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backend/indicators.py

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