def execute(stock_name1, stock_name2, start_date, end_date, fetchStocks):
stock1 = stockDataRetriever(stock_name1, start_date, end_date).fetchStock(fetchStocks)
stock2 = stockDataRetriever(stock_name2, start_date, end_date).fetchStock(fetchStocks)
print("Executing: ", stock_name1, stock_name2)
# generate df by merging both stocks on date
df = pd.merge(stock1[['Date','Close']], stock2[['Date','Close']], on="Date")
# generate zscore from pricing ratios
ratios = df['Close_x']/df['Close_y']
ratios_mavg = df['Close_x'].rolling(window=60).mean()/df['Close_y'].rolling(window=60).mean()
zscore = z_score(ratios)
df['zscore'] = zscore
# plot_zscore(zscore)
# buy stock1 sell stock2 when zscore < -1, buy stock2 sell stock1 when zscore > 1
df['buy'] = np.where(zscore < -1, 1, 0)
df['sell'] = np.where(zscore > 1, -1, 0)
df['signals'] = df['buy'] + df['sell']
df['positions'] = df['signals'].diff()
plot_signals(df, stock_name1, stock_name2)
for denom in [10,100,1000]:
percent_profit = generate_profit(df,denom)
print(percent_profit)
print(benchmark().get_SPY_benchmark())
return percent_profit, plt
def cointegration(stock_name1, stock_name2, start_date, end_date, fetchStocks):
stock1 = stockDataRetriever(stock_name1, start_date,
end_date).fetchStock(fetchStocks)
stock2 = stockDataRetriever(stock_name2, start_date,
end_date).fetchStock(fetchStocks)
score, pvalue, _ = coint(stock1["Close"], stock2["Close"])
return (stock_name1, stock_name2, pvalue)
def execute():
raw_file = stockDataRetriever("wiki/intraday", "none", "none").fetchStock(False)
stock_groups = raw_file.groupby(raw_file['Ticker']) # seperate out each stock into groups
for name, stock in stock_groups:
rsi_df = RSI(name, stock)
macd_df = MACD(name, stock)
# merge rsi and macd strategies
df = pd.merge(rsi_df, macd_df)
# scan over 3 periods to find common buy signals
for i in range(3,len(df)):
if (scan_buy_signal(df['signal_rsi'][i-3:i])) and (scan_buy_signal(df['signal_macd'][i-3:i])):
df['positions'][i] = 1
else:
df['positions'][i] = 0
# create sell signals
sell = np.where((df['signal_rsi'] == -1) | (df['signal_macd'] == -1), -1, 0)
df['positions'] += sell
# #plot trading signals
# ax1 = plt.figure().add_subplot(111, ylabel='Price in $')
# df['Close'].plot(ax=ax1, color='r', lw=2.)
# ax1.set_title(name + ": RSI_MACD")
# plt.xlabel("Time (Minutes)")
# ax1.plot(df.loc[df.positions == 1].index, df.Close[df.positions == 1], '^', markersize=10, color='m')
# ax1.plot(df.loc[df.positions == -1].index, df.Close[df.positions == -1], 'v', markersize=10, color='k')
# plt.show()
# for denom in [10,100,1000]:
for denom in [100]:
profit(df, name, denom)
def build_df(stock_name, start_date, end_date):
# get tweet sentiment
print("getting tweet sentiment")
data = tweetDataRetriever(stock_name).fetch_tweet()
data["Date"] = ""
sentiment = []
for tweet in data["text"]:
sentiment.append(tweet_sentiment(tweet))
data["Sentiment"] = sentiment
# convert twitter date format for merge with stock data
print("converting dates")
date_conv = {
'Jan': '01',
'Feb': '02',
'Mar': '03',
'Apr': '04',
"May": '05',
"Jun": '06',
"Jul": '07',
"Aug": '08',
"Sep": '09',
"Oct": '10',
"Nov": '11',
"Dec": '12'
}
for i, row in data.iterrows():
raw = data["created_at"][i].split()
#data["Date"][i] = raw[5] + "-" + date_conv[raw[1]] + "-" + raw[2]
data.loc[i, "Date"] = raw[5] + "-" + date_conv[raw[1]] + "-" + raw[2]
# drop tweet replies
print("dropping replies")
for i, row in data.iterrows():
if isinstance(data["in_reply_to_screen_name"][i], str):
#print(row, data["in_reply_to_screen_name"][row])
data = data.drop(i)
# aggregate tweets on same day
print("aggregating tweets")
data = data.groupby("Date")[[
"Sentiment", "favorite_count", "retweet_count"
]].apply(lambda x: x.values.tolist())
data = data.to_frame('twitter').reset_index()
# merge tweets with stock data
print("merging")
data_stock = stockDataRetriever(stock_name, start_date,
end_date).fetchStock(True)
df = data_stock.merge(data, how="left", on='Date')
# create average sentiment
print("creating average sentiment, other important values")
average_sentiment_other_values(df)
# create indicators for supervised learning
print("creating indicators for learning")
df["change"] = df["Close"].diff()
df["change"] = df["change"].fillna(0)
df["change_int"] = 0
for i, row in df.iterrows():
val = -1
if float(df["change"][i]) > 0:
val = 1
df.loc[i, "change_int"] = val
df.to_csv("./twitterCSV/" + stock_name[5:] + "_MERGED.csv")
return df
def execute(stock_name, start_date, end_date, fetchStocks, share_amount):
stock = stockDataRetriever(stock_name, start_date,
end_date).fetchStock(fetchStocks)
df = pd.DataFrame(index=stock.index)
df['Close'] = stock['Close'].copy()
df['buy_signal'] = 0
df['sell_signal'] = 0
window = 14
# create frames of roll ups and roll downs
delta = stock['Close'].diff()
upgains, downloss = delta.copy(), delta.copy()
upgains[upgains < 0] = 0
downloss[downloss > 0] = 0
# use moving average to create roll up/down
roll_up = upgains.rolling(window=window, min_periods=1,
center=False).mean()
roll_down = downloss.abs().rolling(window=window,
min_periods=1,
center=False).mean()
# create RSI
RS = roll_up / roll_down
RSI = 100.0 - (100.0 / (1.0 + RS))
# plot RSI
fig = plt.figure()
fig.suptitle(stock_name + ": RSI")
RSI.plot()
df['RSI'] = RSI.copy()
# Create df for buy and sell signal
df['buy_signal'][window:] = np.where(df['RSI'][window:] < 30, 1.0, 0)
df['sell_signal'][window:] = np.where(df['RSI'][window:] > 70, 0, 1.0)
# when signal changes from 1 to 0 or 0 to 1 - is a buy or sell
df['buy_positions'] = df['buy_signal'].diff()
df['sell_positions'] = df['sell_signal'].diff()
# merge positions of buying - when RSI crosses 30 and selling - when price crosses 70
df['buy_positions'] = np.where(df['buy_positions'] < 0, 0,
df['buy_positions'])
df['sell_positions'] = np.where(df['sell_positions'] > 0, 0,
df['sell_positions'])
df['positions'] = df['buy_positions'] + df['sell_positions']
df['signal'] = createSignals(df['positions'].copy())
ax1 = plt.figure().add_subplot(111, ylabel='Price in $')
df[['Close']].plot(ax=ax1, lw=2.)
ax1.set_title(stock_name[5:] + ": RSI")
ax1.plot(df.loc[df.positions == 1.0].index,
df.Close[df.positions == 1.0],
'^',
markersize=10,
color='m')
ax1.plot(df.loc[df.positions == -1.0].index,
df.Close[df.positions == -1.0],
'v',
markersize=10,
color='k')
#share_amount = 100
initial_capital = float(100000.0)
positions = pd.DataFrame(index=df.index).fillna(0.0)
positions[stock_name] = share_amount * df['signal']
portfolio = positions.multiply(stock['Adj. Close'], axis=0)
pos_diff = positions.diff()
portfolio['holdings'] = (positions.multiply(stock['Adj. Close'],
axis=0)).sum(axis=1)
portfolio['cash'] = initial_capital - (pos_diff.multiply(
stock['Adj. Close'], axis=0)).sum(axis=1).cumsum()
portfolio['total'] = portfolio['cash'] + portfolio['holdings']
portfolio['returns'] = portfolio['total'].pct_change()
# compare against baseline long position
baseline_profit = (stock['Adj. Close'].iloc[-1] -
stock['Adj. Close'].iloc[0]) * share_amount
strategy_profit = portfolio['total'].iloc[-1] - initial_capital
print(baseline_profit, strategy_profit)
if strategy_profit < baseline_profit:
percentage_difference_profit = round(
(float(strategy_profit - abs(baseline_profit))) / initial_capital *
100, 2)
else:
percentage_difference_profit = round(
(float(strategy_profit - baseline_profit)) / initial_capital * 100,
2)
return percentage_difference_profit, plt
def execute(stock_name, start_date, end_date, fetchStocks, share_amount):
stock = stockDataRetriever(stock_name, start_date,
end_date).fetchStock(fetchStocks)
window = 12
df = pd.DataFrame(index=stock.index)
df['low_signal'] = 0.0
df['high_signal'] = 0.0
# Create bands
df['middle_band'] = stock['Close'].rolling(window=window,
min_periods=1,
center=False).mean()
df['moving_deviation'] = stock['Close'].rolling(window=window,
min_periods=1,
center=False).std()
df['upper_band'] = df['middle_band'] + df['moving_deviation'] * 2
df['lower_band'] = df['middle_band'] - df['moving_deviation'] * 2
df['closing_price'] = stock['Close']
# Create df for high and low bands signal
df['low_signal'][window:] = np.where(
df['closing_price'][window:] < df['lower_band'][window:], 1.0, 0)
df['high_signal'][window:] = np.where(
df['closing_price'][window:] > df['upper_band'][window:], 0, 1.0)
# when signal changes from 1 to 0 or 0 to 1 - is a buy or sell
df['buy_positions'] = df['low_signal'].diff()
df['sell_positions'] = df['high_signal'].diff()
# merge positions of buying - when price crosses lower band and selling - when price crosses upper band
df['buy_positions'] = np.where(df['buy_positions'] < 0, 0,
df['buy_positions'])
df['sell_positions'] = np.where(df['sell_positions'] > 0, 0,
df['sell_positions'])
df['positions'] = df['buy_positions'] + df['sell_positions']
# combine low,high signals to create stock ownership signal
df['signal'] = createSignals(df['positions'].copy())
dfplot = df.copy(deep=True)
dfplot = dfplot.loc[:600]
ax1 = plt.figure().add_subplot(111, ylabel='Price in $')
dfplot[['upper_band', 'lower_band', 'middle_band',
'closing_price']].plot(ax=ax1, lw=2.)
plt.xlabel("Time (Days)")
ax1.set_title(stock_name[5:] + ": Bollinger Bands")
ax1.plot(dfplot.loc[dfplot.positions == 1.0].index,
dfplot.lower_band[dfplot.positions == 1.0],
'^',
markersize=10,
color='m')
ax1.plot(dfplot.loc[dfplot.positions == -1.0].index,
dfplot.upper_band[dfplot.positions == -1.0],
'v',
markersize=10,
color='k')
#share_amount = 100
initial_capital = float(10000.0)
positions = pd.DataFrame(index=df.index).fillna(0.0)
positions[stock_name] = share_amount * df['signal']
portfolio = positions.multiply(stock['Adj. Close'], axis=0)
pos_diff = positions.diff()
portfolio['holdings'] = (positions.multiply(stock['Adj. Close'],
axis=0)).sum(axis=1)
portfolio['cash'] = initial_capital - (pos_diff.multiply(
stock['Adj. Close'], axis=0)).sum(axis=1).cumsum()
portfolio['total'] = portfolio['cash'] + portfolio['holdings']
portfolio['returns'] = portfolio['total'].pct_change()
# compare against baseline long position
baseline_profit = (stock['Adj. Close'].iloc[-1] -
stock['Adj. Close'].iloc[0]) * share_amount
strategy_profit = portfolio['total'].iloc[-1] - initial_capital
print(baseline_profit, strategy_profit)
if strategy_profit < baseline_profit:
percentage_difference_profit = round(
(float(strategy_profit - abs(baseline_profit))) / initial_capital *
100, 2)
else:
percentage_difference_profit = round(
(float(strategy_profit - baseline_profit)) / initial_capital * 100,
2)
return plt, percentage_difference_profit
def execute(stock_name, start_date, end_date, fetchStocks, share_amount):
stock = stockDataRetriever(stock_name, start_date,
end_date).fetchStock(fetchStocks)
# Initialize the short and long windows and buy sell in df
short_window = 40
long_window = 100
df = pd.DataFrame(index=stock.index)
df['signal'] = 0.0
# Create short and long simple moving average
df['short_mavg'] = stock['Close'].rolling(window=short_window,
min_periods=1,
center=False).mean()
df['long_mavg'] = stock['Close'].rolling(window=long_window,
min_periods=1,
center=False).mean()
# Create df
df['signal'][short_window:] = np.where(
df['short_mavg'][short_window:] > df['long_mavg'][short_window:], 1.0,
0.0)
# when signal changes fromn 1 to 0 or 0 to 1 - is a buy or sell
df['positions'] = df['signal'].diff()
#share_amount = 100
initial_capital = float(10000.0)
# create a portfolio that simulates owning and buying stocks
positions = pd.DataFrame(index=df.index).fillna(0.0)
positions[stock_name] = share_amount * df['signal']
portfolio = positions.multiply(stock['Adj. Close'], axis=0)
pos_diff = positions.diff()
portfolio['holdings'] = (positions.multiply(stock['Adj. Close'],
axis=0)).sum(axis=1)
portfolio['cash'] = initial_capital - (pos_diff.multiply(
stock['Adj. Close'], axis=0)).sum(axis=1).cumsum()
portfolio['total'] = portfolio['cash'] + portfolio['holdings']
portfolio['returns'] = portfolio['total'].pct_change()
# compare against baseline long position
baseline_profit = (stock['Adj. Close'].iloc[-1] -
stock['Adj. Close'].iloc[0]) * share_amount
strategy_profit = portfolio['total'].iloc[-1] - initial_capital
if strategy_profit < baseline_profit:
percentage_difference_profit = round(
(float(strategy_profit - abs(baseline_profit))) / initial_capital *
100, 2)
else:
percentage_difference_profit = round(
(float(strategy_profit - baseline_profit)) / initial_capital * 100,
2)
print(baseline_profit, strategy_profit)
# print out sharpe ratio
# sharpe_ratio(portfolio['returns'])
fig = plt.figure()
fig.suptitle(stock_name + ": SMA")
ax1 = fig.add_subplot(111, ylabel='Price in $')
dfplot = df.copy(deep=True)
dfplot = dfplot.loc[:600]
stock.loc[:600]['Close'].plot(ax=ax1, color='r', lw=2.)
dfplot[['short_mavg', 'long_mavg']].plot(ax=ax1, lw=2.)
plt.xlabel("Time (Days)")
#Plot the buy and sell df
ax1.plot(dfplot.loc[dfplot.positions == 1.0].index,
dfplot.short_mavg[dfplot.positions == 1.0],
'^',
markersize=10,
color='m')
ax1.plot(dfplot.loc[dfplot.positions == -1.0].index,
dfplot.short_mavg[dfplot.positions == -1.0],
'v',
markersize=10,
color='k')
return plt, percentage_difference_profit
def execute(stock_name, start_date, end_date, fetchStocks, share_amount):
stock = stockDataRetriever(stock_name, start_date,
end_date).fetchStock(fetchStocks)
short_window = 50
long_window = 200
df = pd.DataFrame(index=stock.index)
df['signal'] = 0.0
# Create short and long simple moving average
df['short_ema'] = stock['Close'].ewm(span=short_window,
adjust=False).mean()
df['long_ema'] = stock['Close'].ewm(span=long_window, adjust=False).mean()
# Create df
df['signal'][short_window:] = np.where(
df['short_ema'][short_window:] > df['long_ema'][short_window:], 1.0,
0.0)
# when signal changes fromn 1 to 0 or 0 to 1 - is a buy or sell
df['positions'] = df['signal'].diff()
ax1 = plt.figure().add_subplot(111, ylabel='Price in $')
stock['Close'].plot(ax=ax1, color='r', lw=2.)
df[['short_ema', 'long_ema']].plot(ax=ax1, lw=2.)
ax1.set_title(stock_name[5:] + ": EMA")
# Plot the buy and sell df
ax1.plot(df.loc[df.positions == 1.0].index,
df.short_ema[df.positions == 1.0],
'^',
markersize=10,
color='m')
ax1.plot(df.loc[df.positions == -1.0].index,
df.short_ema[df.positions == -1.0],
'v',
markersize=10,
color='k')
#share_amount = 100
initial_capital = float(100000.0)
positions = pd.DataFrame(index=df.index).fillna(0.0)
positions[stock_name] = share_amount * df['signal']
portfolio = positions.multiply(stock['Adj. Close'], axis=0)
pos_diff = positions.diff()
portfolio['holdings'] = (positions.multiply(stock['Adj. Close'],
axis=0)).sum(axis=1)
portfolio['cash'] = initial_capital - (pos_diff.multiply(
stock['Adj. Close'], axis=0)).sum(axis=1).cumsum()
portfolio['total'] = portfolio['cash'] + portfolio['holdings']
portfolio['returns'] = portfolio['total'].pct_change()
# compare against baseline long position
baseline_profit = (stock['Adj. Close'].iloc[-1] -
stock['Adj. Close'].iloc[0]) * share_amount
strategy_profit = portfolio['total'].iloc[-1] - initial_capital
print(baseline_profit, strategy_profit)
if strategy_profit < baseline_profit:
percentage_difference_profit = round(
(float(strategy_profit - abs(baseline_profit))) / initial_capital *
100, 2)
else:
percentage_difference_profit = round(
(float(strategy_profit - baseline_profit)) / initial_capital * 100,
2)
return plt, percentage_difference_profit