first commit

2018-05-08 20:51:20 +08:00
commit 55c11b374a
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+"""
+script that runs continuously and collects pricing and technical indicators data,
+entering the data into instrument/window data tables.
+"""
+import sqlite3
+import sqlalchemy
+import pandas as pd
+from datetime import datetime
+from backend.pricing import history
+from backend.config import currencies
+from backend.indicators import sma, stoch, pivotPoints, bollBands
+
+
+windows = ['M5', 'M15', 'H1', 'H4', 'D']
+for instr in currencies:
+    for w in windows:
+        LiteCurrencyDB(instr, w)
+
+def LiteCurrencyDB(instr, w):
+    # connect to the DB
+    db = sqlite3.connect('liteDB/currencies.db')
+    c = db.cursor()
+    table_name = "%s_%s" % (instr, w)
+    # get last 500 data points
+    df = history(instr, w)
+    prices = df['Close']
+    # set the dataframe with our technical indicators
+    df['fast_sma'] = sma(prices, 6)
+    df['slow_sma'] = sma(prices, 10)
+    df = stoch(df, 6, 3)
+    df = bollBands(df, 5, 0.5)
+    pp = pd.DataFrame(data=pivotPoints(df))
+    df = pd.concat([df,pp], axis=1, join_axes=[df.index])
+    inputs = list(tuple(row for idx, row in df.iterrows()))
+    # insert them into the dedicated table for this currency and timeframe
+    c.execute('''CREATE TABLE IF NOT EXISTS {}(
+                dt TEXT, Open REAL, High REAL, Low REAL, Close REAL,
+                Volume INTEGER, fast_sma REAL, slow_sma REAL, Lstoch REAL,
+                Hstoch REAL, K REAL, D REAL, rolling_mean REAL, boll_high REAL,
+                boll_low REAL, PP REAL, R1 REAL, R2 REAL, R3 REAL, S1 REAL,
+                S2 REAL, S3 REAL
+                )'''.format(table_name))
+    c.executemany('''INSERT INTO {}(
+            dt, Open, High, Low, Close, Volume, fast_sma, slow_sma, Lstoch,
+            Hstoch, K, D, rolling_mean, boll_high, boll_low, PP, R1, R2, R3,
+            S1, S2, S3
+            ) VALUES(?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?)'''
+            .format(table_name), inputs)
+    db.commit()
+    print('table created successfully')
+    return db, df