_date, end=end_date) +# index_df['Percent Change'] = index_df['Adj Close'].pct_change() +# index_return = index_df['Percent Change'].sum() * 100 +# +# RS_Rating = round((stock_return / index_return) * 10, 2) +# +# sma = [50, 150, 200] +# for x in sma: +# df["SMA_"+str(x)] = round(df.iloc[:,4].rolling(window=x).mean(), 2) + +# currentClose = df["Adj Close"][-1] +# moving_average_50 = df["SMA_50"][-1] +# moving_average_150 = df["SMA_150"][-1] +# moving_average_200 = df["SMA_200"][-1] +# low_of_52week = min(df["Adj Close"][-260:]) +# high_of_52week = max(df["Adj Close"][-260:]) +# closePrice = df["Close"] +closePrice = pred_stock +# print(closePrice.head()) + +roc_res = roc11(closePrice) +wma_res = wma10(roc_res) +print("==========") +print(len(wma_res)); +print(len(closePrice)); +print(closePrice.index) +entry_buy = [] +entry_sell= [] +entry_date = [] + +# for i in range(len(wma_res)): +# # mark_1 = sum(wma_res["COPPOCK"][i:i+3]) +# if ( i+6 < len(wma_res)): +# mark_1 = wma_res["COPPOCK"][i] +# mark_2 = wma_res["COPPOCK"][i+3] +# mark_3 = wma_res["COPPOCK"][i+6] +# if (mark_2 < mark_1 and mark_2 < mark_3): +# # entry_date.append(closePrice.index[-237:][i]) +# entry_buy.append(i) +# print("Down ",i) +# # print(entry_date) +# +# if (mark_2 > mark_1 and mark_2 > mark_3): +# # entry_date.append(closePrice.index[-237:][i]) +# entry_sell.append(i) +# print("Up ",i) +# # print(entry_date) + +# i = 0 +for i in range(len(wma_res)): + # mark_1 = sum(wma_res["COPPOCK"][i:i+3]) + if ( i+15 < len(wma_res)): + mark_1 = wma_res["COPPOCK"][i] + mark_2 = wma_res["COPPOCK"][i+8] + mark_3 = wma_res["COPPOCK"][i+15] + + mark_h = wma_res["COPPOCK"][i+10] + + if ((mark_2*3 < mark_1) and ( mark_2*3 < mark_3 ) and ( mark_2 > mark_h )): + # entry_date.append(closePrice.index[-237:][i]) + entry_buy.append(i+8) + print("Down ",i," ",mark_2) + # print(entry_date) + + if (mark_2 > mark_1 and mark_2 > mark_3 and mark_2 < mark_h): + # entry_date.append(closePrice.index[-237:][i]) + entry_sell.append(i+8) + print("Up ",i," ",mark_2) + # print(entry_date) + # i = i + 7 + # print(i) + +# wma_res['Date'] = closePrice.index[-237:] +ax1 = plt.subplot(211) +plt.title("COPPOCK Indicator "+str(ticker)) +# for i in entry_points: +# plt.vlines(x=closePrice[-237:][i], ymin=0, ymax=max(closePrice)) +plt.plot(closePrice, markevery=entry_buy, marker="^", ms=4, mfc="y", linewidth=1) +plt.plot(closePrice, markevery=entry_sell, marker="v", ms=4, mfc="r", linewidth=1) + +ax2 = plt.subplot(212, sharex=ax1) +# plt.axhline(y=0, color='r') +# plt.plot(closePrice[-len(wma_res):], wma_res, markevery=entry_sell, marker="o") +plt.plot(range(len(wma_res)), wma_res, markevery=entry_sell, marker="o", mfc='r' ) +plt.plot(range(len(wma_res)), wma_res, markevery=entry_buy, marker="+", mfc='g', linewidth=0.1) +plt.show() +plt.savefig("./screening_result/pred.png") +# plt.savefig("./screening_result/"+str(stock)+".png", dpi=1200) +plt.close() + + + +# writer = ExcelWriter("ScreenOutput.xlsx") +# exportList.to_excel(writer, "Sheet1") +# writer.save() diff --git a/screener.py.pdf b/screener.py.pdf new file mode 100644 index 0000000000000000000000000000000000000000..c33c11020aa4c1d4c0a3eb0946f6a35f090809bd GIT binary patch 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a/screener_2.py b/screener_2.py new file mode 100644 index 0000000..368145e --- /dev/null +++ b/screener_2.py @@ -0,0 +1,198 @@ +from pandas_datareader import data as pdr +import json +from yahoo_fin import stock_info as si +from pandas import ExcelWriter +import numpy as np +import matplotlib.pyplot as plt +import yfinance as yf +import pandas as pd +import requests +import datetime +import time +import sys +import glob +import os + + +def roc11(closes): + print(len(closes)) + roc11_val = [] + roc14_val = [] + roc_sum = [] + x = len(closes) + for i in range(x): + if i+11 == x: # Fixed + break + # cur_indx = x - i + # temp_11 = (closes[cur_indx] - closes[cur_indx-11])/closes[cur_indx-11]*100 + temp_11 = (closes[i+11] - closes[i])/closes[i]*100 + roc11_val.append(temp_11) + + for i in range(x): + if i+14 == x: # Fixed + break + temp_14 = (closes[i+14] - closes[i])/closes[i]*100 + roc14_val.append(temp_14) + + for i in range(len(roc14_val)): + roc_sum.append(roc11_val[i+3]+roc14_val[i]) + print("Finished ") + return roc_sum + +def wma10(roc_sum, n=10): + roc_sum = pd.DataFrame(roc_sum, columns=['COPPOCK']) + weights = np.arange(1, n+1) + wmas = roc_sum.rolling(n).apply(lambda x:np.dot(x, weights)/weights.sum(), raw=True) + + print(wmas) + return wmas + + +yf.pdr_override() + +# stocklist = si.tickers_sp500() +# stock_file = open("./symbols_backup.txt", "r") +# stocklist = stock_file.readlines() +# stock_file.close() +# index_name = '^GSPC' # S&P 500 + +final = [] +index = [] +n = -1 + +def screener(ticker): + # ticker = sys.argv[1] + path='./Samples/'+ticker+'/' + all_dirs = glob.glob(path+"*") + print(path) + print(all_dirs) + latest_dir = max(all_dirs, key=os.path.getmtime) + print("Latest Data = ", latest_dir) + latest_dir = latest_dir + "/" + listsymbol = os.listdir(latest_dir+'/') + path = open(latest_dir+listsymbol[0]) + a = json.load(path) + + pred_stock = a[0] + real_pred = a[1] + + # RS_Rating + start_date = datetime.datetime.now() - datetime.timedelta(days=(365*3)+13) + end_date = datetime.date.today() + # + df = pdr.get_data_yahoo(ticker, start=start_date, end=end_date) + real_stock = df["Close"].values + # real_stock = real_stock.reset_index(drop=True, inplace=True) + # real_stock = df["Close"].drop("Date") + print(real_stock) + print("************") + closePrice = pred_stock + # print(closePrice.head()) + + roc_res = roc11(closePrice) + wma_res = wma10(roc_res) + print("==========") + print(len(wma_res)); + print(len(closePrice)); + print(closePrice.index) + entry_buy = [] + entry_sell= [] + entry_date = [] + + + # i = 0 + entry_buy = [] + + val = wma_res["COPPOCK"] + y = 0 + while (y z and x < val[y+4]: + entry_buy.append(y+1) + + y = y+1 + + y = 0 + while (y z: + x = z + if x < z and x > val[y+1]: + entry_sell.append(y) + y = y+1 + + mark_zero = [] + for i in range(len(wma_res)-1): + mark_1 = wma_res["COPPOCK"][i] + mark_2 = wma_res["COPPOCK"][i+1] + if mark_1 > 0 and mark_2 < 0: + mark_zero.append(i+1) + + mark_zero_sell = [] + for i in range(len(wma_res)-20): + mark_1 = wma_res["COPPOCK"][i] + mark_2 = wma_res["COPPOCK"][i+1] + if mark_1 < 0 and mark_2 > 0: + mark_zero_sell.append(i+1) + + print(entry_buy[-10:]) + for i in range(len(entry_buy)): + entry_buy[i] = entry_buy[i]+21 + + for i in range(len(mark_zero)): + mark_zero[i] = mark_zero[i]+21 + + for i in range(len(mark_zero_sell)): + mark_zero_sell[i] = mark_zero_sell[i]+21 + + for i in entry_sell: + i = i+14 + + print(entry_buy[-10:]) + + # ax1 = plt.subplot(211) + # plt.title("COPPOCK Indicator "+str(ticker)) + # plt.plot(real_stock[2:], linewidth=2, label="real", linestyle="--") + # plt.plot(real_pred, linewidth=2, label="real", linestyle="--") + # plt.plot(closePrice, linewidth=0.2, label="Prediction") + # plt.legend() + # + # ax2 = plt.subplot(212, sharex=ax1) + + fill_front = [] + for i in range(21): + fill_front.append(np.nan) + fill_front.extend(wma_res["COPPOCK"]) + + wma_res = fill_front + + # plt.plot(range(len(wma_res)), wma_res, markevery=mark_zero_sell, marker="x", mfc='c', linewidth=0.8) + # plt.plot(range(len(wma_res)), wma_res, markevery=mark_zero, marker="x", mfc='c', linewidth=0.8) + # plt.plot(range(len(wma_res)), np.zeros(len(wma_res)), linewidth=0.8) + # + # plt.show() + # plt.savefig("./screening_result/pred.png") + # plt.close() + indictaor = [] + indictaor.append(wma_res) + indictaor.append(np.zeros(len(wma_res))) + markers = [] + markers.append(mark_zero) + markers.append(mark_zero_sell) + stocks = [] + stocks.append(real_pred) + stocks.append(closePrice) + return indictaor, markers, stocks + +# screener("MA") + + +# writer = ExcelWriter("ScreenOutput.xlsx") +# exportList.to_excel(writer, "Sheet1") +# writer.save() diff --git a/stocks.csv b/stocks.csv new file mode 100644 index 0000000..967a3d0 --- /dev/null +++ b/stocks.csv @@ -0,0 +1,3 @@ +,Company,Index +0,"SOXL +",27 diff --git a/symbols.txt b/symbols.txt new file mode 100644 index 0000000..c9cf249 --- /dev/null +++ b/symbols.txt @@ -0,0 +1,11 @@ +AAPL +SHOP +MELI +GOOGL +FB +NEE +CMCSA +MA +TSLA +AMD +XAR diff --git a/symbols_backup.txt b/symbols_backup.txt new file mode 100644 index 0000000..a3b779c --- /dev/null +++ b/symbols_backup.txt @@ -0,0 +1,39 @@ +AAPL +ADBE +ADP +AMT +APD +AWK +AXP +BLL +BR +CCEP +CHTR +CMCSA +CSGP +FICO +GPN +GOOGL +HSY +LBRDK +MA +MAA +MSFT +NEE +NOC +PG +PLNT +PYPL +SNPS +SOXL +SYY +VRSN +WEC +SHOP +KEYS +DG +HON +ACN +TMO +PAYC +IHI diff --git a/temp_plotter.py b/temp_plotter.py new file mode 100644 index 0000000..2948473 --- /dev/null +++ b/temp_plotter.py @@ -0,0 +1,127 @@ +import matplotlib.pyplot as plt +import json +import os +from datetime import datetime, date, timedelta +from matplotlib.dates import date2num +import yfinance as yf +import sys +import glob +import numpy as np +# path='../Febrian/Bang Nino/Samples/NEE/2020-05-08/' + +my_args = sys.argv[1:] +tick = my_args[0] +filter=int(my_args[1]) +visual=100 +print(tick) +# path='./Samples/'+tick+'/2020-12-07/' +path='./TempData/'+tick+'/' +all_dirs = glob.glob(path+"*") +print(path) +print(all_dirs) +latest_dir = max(all_dirs, key=os.path.getctime) +print("Latest Data = ", latest_dir) +latest_dir = latest_dir + "/" +listsymbol = os.listdir(latest_dir+'/') +print(listsymbol) + +with open(latest_dir+listsymbol[0],'r') as f: + print(latest_dir+listsymbol[0]) + hasil=json.load(f) + # print(hasil) + print(len(hasil[0])) + print(len(hasil[1])) + print(len(hasil[2])) + akhir=datetime.strptime(str(hasil[2][-1]),'%Y-%m-%d').date() + print("Current Log : ",akhir) + extend1=[akhir+timedelta(days=x) for x in range(1,50,1)] + extend=[] + + for x in extend1: + if x.weekday() not in [5,6]: + extend.append(x) + + extend=extend[:14] + extend=[str(x) for x in extend] + print("ASasdasdsa",extend) + # date=date2num(date) + date=hasil[2] + plt.plot(hasil[1],color='grey',linewidth=3, linestyle='--', marker='x', alpha=0.8, label="Confirmation") + plt.plot(hasil[2],hasil[1][0:-5],color='b',linewidth=2) + # plt.show() + +symbol=yf.Ticker(tick) +symbol=symbol.history(start=akhir,end=akhir+timedelta(days=30),interval='1d') +# print(symbol) +symbol=symbol.drop(symbol.index[0]) +symbol=symbol.drop(symbol.index[0]) +symbol=symbol['Close'][0:14].tolist() +# plt.plot(extend,symbol,color='g',linewidth=10) +avg=[] +for ex,x in enumerate(listsymbol): + with open(latest_dir+x) as f: + print(x) + hasil=json.load(f) + a=hasil[0][-10:][:14] + # a = hasil[0][-14:] + b=hasil[1][-14:] + print("A & B Temp :") + print(a) + print(b) + count=0 + for x in range(7): + if (b[x]-(filter/10) <= a[x] <= b[x]+(filter/10)): + count=count+1 + if count>5: + print("HORE") + avg.append(hasil[0][-14:]) + # plt.plot(date+extend,hasil[0], label='Sample %s'%ex, alpha=0.3) +# print(avg) +print("#################################################") +print(avg) +print(" ") +print("#################################################") +avg_total=[] +for x in range(14): + temp=[] + for a in range(len(avg)): + temp.append(avg[a][x]) + # avg_total.append(sum(temp)/len(temp)) + avg_total.append(np.mean(temp)) + +# print(avg_total) + +atas=[x+(1/visual*x) for x in avg_total] +bawah=[x-(1/visual*x) for x in avg_total] +print([hasil[1][0:-5][-1]]+bawah) +print([hasil[1][0:-5][-1]]+atas) +print([date[-1]]+extend) +plt.fill_between([date[-1]]+extend,[hasil[1][0:-5][-1]]+bawah,[hasil[1][0:-5][-1]]+atas,alpha=0.2,label='Prediction Band') +plt.plot([date[-1]]+extend,[hasil[1][-5]]+avg_total,color='y',linewidth=1,label='Prediction', marker='x') +plt.grid() +# plt.show() + +# print("Date Extended ",(date+extend)) +symbol = yf.Ticker(tick) +symbol = symbol.history(start=akhir,end=akhir+timedelta(days=20),interval='1d') +print(symbol) +symbol = symbol.drop(symbol.index[0]) +symbol = symbol.drop(symbol.index[0]) +symbol = symbol['Close'][0:14].tolist() +# symbol = symbol['Close'].tolist() +# print(symbol) +# plt.plot(extend[0:len(symbol)],symbol,color='g',label='Actual',linewidth=1) +# plt.plot(hasil[1],color='r', linestyle='--', label='Confirmation',linewidth=2) +plt.plot(hasil[2]+extend[0:14],hasil[0],color='b',label='Train',linewidth=2, alpha=0.4) +# plt.plot(symbol, label="Symbol Real") +# plt.plot(hasil[0], label="Real Predictioh") +# print(symbol) +# print(symbol) +# detail = str(akhir)+"\n"+"Prediction :"+str(avg_total[-1:])+"\n"+"Real : " +# plt.text(0.05, 120, detail, color='black', bbox=dict(facecolor='none', edgecolor='black', boxstyle='round,pad=1')) +plt.legend(loc='upper center', bbox_to_anchor=(0.5, -0.01), + fancybox=True, shadow=True, ncol=7) +plt.title(tick+" Date: "+str(hasil[2][-1])+" to "+str(extend[-1])) +# plt.get_xaxis().set_ticks([]) +plt.xticks([]) +plt.show() diff --git a/temp_pred.png b/temp_pred.png new file mode 100644 index 0000000000000000000000000000000000000000..73bc0f43169bfcaa6d7ebf29de56f46267a39e00 GIT binary patch literal 47048 zcmd>mcRZGT`1XxZ5fvF(MOI`L%FeDt_8ytp*?UEZj8aKvD7uC0JtAapw=H|z_TJv} zPS5ZC>wW)u|9n5shv#{?UE_P5-}5}q<2cUi)f0J{3+Jz%M<5Uv9>_{6ArRQU2n5!j 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