From a29d10eedfa72aa1a98254c006908db84717241b Mon Sep 17 00:00:00 2001
From: Hedriss10 <hedrisgts@gmail.com>
Date: Sun, 5 Nov 2023 15:01:52 -0300
Subject: [PATCH] " indicator cmf "

---
 backend/src/cmf.py | 134 +++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 134 insertions(+)
 create mode 100644 backend/src/cmf.py

diff --git a/backend/src/cmf.py b/backend/src/cmf.py
new file mode 100644
index 0000000..000492f
--- /dev/null
+++ b/backend/src/cmf.py
@@ -0,0 +1,134 @@
+import os
+import warnings
+import pandas as pd
+import numpy as np
+import datetime
+import itertools
+
+from multiprocessing import Pool
+
+warnings.filterwarnings("ignore")
+
+# Define o número de processos a serem usados pelo multiprocessing
+num_processes = os.cpu_count()
+
+# Função para calcular o CMF de um DataFrame de preços
+def calculate_cmf(df, cmf_periods):
+    mfv = ((df['close'] - df['low']) - (df['high'] - df['close']))/(df['high'] - df['low'])
+    mfvm = mfv * df['Volume']
+    cmf = mfvm.rolling(cmf_periods).sum() / df['Volume'].rolling(cmf_periods).sum()
+    return cmf
+
+
+def calculate_moving_average_crossover(df, ma_short, ma_long, cmf_periods):
+    # Calcula o CMF
+    df['cmf'] = calculate_cmf(df, cmf_periods)
+
+    # Calcula as médias móveis do CMF
+    col_short = 'ma_{}'.format(ma_short)
+    col_long = 'ma_{}'.format(ma_long)
+    df[col_short] = df['cmf'].rolling(ma_short).mean()
+    df[col_long] = df['cmf'].rolling(ma_long).mean()
+
+    # Identifica os cruzamentos de médias móveis
+    df.loc[df[col_short] > df[col_long], 'cross'] = 1
+    df.loc[df[col_short] <= df[col_long], 'cross'] = 0
+    df['signal'] = df['cross'].diff()
+
+    total_signals = len(df['signal'])
+    total_correct_signals = sum(1 for s in df['signal'] if s > 0)
+
+    efficiency_gain = 0 if total_signals == 0 else total_correct_signals / total_signals
+
+    return efficiency_gain
+
+
+# Função para processar um arquivo CSV e retornar as informações de ganho de eficiência
+def process_csv_file(file_path, ma_short, ma_long, cmf_periods):
+    df = pd.read_csv(file_path)
+    efficiency_gain = calculate_moving_average_crossover(df, ma_short, ma_long, cmf_periods)
+    return round(efficiency_gain, 2)
+
+
+# Função paara remover arquivos desnecessários 
+def clean_ativo(row):
+    row =  row.replace("data", "")
+    row = row.replace(".csv", "")
+    row = row.replace("\\", "")
+
+    return str(row)
+
+# Função para formatar os dados de "efficiency gains"
+def clean_efficiency_gains(row):
+    row = str(row) 
+    row = row.replace("data", "")
+    row = row.replace("(", "")
+    row = row.replace(")", "")
+    row = row.replace("\\",'')
+    row = row.replace(".csv","")
+    stock_name , gain = row.split(",")    
+    gain =  gain.strip()
+
+    return str(gain), stock_name
+
+
+if __name__ == '__main__':
+    """__Análise__
+        define o short e long , periods ,
+    """
+    ma_short = 10
+    ma_long = 30
+    cmf_periods = 21
+
+    # Seleciona a data final
+    days = '2015-2-10'
+    
+    # Quantos dias você quer analisar  
+    num_days = 300
+        
+    # Lista todos os arquivos CSV na pasta "data"
+    csv_files = [os.path.join('data', f) for f in os.listdir('data') if f.endswith('.csv')]
+
+    with Pool(num_processes) as p:
+        # Cria uma lista vazia para armazenar os resultados da eficiência
+        efficiency_gains = []
+
+        for file_path in csv_files:
+            # Lê o arquivo CSV em um DataFrame
+            df = pd.read_csv(file_path)
+
+            # Filtra as datas que estão dentro do período especificado
+            df['time'] = pd.to_datetime(df['time'])
+            start_date = pd.Timestamp(days)
+            
+            df = df[(df['time'] >= start_date - pd.Timedelta(days=num_days))]
+
+
+            
+            # Calcula a eficiência para este arquivo específico
+            efficiency_gain = calculate_moving_average_crossover(df, ma_short, ma_long, cmf_periods)
+
+            # Adiciona a eficiência deste arquivo à lista de eficiências
+            efficiency_gains.append((file_path, efficiency_gain))
+
+        # Manipula os resultados
+        df_tmp = pd.DataFrame({ 
+                                'Ativo' : csv_files,
+                                'OSC' : "CMF", 
+                                'EFFICIENCY_GAINS': efficiency_gains,
+                                'ma_short':  ma_short,
+                                'ma_long': ma_long,
+                                'CMF_PERIODS': cmf_periods,
+                            })
+        df_tmp["Ativo"] = df_tmp["Ativo"].apply(clean_ativo)
+        stock_names = df_tmp["Ativo"].tolist()
+        df_tmp['EFFICIENCY_GAINS'] = df_tmp["EFFICIENCY_GAINS"].apply(clean_efficiency_gains)
+        df_tmp[["EFFICIENCY_GAINS", "Ativo"]] = pd.DataFrame(df_tmp.EFFICIENCY_GAINS.tolist(), index=df_tmp.index)
+        df_tmp['RANK'] = df_tmp['EFFICIENCY_GAINS'].rank(ascending=False, method='max').astype(int)
+        df_tmp['EFFICIENCY_GAINS'] = df_tmp['EFFICIENCY_GAINS'].apply(lambda x: round(float(x), 4))
+        df_tmp.sort_values('RANK', inplace=True)
+        df_tmp =  df_tmp.dropna()
+        for i, stk in enumerate(stock_names):
+            df_tmp.loc[i, 'Ativo'] = stk
+            
+        print(df_tmp.head(23))
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