Add figures to report to finish project 6

manual_strategy/indicators.py

@@ -2,7 +2,7 @@ import pandas as pd
 import datetime as dt
 import matplotlib.pyplot as plt
 import TheoreticallyOptimalStrategy as tos
-from util import plot_data, get_data
+from util import get_data
 from marketsim.marketsim import compute_portvals
 from optimize_something.optimization import calculate_stats
 
@@ -31,7 +31,7 @@ def test_optimal_strategy():
     orders = pd.DataFrame(data=d, index=[start_date, end_date])
     bench = compute_portvals(orders, start_value, 0, 0)
     cum_ret_bench, avg_daily_ret_bench, \
-    std_daily_ret_bench, sharpe_ratio_bench = calculate_stats(bench, [1])
+        std_daily_ret_bench, sharpe_ratio_bench = calculate_stats(bench, [1])
 
     # Compare portfolio against benchmark
     print(f"Date Range: {start_date} to {end_date}")
@@ -56,7 +56,7 @@ def test_optimal_strategy():
     portvals.drop(columns=["Portval"], inplace=True)
 
     portvals.plot(title="Optimal strategy versus 1000 shares of JPM")
-    plt.savefig('figure_5.png')
+    plt.savefig('figure_6.png')
 
 
 def normalize(timeseries):
@@ -68,9 +68,9 @@ def bollinger_band(df, symbol, period=20, m=2):
     std = df[symbol].rolling(period).std()
     boll_up = boll_sma + m * std
     boll_lo = boll_sma - m * std
-    df[f"Boll({symbol}, {period})-sma"] = boll_sma
-    df[f"Boll({symbol}, {period})-up"] = boll_up
-    df[f"Boll({symbol}, {period})-lo"] = boll_lo
+    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):
@@ -78,31 +78,90 @@ def sma(df, symbol, period):
     df[f"{symbol}-sma({period})"] = df[symbol].rolling(period).mean()
 
 
+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 main():
-    # test_optimal_strategy()
+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"{symbol}-rsi({period})"
+    # Add one to get 'period' price changes (first change is nan).
+    period += 1
+    df[key] = df[symbol].rolling(period).apply(rsi)
+
+
+def macd(df, symbol):
+    macd = df[symbol].ewm(span=12).mean() - df[symbol].ewm(span=26).mean()
+    df[f"{symbol}-macd"] = macd
+    df[f"{symbol}-macd-signal"] = macd.rolling(9).mean()
+
+
+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 = normalize(df)
+    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)
-    # bollinger_band(df, symbol)
+    price_sma(df, symbol, 8)
+    df.plot(title="SMA and price / SMA", subplots=True)
+    plt.savefig('figure_2.png')
 
-    # TODO
-    # rsi(df, symbol)
-    # macd(df, symbol)
+    df = df_orig.copy()
+    bollinger_band(df, symbol)
+    df.plot(title="Bollinger Band")
+    plt.savefig('figure_3.png')
 
-    plot_data(df)
+    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')
+
+
+def main():
+    test_optimal_strategy()
+    test_indicators()
 
 
 if __name__ == "__main__":

manual_strategy/manual_strategy.md

@@ -1,6 +1,24 @@
 # Indicators
 
+## SMA and EMA
 
+![SMA and EMA](figure_1.png)
+
+## SMA/Price
+
+![SMA/Price](figure_2.png)
+
+## Bollinger Band
+
+![Bollinger Band](figure_3.png)
+
+## RSI
+
+![RSI](figure_4.png)
+
+## MACD
+
+![MACD](figure_5.png)
 
 
 # Optimal Strategy