def coinTossWinnerGraph(cur, ax):
cur.execute('use IPL_DATA_SET');
cur.execute('Select Tosswinner, count(*) from Matches group by Tosswinner');
QuerryResponse = cur.fetchall();
Teams = []
TeamWinCount = []
for i in range(0,len(QuerryResponse)):
Teams.insert(0,QuerryResponse[i][0]);
TeamWinCount.insert(0,QuerryResponse[i][1]);
ax.scatter(Teams,TeamWinCount);
ax.set_title('Coin Toss Wins');
plt.xlabel('Team')
plt.ylabel('Number of Wins');
ax.xaxis.set_label_coords(0.50, 0.075);
plt.setp(ax.get_xticklabels(), rotation=30, horizontalalignment='right')
plt.tick_params(axis='x', which='major', labelsize=7)
crs = mplcursors.cursor(ax,hover=True)
def draw_networkx_nodes(G, pos,
nodelist=None,
node_size=300,
node_color='r',
node_shape='o',
alpha=1.0,
cmap=None,
vmin=None,
vmax=None,
ax=None,
linewidths=None,
label=None,
**kwds):
"""Draw the nodes of the graph G.
This draws only the nodes of the graph G.
Parameters
----------
G : graph
A networkx graph
pos : dictionary
A dictionary with nodes as keys and positions as values.
Positions should be sequences of length 2.
ax : Matplotlib Axes object, optional
Draw the graph in the specified Matplotlib axes.
nodelist : list, optional
Draw only specified nodes (default G.nodes())
node_size : scalar or array
Size of nodes (default=300). If an array is specified it must be the
same length as nodelist.
node_color : color string, or array of floats
Node color. Can be a single color format string (default='r'),
or a sequence of colors with the same length as nodelist.
If numeric values are specified they will be mapped to
colors using the cmap and vmin,vmax parameters. See
matplotlib.scatter for more details.
node_shape : string
The shape of the node. Specification is as matplotlib.scatter
marker, one of 'so^>v<dph8' (default='o').
alpha : float
The node transparency (default=1.0)
cmap : Matplotlib colormap
Colormap for mapping intensities of nodes (default=None)
vmin,vmax : floats
Minimum and maximum for node colormap scaling (default=None)
linewidths : [None | scalar | sequence]
Line width of symbol border (default =1.0)
label : [None| string]
Label for legend
Returns
-------
matplotlib.collections.PathCollection
`PathCollection` of the nodes.
Examples
--------
>>> G=nx.dodecahedral_graph()
>>> nodes=nx.draw_networkx_nodes(G,pos=nx.spring_layout(G))
Also see the NetworkX drawing examples at
http://networkx.github.io/documentation/latest/gallery.html
See Also
--------
draw()
draw_networkx()
draw_networkx_edges()
draw_networkx_labels()
draw_networkx_edge_labels()
"""
try:
import matplotlib.pyplot as plt
import numpy
except ImportError:
raise ImportError("Matplotlib required for draw()")
except RuntimeError:
print("Matplotlib unable to open display")
raise
if ax is None:
ax = plt.gca()
if nodelist is None:
nodelist = G.nodes()
if not nodelist or len(nodelist) == 0: # empty nodelist, no drawing
return None
try:
xy = numpy.asarray([pos[v] for v in nodelist])
except KeyError as e:
raise nx.NetworkXError('Node %s has no position.'%e)
except ValueError:
raise nx.NetworkXError('Bad value in node positions.')
node_collection = ax.scatter(xy[:, 0], xy[:, 1],
s=node_size,
c=node_color,
marker=node_shape,
cmap=cmap,
vmin=vmin,
vmax=vmax,
alpha=alpha,
linewidths=linewidths,
label=label,
**kwds)
node_collection.set_zorder(2)
return node_collection
def plot_corr_diff(tseries1, tseries2, fig=None,
ts_names=['1', '2']):
"""
Show the differences in *Fischer-transformed* snr correlations for two
time-series
Parameters
----------
tseries1, tseries2 : nitime TimeSeries objects
These are the time-series to compare, with each of them having the
dims: (n_channels, n_reps, time), where n_channels1 = n_channels2
lb,ub: float
Lower and upper bounds on the frequency range over which to
calculate the information rate (default to [0,Nyquist]).
fig: matplotlib figure object
If you want to do this on already existing figure. Otherwise, a new
figure object will be generated.
ts_names: list of str
Labels for the two inputs, to be used in plotting (defaults to
['1','2'])
bandwidth, adaptive, low_bias: See :func:`SNRAnalyzer` for details
Returns
-------
fig: a matplotlib figure object
"""
if fig is None:
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
SNR1 = []
SNR2 = []
corr1 = []
corr2 = []
corr_e1 = []
corr_e2 = []
for i in range(tseries1.shape[0]):
SNR1.append(nta.SNRAnalyzer(ts.TimeSeries(tseries1.data[i],
sampling_rate=tseries1.sampling_rate)))
corr1.append(np.arctanh(np.abs(SNR1[-1].correlation[0])))
corr_e1.append(SNR1[-1].correlation[1])
SNR2.append(nta.SNRAnalyzer(ts.TimeSeries(tseries2.data[i],
sampling_rate=tseries2.sampling_rate)))
corr2.append(np.arctanh(np.abs(SNR2[-1].correlation[0])))
corr_e2.append(SNR1[-1].correlation[1])
ax.scatter(np.array(corr1), np.array(corr2))
ax.errorbar(np.mean(corr1), np.mean(corr2),
yerr=np.std(corr2),
xerr=np.std(corr1))
plot_min = min(min(corr1), min(corr2))
plot_max = max(max(corr1), max(corr2))
ax.plot([plot_min, plot_max], [plot_min, plot_max], 'k--')
ax.set_xlabel('Correlation (Fischer Z) %s' % ts_names[0])
ax.set_ylabel('Correlation (Fischer Z) %s' % ts_names[1])
return fig, corr1, corr2
def plotTSs1(start_date, end_date, shortMa, longMa):
yf.pdr_override()
plt.close()
shortMaP = int(shortMa)
longMaP = int(longMa)
data = pdr.get_data_yahoo("BTC-USD", start_date, end_date)
btc = pd.DataFrame(data)
btc = btc[['Adj Close']]
btc.columns = ['price']
btc.reset_index(level=0, inplace=True)
###################################################################################
# Code taken from Willems, K., 2019. (Tutorial) Python For Finance: Algorithmic Trading
#[online] DataCamp Community. Available at: <https://www.datacamp.com/community/tutorials/finance-python-trading#basics>
plt.close()
signal = pd.DataFrame(index=btc.index) #copy index from BTC
signal['id'] = 0.0
signal['shortMA'] = btc['price'].rolling(
window=shortMaP,
min_periods=1).mean() #Short Moving Average Calculated
signal['longMA'] = btc['price'].rolling(
window=longMaP, min_periods=1).mean() ##Long Moving Average Calculated
signal['id'][shortMaP:] = np.where(
signal['shortMA'][shortMaP:] > signal['longMA'][shortMaP:], 1.0,
0.0) #where short moving average surpasses long, id is changed to 1
#[shortMaP:] only for the period that is surpasses than the short moving average
signal['buySell'] = signal['id'].diff()
fig = plt.figure()
ax1 = fig.add_subplot(111, ylabel=' Currency Price in $')
ax1.plot(btc.price) #Plot price
# Plot the short and long moving averages
ax1.plot(signal['shortMA'], color='black', label='Short Moving Average')
ax1.plot(signal['longMA'], color='orange', label='Long Moving Average')
ax1.legend(loc='upper right')
# The buy signals according to buySell are plotted
ax1.plot(signal.loc[signal.buySell == 1.0].index,
signal.shortMA[signal.buySell == 1.0],
'^',
markersize=10,
color='green')
# The sell signals according to buySell are plotted
ax1.plot(signal.loc[signal.buySell == -1.0].index,
signal.shortMA[signal.buySell == -1.0],
'v',
markersize=10,
color='red')
# Show the plot
plt.show()
###################################################################################
# Code taken from Willems, K., 2019. (Tutorial) Python For Finance: Algorithmic Trading
portfolio = pd.DataFrame(index=signal.index)
initInvestment = 100000
stocksOwned = pd.DataFrame(index=signal.index).fillna(0.0)
noCur = 10 #No of currency to be purchased
stocksOwned['BTC'] = noCur * signal['id']
portfolio['Holdings'] = stocksOwned['BTC'].multiply(btc['price'], axis=0)
buySell = stocksOwned['BTC'].diff()
portfolio['cash'] = initInvestment - (buySell.multiply(btc['price'],
axis=0)).cumsum()
portfolio['total'] = portfolio['cash'] + portfolio['Holdings']
portfolio['cash'][0] = initInvestment
portfolio['total'][0] = initInvestment
###################################################################################
fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot(portfolio.index, portfolio['total'], label='Price')
ax.set_xlabel('Date')
ax.set_ylabel('Value of Portfolio in $')
day = portfolio.loc[signal.buySell == 1.0].index
day2 = portfolio.loc[signal.buySell == -1.0].index
#x co
ax.scatter(x=day, y=portfolio.loc[day, 'total'], color='green', marker='^')
ax.scatter(x=day2, y=portfolio.loc[day2, 'total'], color='red', marker='^')
def draw_networkx_nodes(G,
pos,
nodelist=None,
node_size=300,
node_color='r',
node_shape='o',
alpha=1.0,
cmap=None,
vmin=None,
vmax=None,
ax=None,
linewidths=None,
label=None,
**kwds):
"""Draw the nodes of the graph G.
This draws only the nodes of the graph G.
Parameters
----------
G : graph
A networkx graph
pos : dictionary
A dictionary with nodes as keys and positions as values.
Positions should be sequences of length 2.
ax : Matplotlib Axes object, optional
Draw the graph in the specified Matplotlib axes.
nodelist : list, optional
Draw only specified nodes (default G.nodes())
node_size : scalar or array
Size of nodes (default=300). If an array is specified it must be the
same length as nodelist.
node_color : color string, or array of floats
Node color. Can be a single color format string (default='r'),
or a sequence of colors with the same length as nodelist.
If numeric values are specified they will be mapped to
colors using the cmap and vmin,vmax parameters. See
matplotlib.scatter for more details.
node_shape : string
The shape of the node. Specification is as matplotlib.scatter
marker, one of 'so^>v<dph8' (default='o').
alpha : float
The node transparency (default=1.0)
cmap : Matplotlib colormap
Colormap for mapping intensities of nodes (default=None)
vmin,vmax : floats
Minimum and maximum for node colormap scaling (default=None)
linewidths : [None | scalar | sequence]
Line width of symbol border (default =1.0)
label : [None| string]
Label for legend
Returns
-------
matplotlib.collections.PathCollection
`PathCollection` of the nodes.
Examples
--------
>>> G=nx.dodecahedral_graph()
>>> nodes=nx.draw_networkx_nodes(G,pos=nx.spring_layout(G))
Also see the NetworkX drawing examples at
http://networkx.github.io/documentation/latest/gallery.html
See Also
--------
draw()
draw_networkx()
draw_networkx_edges()
draw_networkx_labels()
draw_networkx_edge_labels()
"""
try:
import matplotlib.pyplot as plt
import numpy
except ImportError:
raise ImportError("Matplotlib required for draw()")
except RuntimeError:
print("Matplotlib unable to open display")
raise
if ax is None:
ax = plt.gca()
if nodelist is None:
nodelist = G.nodes()
if not nodelist or len(nodelist) == 0: # empty nodelist, no drawing
return None
try:
xy = numpy.asarray([pos[v] for v in nodelist])
except KeyError as e:
raise nx.NetworkXError('Node %s has no position.' % e)
except ValueError:
raise nx.NetworkXError('Bad value in node positions.')
node_collection = ax.scatter(xy[:, 0],
xy[:, 1],
s=node_size,
c=node_color,
marker=node_shape,
cmap=cmap,
vmin=vmin,
vmax=vmax,
alpha=alpha,
linewidths=linewidths,
label=label,
**kwds)
node_collection.set_zorder(2)
return node_collection
def plot_corr_diff(tseries1, tseries2, fig=None, ts_names=['1', '2']):
"""
Show the differences in *Fischer-transformed* snr correlations for two
time-series
Parameters
----------
tseries1, tseries2 : nitime TimeSeries objects
These are the time-series to compare, with each of them having the
dims: (n_channels, n_reps, time), where n_channels1 = n_channels2
lb,ub: float
Lower and upper bounds on the frequency range over which to
calculate the information rate (default to [0,Nyquist]).
fig: matplotlib figure object
If you want to do this on already existing figure. Otherwise, a new
figure object will be generated.
ts_names: list of str
Labels for the two inputs, to be used in plotting (defaults to
['1','2'])
bandwidth, adaptive, low_bias: See :func:`SNRAnalyzer` for details
Returns
-------
fig: a matplotlib figure object
"""
if fig is None:
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
SNR1 = []
SNR2 = []
corr1 = []
corr2 = []
corr_e1 = []
corr_e2 = []
for i in range(tseries1.shape[0]):
SNR1.append(
nta.SNRAnalyzer(
ts.TimeSeries(tseries1.data[i],
sampling_rate=tseries1.sampling_rate)))
corr1.append(np.arctanh(np.abs(SNR1[-1].correlation[0])))
corr_e1.append(SNR1[-1].correlation[1])
SNR2.append(
nta.SNRAnalyzer(
ts.TimeSeries(tseries2.data[i],
sampling_rate=tseries2.sampling_rate)))
corr2.append(np.arctanh(np.abs(SNR2[-1].correlation[0])))
corr_e2.append(SNR1[-1].correlation[1])
ax.scatter(np.array(corr1), np.array(corr2))
ax.errorbar(np.mean(corr1),
np.mean(corr2),
yerr=np.std(corr2),
xerr=np.std(corr1))
plot_min = min(min(corr1), min(corr2))
plot_max = max(max(corr1), max(corr2))
ax.plot([plot_min, plot_max], [plot_min, plot_max], 'k--')
ax.set_xlabel('Correlation (Fischer Z) %s' % ts_names[0])
ax.set_ylabel('Correlation (Fischer Z) %s' % ts_names[1])
return fig, corr1, corr2
def plotTsS2(start_date, end_date):
plt.close()
yf.pdr_override()
data = pdr.get_data_yahoo("BTC-USD", start_date, end_date)
btc = pd.DataFrame(data)
btc = btc[['Adj Close']]
btc.columns = ['Close']
btc.reset_index(level=0, inplace=True)
b = pd.DataFrame()
for x in btc:
b['price'] = btc['Close']
b['sma'] = btc['Close'].rolling(window=20).mean()
b['std'] = btc['Close'].rolling(window=20).std()
b['bolU'] = b['sma'] + (2 * b['std']) #Calculating Upper Bound
b['bolD'] = b['sma'] - (2 * b['std']) #Calculating Lower Bound
#Convert Bollinger Bands to %b - bollinger column
b['bollinger'] = (b['price'] - b['bolD']) / (b['bolU'] - b['bolD'])
bb1 = b[['price', 'bolU', 'bolD', 'bollinger']]
bb1.columns = ['Price', 'Upper Band', 'Lower Band', 'Bollinger']
bb1.fillna(0, inplace=True)
# In[ ]:
# In[9]:
RSI = pd.DataFrame(index=btc.index)
RSI['price'] = btc['Close']
RSI['val'] = None
RSI['up'] = 0 #When there is no up value 0
RSI['down'] = 0 #When there is no down value 0
size = RSI.shape[0]
dp = 14
for x in range(size):
if x == 0:
continue #first day will continue
#calculating the ups , when closing price is higher in day x than x -1
if RSI['price'].iloc[x] > RSI['price'].iloc[x - 1]: #
RSI['up'].iloc[x] = RSI['price'].iloc[x] - RSI['price'].iloc[x - 1]
else:
#calculating the downs days , when closing price is lower in day x than x -1
RSI['down'].iloc[x] = RSI['price'].iloc[x -
1] - RSI['price'].iloc[x]
if x >= dp:
avgUp = RSI['up'][x - dp:x].sum(
) / dp #calculates avg up of last dp days
avgDown = RSI['down'][x - dp:x].sum(
) / dp #calculates avg down of last dp days
rs = avgUp / avgDown #calculation of RS
RSI['val'].iloc[x] = 100 - 100 / (1 + rs)
signals = pd.DataFrame(index=btc.index) #copy index for BTC
signals['price'] = btc['Close']
signals['id'] = 0.0
signals['RSI'] = RSI['val']
signals['RSI'].fillna(0, inplace=True)
signals['bollinger'] = bb1['Bollinger']
signals['id'] = [np.nan for i in signals.index]
# only verifications for days after DPth (period of RSI) day
signals['id'][dp:].loc[((signals['RSI'] < 30) &
(signals['bollinger'] < 0))] = 1
signals['id'][dp:].loc[((signals['RSI'] > 70) &
(signals['bollinger'] > 1))] = 0
signals['id'].ffill(inplace=True) #fill empty values with 0
signals['id'].fillna(0, inplace=True)
signals['buySell'] = signals['id'].diff()
signals['buySell'].fillna(0, inplace=True)
fig, (ax) = plt.subplots(1, 1)
ax.plot(signals['price'])
ax.set_xlabel('Date')
ax.set_ylabel('Value in $')
day = signals.loc[signals.buySell == 1.0].index
day2 = signals.loc[signals.buySell == -1.0].index
#x cordinates, index of day --> y cordinate,price of day index
ax.scatter(
x=day,
y=signals.loc[day, 'price'],
marker='^',
color='green',
)
ax.scatter(x=day2, y=signals.loc[day2, 'price'], marker='v', color='red')
plt.show()
def plotPorts2(start_date, end_date):
plt.close()
yf.pdr_override()
data = pdr.get_data_yahoo("BTC-USD", start_date, end_date)
btc = pd.DataFrame(data)
btc = btc[['Adj Close']]
btc.columns = ['Close']
btc.reset_index(level=0, inplace=True)
# In[8]:
b = pd.DataFrame()
for x in btc:
b['price'] = btc['Close']
b['sma'] = btc['Close'].rolling(window=20).mean()
b['std'] = btc['Close'].rolling(window=20).std()
b['bolU'] = b['sma'] + (2 * b['std']) #Calculating Upper Bound
b['bolD'] = b['sma'] - (2 * b['std']) #Calculating Lower Bound
#Convert Bollinger Bands to %b - bollinger column
b['bollinger'] = (b['price'] - b['bolD']) / (b['bolU'] - b['bolD'])
bb1 = b[['price', 'bolU', 'bolD', 'bollinger']]
bb1.columns = ['Price', 'Upper Band', 'Lower Band', 'Bollinger']
bb1.fillna(0, inplace=True)
# In[ ]:
# In[9]:
RSI = pd.DataFrame(index=btc.index)
RSI['price'] = btc['Close']
RSI['val'] = None
RSI['up'] = 0 #When there is no up value 0
RSI['down'] = 0 #When there is no down value 0
size = RSI.shape[0]
dp = 14
for x in range(size):
if x == 0:
continue #first day will continue
#calculating the ups , when closing price is higher in day x than x -1
if RSI['price'].iloc[x] > RSI['price'].iloc[x - 1]: #
RSI['up'].iloc[x] = RSI['price'].iloc[x] - RSI['price'].iloc[x - 1]
else:
#calculating the downs days , when closing price is lower in day x than x -1
RSI['down'].iloc[x] = RSI['price'].iloc[x -
1] - RSI['price'].iloc[x]
if x >= dp:
avgUp = RSI['up'][x - dp:x].sum(
) / dp #calculates avg up of last dp days
avgDown = RSI['down'][x - dp:x].sum(
) / dp #calculates avg down of last dp days
rs = avgUp / avgDown #calculation of RS
RSI['val'].iloc[x] = 100 - 100 / (1 + rs)
signals = pd.DataFrame(index=btc.index) #copy index for BTC
signals['price'] = btc['Close']
signals['id'] = 0.0
signals['RSI'] = RSI['val']
signals['RSI'].fillna(0, inplace=True)
signals['bollinger'] = bb1['Bollinger']
signals['id'] = [np.nan for i in signals.index]
# only verifications for days after DPth (period of RSI) day
signals['id'][dp:].loc[((signals['RSI'] < 30) &
(signals['bollinger'] < 0))] = 1
signals['id'][dp:].loc[((signals['RSI'] > 70) &
(signals['bollinger'] > 1))] = 0
signals['id'].ffill(inplace=True) #fill empty values with 0
signals['id'].fillna(0, inplace=True)
signals['buySell'] = signals['id'].diff()
signals['buySell'].fillna(0, inplace=True)
###################################################################################
# Code taken from Willems, K., 2019. (Tutorial) Python For Finance: Algorithmic Trading
initInvestment = 100000
stocksOwned = pd.DataFrame(index=signals.index).fillna(0.0)
noCur = 10 #No of currency to be purchased
stocksOwned['BTC'] = noCur * signals['id']
portfolio = pd.DataFrame(index=signals.index)
portfolio['Holdings'] = stocksOwned['BTC'].multiply(btc['Close'], axis=0)
buySell = stocksOwned['BTC'].diff()
portfolio['cash'] = initInvestment - (buySell.multiply(btc['Close'],
axis=0)).cumsum()
portfolio['total'] = portfolio['cash'] + portfolio['Holdings']
portfolio['cash'][0] = initInvestment
portfolio['total'][0] = initInvestment
###################################################################################
# In[50]:
fig, (ax) = plt.subplots(1, 1, sharex=True)
ax.plot(portfolio.index, portfolio['total'], label='Price')
ax.set_xlabel('Date')
ax.set_ylabel('Value of portfolio in USD')
day = signals.loc[signals.buySell == 1.0].index
day2 = signals.loc[signals.buySell == -1.0].index
ax.scatter(x=day, y=portfolio.loc[day, 'total'], color='green')
ax.scatter(x=day2, y=portfolio.loc[day2, 'total'], color='red')
plt.show()
