def animate(i, symbol):
print(symbol)
df = dd.read_csv(
'./{0}_*.csv'.format(symbol),
header=None,
names=['DateTime', 'Ticker', 'Open', 'High', 'Low', 'Close', 'Volume'])
df['Volume'] = df['Volume'].apply(value_to_float)
df = df.compute()
#Read the FB_Max and FB_Min from file
dfFB = pd.read_csv('./FB_Max_Min.csv', header=0)
price_max = dfFB['FB_Max'].tail(1).values[0]
price_min = dfFB['FB_Min'].tail(1).values[0]
if (price_max == 0.0 and price_min == 0.0):
#price_max = df['Close'].max()
#price_min = df['Close'].min()
price_max = 2 * (df['Close'].max() + df['Close'].min() + df['Close'].
tail(1).values[0]) / 3 - df['Close'].min()
price_min = 2 * (df['Close'].max() + df['Close'].min() + df['Close'].
tail(1).values[0]) / 3 - df['Close'].max()
# Fibonacci Levels considering original trend as upward move
diff = price_max - price_min
level1 = price_max - 0.236 * diff
level2 = price_max - 0.382 * diff
level3 = price_max - 0.618 * diff
df['Volume_New'] = df['Volume'] - df['Volume'].shift(1)
df['Volume_New'] = df['Volume_New'].fillna(0)
df['Volume_New'] = np.where(df['Volume_New'] < 0, 0, df['Volume_New'])
#df['Volume_New'] = df['Volume_New']/1e6 # dollar volume in millions
#df['SMA(10)'] = df['Close'].rolling(window=10).mean()
#df['SMA(20)'] = df['Close'].rolling(window=20).mean()
#df['SMA(50)'] = df['Close'].rolling(window=50).mean()
df['SMA(150)'] = df['Close'].rolling(window=150).mean()
df['SMA(200)'] = df['Close'].rolling(window=200).mean()
#y = df['Close'].tail(60).values
#x = df.tail(60).index.values
#fit = np.polyfit(x, y, deg=1)
titleColor = 'red'
pctChange = 100 * (
df['Close'].tail(1).values[0] -
df['Open'].tail(1).values[0]) / df['Open'].tail(1).values[0]
if pctChange > 0:
titleColor = 'green'
elif pctChange == 0.0:
titleColor = 'black'
df2 = df[['DateTime', 'Open', 'High', 'Low', 'Close', 'Volume']]
print(df2.head(5))
#clear ax1, ax2
ax1.clear()
ax2.clear()
ax1.set_title('{0} ({1:.2f}%)'.format(symbol, pctChange),
color=titleColor,
fontsize=15)
#ax1.plot(x, fit[0] * x + fit[1], color='red', linewidth=5.0)
ax1.axhspan(level1, price_min, alpha=0.4, color='lightsalmon')
#ax1.axhspan(level2, level1, alpha=0.5, color='palegoldenrod')
ax1.axhspan(level2, level1, alpha=0.5, color='gold')
ax1.axhspan(level3, level2, alpha=0.5, color='palegreen')
ax1.axhspan(price_max, level3, alpha=0.5, color='powderblue')
#Plot Close, High as line
df.plot(y=['Close', 'High'], color=['Blue', 'Green'], ax=ax1)
#df.plot(y= ['SMA(10)', 'SMA(20)', 'SMA(50)', 'SMA(200)'], color=['Red', 'Yellow', 'Purple', 'Orange'], ax=ax1)
#df.plot(y= ['SMA(10)', 'SMA(50)', 'SMA(200)'], color=['Red', 'Purple', 'Orange'], ax=ax1)
df.plot(y=['SMA(150)', 'SMA(200)'], color=['Yellow', 'Purple'], ax=ax1)
yLast = df.tail(1)['Close'].values[0]
#print(yLast)
ax1.annotate('%0.3f' % yLast,
xy=(0.95, yLast),
xytext=(8, 0),
xycoords=('axes fraction', 'data'),
textcoords='offset points')
ax1.axhline(y=yLast, color='y', linestyle='-.')
ax1.legend(loc='upper left')
#Plot Volume as positive and negative bar
#df['Volume']=df['Volume'].loc[::10]
quotes = list(
zip(df.index.tolist(), df['Open'].tolist(), df['High'].tolist(),
df['Low'].tolist(), df['Close'].tolist(),
df['Volume_New'].tolist()))
bc = volume_overlay3(ax2,
quotes,
colorup='g',
colordown='r',
width=2.5,
alpha=1.0)
ax2.set_ylim(df['Volume_New'].min(), 5 * df['Volume_New'].max())
ax2.add_collection(bc)
# Formatter Class to eliminate weekend data gaps on chart
class MyFormatter(Formatter):
def __init__(self, datetimes, fmt='%Y-%m-%d %H:%M:%S'):
self.datetimes = datetimes
self.fmt = fmt
def __call__(self, x, pos=0):
'Return the label for time x at position pos'
ind = int(round(x))
#print(ind)
if ind >= len(self.datetimes) or ind < 0:
return ''
#print(self.datetimes[ind])
return self.datetimes[ind].strftime(self.fmt)
#return self.dates[ind]
dff = pd.to_datetime(df['DateTime'])
#print(type(dff))
formatter = MyFormatter(dff)
ax1.set_xticklabels(df["DateTime"].tolist(), rotation=15, ha='right')
ax1.xaxis.set_major_formatter(formatter)
#ax1.set_xlim(0, len(df)-1)
ax1.minorticks_on()
# Customize the major grid
ax1.grid(which='major', linestyle='-', linewidth='0.5', color='red')
# Customize the minor grid
ax1.grid(which='minor', linestyle=':', linewidth='0.5', color='black')
def animate(df, symbol, idx):
print(symbol)
#df = pd.read_csv('.\\{0}_{1}-{2}-{3}.csv'.format(symbol, EndDate.year, EndDate.month, EndDate.day), header=None, names=['DateTime','Ticker', 'Open', 'High', 'Low', 'Close', 'Volume'])
#df = dd.read_csv('./{0}_*.csv'.format(symbol), header=None, names=['DateTime','Ticker', 'Open', 'High', 'Low', 'Close', 'Volume'])
#df = df.compute()
#df = df.head(idx)
#idx= idx + 1
#dfFB = dd.read_csv('./FB_Max_Min.csv', header=None, names=['FB_Max', 'FB_Min'])
dfFB = pd.read_csv('./FB_Max_Min.csv', header=0)
price_max = dfFB['FB_Max'].tail(1).values[0]
price_min = dfFB['FB_Min'].tail(1).values[0]
#print('idx= {0}, price_max = {1}, price_min={2}'.format(idx, price_max, price_min))
if (price_max == 0.0 and price_min == 0.0):
#price_max = df['Close'].max()
#price_min = df['Close'].min()
price_max = 2 * (df['Close'].max() + df['Close'].min() + df['Close'].
tail(1).values[0]) / 3 - df['Close'].min()
price_min = 2 * (df['Close'].max() + df['Close'].min() + df['Close'].
tail(1).values[0]) / 3 - df['Close'].max()
# Fibonacci Levels considering original trend as upward move
diff = price_max - price_min
level1 = price_max - 0.236 * diff
level2 = price_max - 0.382 * diff
level3 = price_max - 0.618 * diff
#find OVB
#df['OVB'] = (df['Volume'] * (~df['Close'].diff().le(0) * 2 - 1)).cumsum()
df['Volume_New'] = df['Volume'] - df['Volume'].shift(1)
df['Volume_New'] = df['Volume_New'].fillna(0)
df['Volume_New'] = np.where(df['Volume_New'] < 0, 0, df['Volume_New'])
#df['Volume'] = df['Volume']/1e6 # dollar volume in millions
#df['SMA(10)'] = df['Close'].rolling(window=10).mean()
#df['SMA(20)'] = df['Close'].rolling(window=20).mean()
#df['SMA(50)'] = df['Close'].rolling(window=50).mean()
df['SMA(150)'] = df['Close'].rolling(window=150).mean()
df['SMA(200)'] = df['Close'].rolling(window=200).mean()
y = df['Close'].tail(60).values
x = df.tail(60).index.values
fit = np.polyfit(x, y, deg=1)
titleColor = 'red'
pctChange = 100 * (
df['Close'].tail(1).values[0] -
df['Open'].tail(1).values[0]) / df['Open'].tail(1).values[0]
if pctChange > 0.0:
titleColor = 'green'
elif pctChange == 0.0:
titleColor = 'black'
df2 = df[['DateTime', 'Open', 'High', 'Low', 'Close', 'Volume']]
print(df2.tail(5))
ax1.clear()
ax2.clear()
#ax1.set_title(symbol)
ax1.set_title('{0} ({1:.2f}%)'.format(symbol, pctChange),
color=titleColor,
fontsize=15,
fontweight='bold')
#ax1.plot(x, fit[0] * x + fit[1], color='red', linewidth=5.0)
ax1.axhspan(level1, price_min, alpha=0.4, color='lightsalmon')
#ax1.axhspan(level2, level1, alpha=0.5, color='palegoldenrod')
ax1.axhspan(level2, level1, alpha=0.5, color='gold')
ax1.axhspan(level3, level2, alpha=0.5, color='palegreen')
ax1.axhspan(price_max, level3, alpha=0.5, color='powderblue')
df.plot(y=['Close', 'High'], color=['Blue', 'Green'], ax=ax1)
#df.plot(y= ['OVB'], color=['Blue'], ax=ax2)
#quotes = list(zip(df.index.tolist(), df['Open'].tolist(), df['High'].tolist(), df['Low'].tolist(), df['Close'].tolist(), df['Volume'].tolist()))
#candlestick_ochl(ax1, quotes, colorup='b', colordown='g', width=0.2, alpha=1.0)
#df.plot(y= ['SMA(10)', 'SMA(20)', 'SMA(50)', 'SMA(200)'], color=['Red', 'Yellow', 'Purple', 'Orange'], ax=ax1)
df.plot(y=['SMA(150)', 'SMA(200)'], color=['Yellow', 'Purple'], ax=ax1)
yLast = df['Close'].tail(1).values[0]
#print(yLast)
#plt.annotate('%0.3f' % yLast, xy=(1, yLast), xytext=(8, 0), xycoords=('axes fraction', 'data'), textcoords='offset points')
#plt.axhline(y=yLast, color='y', linestyle='-.')
ax1.annotate('%0.3f' % yLast,
xy=(0.95, yLast),
xytext=(8, 0),
xycoords=('axes fraction', 'data'),
textcoords='offset points')
ax1.axhline(y=yLast, color='y', linestyle='-.')
# Plot Volume Chart
#df['Price * Volume'] = (df['Close'] * df['Volume'])/1e6 # dollar volume in millions
#vmax = df['Price * Volume'].max()
#df.plot(y= ['Price * Volume'], kind='bar', legend=False, color=['Orange'], ax=ax2)
#df['Volume'] = df['Volume']/1e6 # dollar volume in millions
#vmin = df['Volume'].min()
#vmax = df['Volume'].max()
#df['Volume']=df['Volume'].loc[::5]
#df.plot(y= ['Volume'], legend=False, color=['Orange'], ax=ax2)
#df.plot(y= ['Volume'], kind='bar', color=['Orange'], ax=ax2)
#ax2.set_ylim(vmin, 5*vmax)
#ax.set_yticks([])
#ax2.xaxis.label.set_visible(False)
# Plot Volume Chart
quotes = list(
zip(df.index.tolist(), df['Open'].tolist(), df['High'].tolist(),
df['Low'].tolist(), df['Close'].tolist(),
df['Volume_New'].tolist()))
#quotes = list(zip(df.index.tolist(), df['Open'].tolist(), df['High'].tolist(), df['Low'].tolist(), df['Close'].tolist(), df['Volume'].tolist()))
bc = volume_overlay3(ax2,
quotes,
colorup='g',
colordown='r',
width=2.5,
alpha=1.0)
ax2.set_ylim(df['Volume_New'].min(), 5 * df['Volume_New'].max())
#ax2.set_ylim(df['Volume'].min(), 5*df['Volume'].max())
ax2.add_collection(bc)
#vmin = df['Volume'].min()
#vmax = df['Volume'].max()
#df.plot(y= ['Volume'], legend=False, color=['Orange'], ax=ax2)
#df.plot(y= ['Volume'], kind='bar', color=['Orange'], ax=ax2)
#ax2.set_ylim(vmin, 3*vmax)
#ax.set_yticks([])
#ax2.xaxis.label.set_visible(False)
# Formatter Class to eliminate weekend data gaps on chart
class MyFormatter(Formatter):
def __init__(self, datetimes, fmt='%Y-%m-%d %H:%M:%S'):
self.datetimes = datetimes
self.fmt = fmt
def __call__(self, x, pos=0):
'Return the label for time x at position pos'
ind = int(round(x))
#print(ind)
if ind >= len(self.datetimes) or ind < 0:
return ''
#print(self.datetimes[ind])
return self.datetimes[ind].strftime(self.fmt)
#return self.dates[ind]
dff = pd.to_datetime(df['DateTime'])
#print(type(dff))
formatter = MyFormatter(dff)
ax1.set_xticklabels(df["DateTime"].tolist(), rotation=15, ha='right')
#ax2.set_xticklabels(df["DateTime"].tolist(), rotation=15, ha='right')
ax1.xaxis.set_major_formatter(formatter)
#ax2.xaxis.set_major_formatter(formatter)
#Don't set the xlim; commented by Anthony Lai on 2018-08-19 because price on chart will look better for the beginning and end
#ax1.set_xlim(0, len(df)-5)
ax1.minorticks_on()
#ax2.minorticks_on()
# Customize the major grid
ax1.grid(which='major', linestyle='-', linewidth='0.5', color='red')
#ax2.grid(which='major', linestyle='-', linewidth='0.5', color='red')
# Customize the minor grid
ax1.grid(which='minor', linestyle=':', linewidth='0.5', color='black')
#ax2.grid(which='minor', linestyle=':', linewidth='0.5', color='black')
ax1.legend(loc='upper left')
