251 lines
11 KiB
Python
251 lines
11 KiB
Python
import pandas as pd
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from datetime import datetime
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from ta.utils import *
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from ta.volatility import *
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from ta.momentum import *
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from ta.trend import *
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from ta.volume import *
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class rating_model:
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def __init__(self, df):
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#Results are in the form of
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# Strong Sell => 0
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# Sell => 1
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# Neutral => 2
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# Buy => 3
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# Strong Buy => 4
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self.data = df
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def compute_overall_signal(self, data):
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ratingMap = {
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'Strong Sell': 0,
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'Sell': 1,
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'Neutral': 2,
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'Buy': 3,
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'Strong Buy': 4
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}
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# Extract overall ratings from the data
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overallRating = {item['name']: item['signal'] for item in data}
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# Compute mean overall rating
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mean_overall_rating = sum(ratingMap[val] for val in overallRating.values()) / len(overallRating)
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mean_overall_rating /= 4.0
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# Determine overall signal based on mean rating
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if 0 < mean_overall_rating <= 0.15:
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overall_signal = "Strong Sell"
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elif 0.15 < mean_overall_rating <= 0.45:
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overall_signal = "Sell"
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elif 0.45 < mean_overall_rating <= 0.55:
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overall_signal = 'Neutral'
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elif 0.55 < mean_overall_rating <= 0.8:
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overall_signal = 'Buy'
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elif 0.8 < mean_overall_rating <= 1.0:
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overall_signal = "Strong Buy"
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else:
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overall_signal = 'n/a'
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return overall_signal
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def ta_rating(self):
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df = pd.DataFrame()
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df['sma_20'] = sma_indicator(self.data['close'], window=20)
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df['sma_50'] = sma_indicator(self.data['close'], window=50)
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df['ema_20'] = ema_indicator(self.data['close'], window=20)
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df['ema_50'] = ema_indicator(self.data['close'], window=50)
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df['wma'] = wma_indicator(self.data['close'], window=20)
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df['adx'] = adx(self.data['high'],self.data['low'],self.data['close'])
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df["adx_pos"] = adx_pos(self.data['high'],self.data['low'],self.data['close'])
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df["adx_neg"] = adx_neg(self.data['high'],self.data['low'],self.data['close'])
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df['williams'] = WilliamsRIndicator(high=self.data['high'], low=self.data['low'], close=self.data['close']).williams_r()
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# Assign ratings based on SMA values
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df['sma_rating'] = 'Neutral'
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if self.data['close'].iloc[-1] < df['sma_50'].iloc[-1] and df['sma_20'].iloc[-1] < df['sma_50'].iloc[-1]:
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df['sma_rating'] = 'Strong Sell'
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elif self.data['close'].iloc[-1] < df['sma_20'].iloc[-1] and df['sma_20'].iloc[-1] < df['sma_50'].iloc[-1]:
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df['sma_rating'] = 'Sell'
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elif df['sma_20'].iloc[-1] <= self.data['close'].iloc[-1] <= df['sma_50'].iloc[-1]:
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df['sma_rating'] = 'Neutral'
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elif self.data['close'].iloc[-1] > df['sma_20'].iloc[-1] and df['sma_20'].iloc[-1] > df['sma_50'].iloc[-1]:
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df['sma_rating'] = 'Buy'
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elif self.data['close'].iloc[-1] > df['sma_50'].iloc[-1] and df['sma_20'].iloc[-1] > df['sma_50'].iloc[-1]:
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df['sma_rating'] = 'Strong Buy'
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# Assign ratings for ema
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df['ema_rating'] = 'Neutral'
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if self.data['close'].iloc[-1] < df['ema_50'].iloc[-1] and df['ema_20'].iloc[-1] < df['ema_50'].iloc[-1]:
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df['ema_rating'] = 'Strong Sell'
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elif self.data['close'].iloc[-1] < df['ema_20'].iloc[-1] and df['ema_20'].iloc[-1] < df['ema_50'].iloc[-1]:
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df['ema_rating'] = 'Sell'
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elif df['ema_20'].iloc[-1] <= self.data['close'].iloc[-1] <= df['ema_50'].iloc[-1]:
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df['ema_rating'] = 'Neutral'
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elif self.data['close'].iloc[-1] > df['ema_20'].iloc[-1] and df['ema_20'].iloc[-1] > df['ema_50'].iloc[-1]:
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df['ema_rating'] = 'Buy'
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elif self.data['close'].iloc[-1] > df['ema_50'].iloc[-1] and df['ema_20'].iloc[-1] > df['ema_50'].iloc[-1]:
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df['ema_rating'] = 'Strong Buy'
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# Assign ratings based on wma
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df['wma_rating'] = pd.cut(self.data['close'] - df['wma'],
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bins=[float('-inf'), -10, -5, 0, 5, 10],
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labels=['Strong Sell', 'Sell', 'Neutral', 'Buy', 'Strong Buy'])
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# Assign ratings based on adx
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if df['adx'].iloc[-1] > 50 and df['adx_neg'].iloc[-1] > df['adx_pos'].iloc[-1]:
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df['adx_rating'] = 'Strong Sell'
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elif df['adx'].iloc[-1] >=25 and df['adx'].iloc[-1] <=50 and df['adx_neg'].iloc[1] > df['adx_pos'].iloc[-1]:
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df['adx_rating'] = 'Sell'
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elif df['adx'].iloc[-1] < 25:
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df['adx_rating'] = 'Neutral'
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elif df['adx'].iloc[-1] >=25 and df['adx'].iloc[-1] <=50 and df['adx_pos'].iloc[-1] > df['adx_neg'].iloc[-1]:
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df['adx_rating'] = 'Buy'
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elif df['adx'].iloc[-1] > 50 and df['adx_pos'].iloc[-1] > df['adx_neg'].iloc[-1]:
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df['adx_rating'] = 'Strong Buy'
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else:
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df['adx_rating'] = 'Neutral'
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# Assign ratings based on williams
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df['williams_rating'] = 'Neutral'
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df.loc[df["williams"] < -80, 'williams_rating'] = "Strong Sell"
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df.loc[(df["williams"] >= -80) & (df["williams"] < -50), 'williams_rating'] = "Sell"
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df.loc[(df["williams"] >= -50) & (df["williams"] <= -20), 'williams_rating'] = "Buy"
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df.loc[df["williams"] > -20, 'williams_rating'] = "Strong Buy"
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#=========Momentum Indicators ============#
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aroon = AroonIndicator(self.data['close'], low=self.data['low'], window=14)
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df['rsi'] = rsi(self.data['close'], window=14)
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df['stoch_rsi'] = stochrsi_k(self.data['close'], window=14, smooth1 = 3, smooth2 =3)*100
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df['macd'] = macd(self.data['close'])
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df['macd_signal'] = macd_signal(self.data['close'])
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df['macd_hist'] = 2*macd_diff(self.data['close'])
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df['roc'] = roc(self.data['close'], window=14)
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df['cci'] = CCIIndicator(high=self.data['high'], low=self.data['low'], close=self.data['close']).cci()
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df['mfi'] = MFIIndicator(high=self.data['high'], low=self.data['low'], close=self.data['close'], volume=self.data['volume']).money_flow_index()
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# Assign ratings based on MFI values
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df['mfi_rating'] = pd.cut(df['mfi'],
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bins=[-1, 20, 40, 60, 80, 101],
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labels=['Strong Buy', 'Buy', 'Neutral', 'Sell', 'Strong Sell'])
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# Assign ratings based on RSI values
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df['rsi_rating'] = pd.cut(df['rsi'],
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bins=[-1, 30, 50, 60, 70, 101],
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labels=['Strong Buy', 'Buy', 'Neutral', 'Sell', 'Strong Sell'])
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# Assign ratings based on Stoch RSI values
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df['stoch_rsi_rating'] = pd.cut(df['stoch_rsi'],
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bins=[-1, 30, 50, 60, 70, 101],
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labels=['Strong Buy', 'Buy', 'Neutral', 'Sell', 'Strong Sell'])
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# Assign ratings for macd
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if df['macd'].iloc[-1] < df['macd_signal'].iloc[-1] and df['macd_hist'].iloc[-1] < 0 \
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and df['macd_hist'].iloc[-1] > df['macd_hist'].iloc[-2]:
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df['macd_rating'] = 'Strong Sell'
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elif df['macd'].iloc[-1] < df['macd_signal'].iloc[-1] and df['macd_hist'].iloc[-1] < 0 \
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and df['macd_hist'].iloc[-1] < df['macd_hist'].iloc[-2]:
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df['macd_rating'] = 'Sell'
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elif abs(df['macd'].iloc[-1] - df['macd_signal'].iloc[-1]) < 0.01 and abs(df['macd_hist'].iloc[-1]) < 0.01:
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df['macd_rating'] = 'Neutral'
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elif df['macd'].iloc[-1] > df['macd_signal'].iloc[-1] and df['macd_hist'].iloc[-1] > 0 and df['macd_hist'].iloc[-1] < df['macd_hist'].iloc[-2]:
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df['macd_rating'] = 'Buy'
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elif df['macd'].iloc[-1] > df['macd_signal'].iloc[-1] and df['macd_hist'].iloc[-1] > 0 and df['macd_hist'].iloc[-1] > df['macd_hist'].iloc[-2]:
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df['macd_rating'] = 'Strong Buy'
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else:
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df['macd_rating'] = 'Neutral'
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# Assign ratings for roc
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if df['roc'].iloc[-1] < -10:
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df['roc_rating'] = 'Strong Sell'
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elif df['roc'].iloc[-1] > -10 and df['roc'].iloc[-1] <= -5:
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df['roc_rating'] = 'Sell'
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elif df['roc'].iloc[-1] > -5 and df['roc'].iloc[-1] < 5:
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df['roc_rating'] = 'Neutral'
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elif df['roc'].iloc[-1] >=5 and df['roc'].iloc[-1] < 10:
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df['roc_rating'] = 'Buy'
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elif df['roc'].iloc[-1] >= 10:
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df['roc_rating'] = 'Strong Buy'
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else:
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df['roc_rating'] = 'Neutral'
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# Define CCI threshold values for signals
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cci_strong_sell_threshold = -100
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cci_sell_threshold = -50
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cci_buy_threshold = 50
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cci_strong_buy_threshold = 100
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# Assign signals based on CCI values
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if df['cci'].iloc[-1] < cci_strong_sell_threshold:
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df['cci_rating'] = 'Strong Sell'
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elif cci_strong_sell_threshold <= df['cci'].iloc[-1] < cci_sell_threshold:
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df['cci_rating'] = 'Sell'
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elif cci_sell_threshold <= df['cci'].iloc[-1] < cci_buy_threshold:
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df['cci_rating'] = 'Neutral'
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elif cci_buy_threshold <= df['cci'].iloc[-1] < cci_strong_buy_threshold:
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df['cci_rating'] = 'Buy'
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else:
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df['cci_rating'] = 'Strong Buy'
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res_list = [
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{'name': 'Relative Strength Index (14)', 'value': round(df['rsi'].iloc[-1],2), 'signal': df['rsi_rating'].iloc[-1]},
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{'name': 'Stochastic RSI Fast (3,3,14,14)', 'value': round(df['stoch_rsi'].iloc[-1],2), 'signal': df['stoch_rsi_rating'].iloc[-1]},
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{'name': 'Money Flow Index (14)', 'value': round(df['mfi'].iloc[-1],2), 'signal': df['mfi_rating'].iloc[-1]},
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{'name': 'Simple Moving Average (20)', 'value': round(df['sma_20'].iloc[-1],2), 'signal': df['sma_rating'].iloc[-1]},
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{'name': 'Exponential Moving Average (20)', 'value': round(df['ema_20'].iloc[-1],2), 'signal': df['ema_rating'].iloc[-1]},
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{'name': 'Weighted Moving Average (20)', 'value': round(df['wma'].iloc[-1],2), 'signal': df['wma_rating'].iloc[-1]},
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{'name': 'Average Directional Index (14)', 'value': round(df['adx'].iloc[-1],2), 'signal': df['adx_rating'].iloc[-1]},
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{'name': 'Commodity Channel Index (14)', 'value': round(df['cci'].iloc[-1],2), 'signal': df['cci_rating'].iloc[-1]},
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{'name': 'Rate of Change (12)', 'value': round(df['roc'].iloc[-1],2), 'signal': df['roc_rating'].iloc[-1]},
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{'name': 'Moving Average Convergence Divergence (12, 26)', 'value': round(df['macd'].iloc[-1],2), 'signal': df['macd_rating'].iloc[-1]},
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{'name': 'Williams %R (14)', 'value': round(df['williams'].iloc[-1],2), 'signal': df['williams_rating'].iloc[-1]}
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]
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overall_signal = self.compute_overall_signal(res_list)
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res_dict = {'overallSignal': overall_signal, 'signalList': res_list}
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return res_dict
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# Load the historical stock price data
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#Testing mode
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# Load the data
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'''
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import sqlite3
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start_date = "2015-01-01"
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end_date = datetime.today().strftime("%Y-%m-%d")
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con = sqlite3.connect('stocks.db')
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symbol = 'ZTS'
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query_template = """
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SELECT
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date, open, high, low, close, volume
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FROM
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"{symbol}"
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WHERE
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date BETWEEN ? AND ?
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"""
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query = query_template.format(symbol=symbol)
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df = pd.read_sql_query(query, con, params=(start_date, end_date))
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test = rating_model(df).ta_rating()
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print(test)
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con.close()
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''' |