Start working on strategy evaluation

strategy_evaluation/indicators.py

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+import pandas as pd
+import datetime as dt
+import matplotlib.pyplot as plt
+from util import get_data
+
+
+def author():
+    return "felixm"
+
+
+def normalize(timeseries):
+    return timeseries / timeseries.iloc[0]
+
+
+def bollinger_band(df, symbol, period=20, m=2):
+    boll_sma = df[symbol].rolling(period).mean()
+    std = df[symbol].rolling(period).std()
+    boll_up = boll_sma + m * std
+    boll_lo = boll_sma - m * std
+    df[f"{symbol}-Boll({period})-sma"] = boll_sma
+    df[f"{symbol}-Boll({period})-up"] = boll_up
+    df[f"{symbol}-Boll({period})-lo"] = boll_lo
+
+
+def sma(df, symbol, period):
+    """Adds SMA for one or multiple periods to df and returns SMAs"""
+    if type(period) is int:
+        period = [period]
+    keys = []
+    for p in period:
+        key = f"{symbol}-sma({p})"
+        df[key] = df[symbol].rolling(p).mean()
+        keys.append(key)
+    return df[keys]
+
+
+def ema(df, symbol, period):
+    """Adds a new column to the dataframe EMA(period)"""
+    df[f"{symbol}-ema({period})"] = df[symbol].ewm(span=period).mean()
+
+
+def price_sma(df, symbol, period):
+    """Calculates SMA and adds new column price divided by SMA to the df."""
+    sma = df[symbol].rolling(period).mean()
+    df[f"{symbol}-price/sma({period})"] = df[symbol] / sma
+
+
+def rsi(df, symbol, period=14):
+    """Calculates relative strength index over given period."""
+
+    def rsi(x):
+        pct = x.pct_change()
+        avg_gain = pct[pct > 0].mean()
+        avg_loss = pct[pct <= 0].abs().mean()
+        rsi = 100 - (100 /
+                     (1 + ((avg_gain / period) /
+                           (avg_loss / period))))
+        return rsi
+
+    key = f"rsi"
+    # Add one to get 'period' price changes (first change is nan).
+    period += 1
+    df[key] = df[symbol].rolling(period).apply(rsi)
+    return df[key]
+
+
+def macd(df, symbol):
+    macd = df[symbol].ewm(span=12).mean() - df[symbol].ewm(span=26).mean()
+    k1 = f"macd"
+    k2 = k1 + "-signal"
+    df[k1] = macd
+    df[k2] = macd.rolling(9).mean()
+    return df[[k1, k2]]
+
+
+def price_delta(df, symbol, period=1):
+    """Calculate delta between previous day and today."""
+    df[f"{symbol}-diff({period})"] = df[symbol].diff(periods=period)
+
+
+def test_indicators():
+    symbol = "JPM"
+
+    sd = dt.datetime(2008, 1, 1)
+    ed = dt.datetime(2009, 12, 31)
+    df = get_data([symbol], pd.date_range(sd, ed))
+    df.drop(columns=["SPY"], inplace=True)
+    df_orig = df.copy()
+    # df = normalize(df)
+
+    sma(df, symbol, 21)
+    ema(df, symbol, 21)
+    df.plot(title="21 SMA and EMA")
+    plt.savefig('figure_1.png')
+
+    df = df_orig.copy()
+    sma(df, symbol, 8)
+    price_sma(df, symbol, 8)
+    df.plot(title="SMA and price / SMA", subplots=True)
+    plt.savefig('figure_2.png')
+
+    df = df_orig.copy()
+    bollinger_band(df, symbol)
+    df.plot(title="Bollinger Band")
+    plt.savefig('figure_3.png')
+
+    df = df_orig.copy()
+    rsi(df, symbol)
+    fig, axes = plt.subplots(nrows=2, sharex=True)
+    df[symbol].plot(ax=axes[0], title="JPM price action")
+    df["JPM-rsi(14)"].plot(ax=axes[1], title="RSI")
+    plt.savefig('figure_4.png')
+
+    df = df_orig.copy()
+    macd(df, symbol)
+    fig, axes = plt.subplots(nrows=2, sharex=True)
+    df[symbol].plot(ax=axes[0], title="JPM price action")
+    df[["JPM-macd", "JPM-macd-signal"]].plot(ax=axes[1])
+    plt.savefig('figure_5.png')
+