newellp88
113 lines
4.1 KiB
Python
113 lines
4.1 KiB
Python
# short list of trading indicators
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import numpy as np
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import pandas as pd
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from sklearn import ensemble, tree
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from sklearn.model_selection import train_test_split
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def sma(series, window):
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mavg = series.rolling(window=window, min_periods=window).mean()
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return mavg
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# exponential weighted moving average
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def ewma(series, span):
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ema = pd.ewma(series, span=span)
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return ema
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def pivotPoints(df):
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n = len(df) - 1 # pivotPoints would be based on the last candle
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pp = (df['High'] + df['Low'] + df['Close']) / 3
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r1 = (2 * pp) - df['Low']
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s1 = (2 * pp) - df['High']
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r2 = (pp - s1) + r1
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s2 = pp - (r1 - s1)
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r3 = (pp - s2) + r2
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s3 = pp - (r2 - s2)
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pivots = {'PP': pp, 'R1': r1, 'R2': r2, 'R3': r3,
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'S1': s1, 'S2': s2, 'S3': s3}
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return pivots
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# rate of change, applied directly to the dataframe
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def roc(df):
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closes = df['Close'].apply(float)
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df['ROC'] = closes.diff() * 100
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df['ROC'].fillna(0)
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return df
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# stochastic oscillator, applied directly to the dataframe
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def stoch(df, period_K, period_D, graph=False):
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df['Low'] = pd.to_numeric(df['Low'], errors='coerce')
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df['Lstoch'] = df['Low'].rolling(window=period_K).min()
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df['High'] = pd.to_numeric(df['High'], errors='coerce')
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df['Hstoch'] = df['High'].rolling(window=period_K).max()
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df['Close'] = pd.to_numeric(df['Close'], errors='coerce')
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df['%K'] = 100*((df['Close'] - df['Lstoch']) / (df['Hstoch'] - df['Lstoch']))
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df['%D'] = df['%K'].rolling(window=period_D).mean()
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if graph == True:
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fig, axes = plt.subplots(nrows=2, ncols=1, figsize=(20,10))
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df['Close'].plot(ax=axes[0])#, title='Close'
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df[['K', 'D']].plot(ax=axes[1])#, title='Oscillator'
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plt.show()
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return df
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# bollinger bands, applied directly to the dataframe
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def bollBands(df, window, n_std, graph=False):
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close = df['Close']
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rolling_mean = close.rolling(window).mean()
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rolling_std = close.rolling(window).std()
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df['rolling_mean'] = rolling_mean
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df['boll_high'] = rolling_mean + (rolling_std * n_std)
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df['boll_low'] = rolling_mean - (rolling_std * n_std)
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if graph == True:
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plt.plot(close)
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plt.plot(df['Rolling Mean'])
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plt.plot(df['Bollinger High'])
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plt.plot(df['Bollinger Low'])
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plt.show()
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return df
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# AdaBoostRegressor with Decision Tree base, uses most of the standard df
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def AdaBoost(df):
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# clean the data
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n = len(df)
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X = np.asarray(df[['Open','High','Low','Volume']][:n-1]) # add or subtract input data columns here
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X = X.reshape(n-1, 4) # adjust '4' to match the number of input columns used above
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y = np.asarray(df[['Close']][1:])
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# build and score the model
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split = 0.8
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X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=split)
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dtree = tree.DecisionTreeRegressor(max_depth=1000)
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model = ensemble.AdaBoostRegressor(n_estimators=5000, learning_rate=2.0, base_estimator=dtree)
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model.fit(X_train, y_train)
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score = model.score(X_test, y_test)
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#print("Model1 accuracy: ", score)
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# make a prediction
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prediction = model.predict(df[['Open','High','Low','Volume']][n-1:])
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#print("AdaBoost predicted close for next candle: ", prediction)
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return prediction
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# RandomForestRegressor with Decision Tree base, uses most of the standard df
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def RandomForest(df):
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# clean the data
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n = len(df)
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X = np.asarray(df[['Open','High','Low','Volume']][:n-1]) # add or subtract input data columns here
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X = X.reshape(n-1, 4) # adjust '4' to match the number of input columns used above
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y = np.asarray(df[['Close']][1:])
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# build and score the model
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split = 0.8
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X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=split)
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model = ensemble.RandomForestRegressor(n_estimators=10000, max_depth=1000)
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model.fit(X_train, y_train)
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score = model.score(X_test, y_test)
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#print("Model2 accuracy: ", score)
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# make a prediction
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prediction = model.predict(df[['Open','High','Low','Volume']][n-1:])
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#print("RandomForest predicted close for next candle: ", prediction)
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return prediction
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