def renko_DF(DF):
"function to convert ohlc data into renko bricks"
df = DF.copy()
df.reset_index(inplace=True)
df = df.iloc[:, [0, 1, 2, 3, 4, 5]]
df.columns = ["date", "open", "high", "low", "close", "volume"]
df2 = Renko(df)
df2.brick_size = max(0.5, round(ATR(DF, 120)["ATR"][-1], 0))
df2.chart_type = Renko.PERIOD_CLOSE
renko_df = df2.get_ohlc_data()
renko_df["bar_num"] = np.where(
renko_df["uptrend"] == True, 1,
np.where(renko_df["uptrend"] == False, -1, 0)
) #Add a column where put 1 and -1 instead of the True/False we had in the uptrend column
for i in range(
1, len(renko_df["bar_num"])
): #Counts the amount of blocks generated in a trend Ex.: If we have 1,1,1,1,-1,-1,1,1... --> 1,2,3,4,-1,-2,1,2...
if renko_df["bar_num"][i] > 0 and renko_df["bar_num"][i - 1] > 0:
renko_df["bar_num"][i] += renko_df["bar_num"][i - 1]
elif renko_df["bar_num"][i] < 0 and renko_df["bar_num"][i - 1] < 0:
renko_df["bar_num"][i] += renko_df["bar_num"][i - 1]
renko_df.drop_duplicates(
subset="date", keep="last", inplace=True
) #Some of the indexes are repeated (for example if there is a spike in a 5 min period and 2 ranko blocks are generated). Drop the duplicate indexes (same 5 min period), but keep the last
return renko_df
def renko_DF(DF):
"function to convert ohlc data into renko bricks"
df = DF.copy()
df.reset_index(inplace=True)
df = df.iloc[:, [0, 1, 2, 3, 4, 5]]
df.columns = ["date", "open", "high", "low", "close", "volume"]
df2 = Renko(df)
df2.brick_size = max(0.5, round(ATR(DF, 120)["ATR"][-1], 0)) #120 days ATR
renko_df = df2.get_ohlc_data()
#button, if its uptrend then 1, if downtrend then -1
renko_df["bar_num"] = np.where(
renko_df["uptrend"] == True, 1,
np.where(renko_df["uptrend"] == False, -1, 0))
#adding the bar_num to the prev number if they are in the same direction
#if bar_num[i] = 1 && bar_num[i-1] = 1, add them
#if they are negative subtract, -1 -- 1 = 0
for i in range(1, len(renko_df["bar_num"])):
if renko_df["bar_num"][i] > 0 and renko_df["bar_num"][i - 1] > 0:
renko_df["bar_num"][i] += renko_df["bar_num"][i - 1]
elif renko_df["bar_num"][i] < 0 and renko_df["bar_num"][i - 1] < 0:
renko_df["bar_num"][i] += renko_df["bar_num"][i - 1]
renko_df.drop_duplicates(subset="date", keep="last", inplace=True)
#this removes all the duplicate values, if there are duplicate roll indicies,
#and keep the prev number (for spikes)
return renko_df
def renko_df(df_raw, ATR):
df_raw = df_raw[-500:]
df_raw.reset_index(inplace=True)
renko = Renko(df_raw[['date', 'open', 'high', 'low', 'close', 'volume']])
renko.brick_size = ATR
df = renko.get_ohlc_data()
df = df.copy()
df['bar_num'] = np.where(df['uptrend'] == True, 1,
np.where(df['uptrend'] == False, -1, 0))
for i in range(1, len(df["bar_num"])):
if df["bar_num"][i] > 0 and df["bar_num"][i - 1] > 0:
df["bar_num"][i] += df["bar_num"][i - 1]
elif df["bar_num"][i] < 0 and df["bar_num"][i - 1] < 0:
df["bar_num"][i] += df["bar_num"][i - 1]
df.drop_duplicates(subset="date", keep="last", inplace=True)
df_raw = df_raw.merge(df.loc[:, ["date", "bar_num"]],
how="outer",
on="date")
df_raw["bar_num"].fillna(method='ffill', inplace=True)
df_raw['OBV'] = ta.OBV(df_raw['close'], df_raw['volume'])
df_raw["obv_slope"] = slope(df_raw['OBV'], 5)
return df_raw
def get_basic_renko(stock_df, period):
"function to convert ohlc data into renko bricks"
df = stock_df.copy()
df.reset_index(inplace=True)
df = df.iloc[:,[0,1,2,3,4,5]]
df.columns = ["date","open","high","low","close","volume"]
df2 = Renko(df)
# completely not sure about this renko brick calculation
atr_df = get_ATR_column_for_stock(stock_df, 120, False)
df2.brick_size = max(0.5, round(atr_df["ATR"][-1],0))
print(f'brick size: {df2.brick_size}')
renko_df = df2.get_ohlc_data()
renko_df["bar_num"] = np.where(renko_df["uptrend"]==True,1,np.where(renko_df["uptrend"]==False,-1,0))
for i in range(1,len(renko_df["bar_num"])):
if renko_df["bar_num"][i]>0 and renko_df["bar_num"][i-1]>0:
renko_df["bar_num"][i]+=renko_df["bar_num"][i-1]
elif renko_df["bar_num"][i]<0 and renko_df["bar_num"][i-1]<0:
renko_df["bar_num"][i]+=renko_df["bar_num"][i-1]
renko_df.drop_duplicates(subset="date",keep="last",inplace=True)
renko_df.columns = ["Date","open","high","low","close","uptrend","bar_num"]
df_to_merge = stock_df.copy()
df_to_merge["Date"] = df_to_merge.index
df_to_merge = df_to_merge.merge(renko_df.loc[:,["Date","bar_num"]],how="outer",on="Date")
df_to_merge.set_index('Date', inplace=True)
df_to_merge["bar_num"].fillna(method='ffill',inplace=True)
return df_to_merge['bar_num']
def renko_DF(DF):
"function to convert ohlc data into renko bricks"
try:
print("Nishant")
df = DF.copy()
df.reset_index(inplace=True)
df = df.iloc[:,[0,1,2,3,4,5]]
df.columns = ["date","open","high","low","close","volume"]
df2 = Renko(df)
# TODO: Investigate what brick size is good
df2.brick_size = max(5, round(Indicator.ATR(DF, 14)["ATR"][-1], 0))
print("Brick size: ", df2.brick_size)
renko_df = df2.get_ohlc_data()
renko_df["bar_num"] = np.where(renko_df["uptrend"] == True, 1, np.where(renko_df["uptrend"] == False, -1, 0))
for i in range(1,len(renko_df["bar_num"])):
if renko_df["bar_num"][i] > 0 and renko_df["bar_num"][i-1] > 0:
renko_df["bar_num"][i] += renko_df["bar_num"][i-1]
elif renko_df["bar_num"][i] < 0 and renko_df["bar_num"][i-1] < 0:
renko_df["bar_num"][i] += renko_df["bar_num"][i-1]
renko_df.drop_duplicates(subset="date",keep="last", inplace=True)
return renko_df
except :
print("Failed to get renko")
def renko(df_original):
df = df_original.copy()
df.reset_index(inplace=True) # convert index to column
df.drop(['Close'], axis=1)
df.columns = ['date', 'open', 'high', 'low', 'close', 'volume'] # change column names
renko_df = Renko(df)
renko_df.brick_size = int(round(atr(df_original, 14)['atr'][-1], 0))
return renko_df.get_ohlc_data()
def renko_DF(DF):
df = DF.copy()
df.reset_index(inplace=True)
df = df.iloc[:,[0,1,2,3,4,5]]
df.columns = ["date", "low", "high", "open", "close", "volume"]
df2 = Renko(df)
df2.brick_size = max(0.5, round(ATR(DF,120)["ATR"][-1],0))
def data_frame(data, size, period1, period2):
df = pd.DataFrame(data)
df['date'], df['time'] = df['date'].dt.date, df['date'].dt.strftime('%H:%M:%S')
df = Renko(df)
df.brick_size = size
data = df.get_bricks()
data['MA4'] = data['close'].rolling(window=period1).mean()
data['MA10'] = data['close'].rolling(window=period2).mean()
return data
def renko_DF(DF):
"function to convert ohlc data into renko bricks"
df = DF.copy()
df.reset_index(inplace=True)
df = df.iloc[:,[0,1,2,3,5,6]]
df.rename(columns = {"Date" : "date", "High" : "high","Low" : "low", "Open" : "open","Adj Close" : "close", "Volume" : "volume"}, inplace = True)
df2 = Renko(df)
df2.brick_size = round(ATR(DF,120)["ATR"][-1],0)
renko_df = df2.get_ohlc_data() #if using older version of the library please use get_bricks() instead
return renko_df
def Renko_trend(DF, n):
df8 = DF.copy()
df8 = df8.reset_index()
df8 = df8.iloc[:, [0, 1, 2, 3, 4]]
df8.columns = ['date', 'open', 'high', 'low', 'close']
renko_df = Renko(df8)
x = statistics.mean(((ATR(ohclv_data, 200)).iloc[:, -1].dropna()))
renko_df.brick_size = x
df8 = renko_df.get_ohlc_data()
return df8
def renko_DF(DF):
df = DF.copy()
df.reset_index(inplace=True)
df = df.iloc[:,[0,1,2,3,5,6]]
df.rename(columns = {"Date" : "date", "High" : "high","Low" : "low", "Open" :
"open","Adj Close" : "close", "Volume" : "volume"},
inplace = True)
df2 = Renko(df)
df2.brick_size = round(ATR(DF,120)["ATR"][-1],0)
renko_df = df2.get_ohlc_data()
return renko_df
def renko_df(dataframe):
"""function to convert ohlc data into renko bricks"""
df_ = dataframe.copy()
df_.reset_index(inplace=True)
df_ = df_.iloc[:, [0, 1, 2, 3, 5, 6]]
df_.rename(columns={"Date": "date", "High": "high", "Low": "low", "Open": "open", "Adj Close": "close",
"Volume": "volume"}, inplace=True)
df2 = Renko(df_)
df2.brick_size = round(atr(dataframe, 120)["ATR"][-1], 0)
# renko_df = df2.get_bricks() # if get_bricks() does not work try using get_ohlc_data() instead
RENKO_df = df2.get_ohlc_data()
return RENKO_df
def GetRenko(DF, n):
df = DF.copy()
df.reset_index(inplace=True)
df = df.iloc[:, [0, 1, 2, 3, 5, 6]]
df.columns = ["date", "open", "high", "low", "close", "volume"]
renko_df = Renko(df)
renko_df.brick_size = round(ATR(DF, 120)['ATR'][-1], 0)
df2 = renko_df.get_bricks()
return df2
def renko_DF(df_):
""" converts ohlc data into renko bricks
must include the following columns: 'date' 'open' 'high' 'low' 'close'
"""
df = copy.deepcopy(df_)
if 'adj_close' in df.columns:
df['close'] = df['adj_close']
df.reset_index(inplace=True)
df2 = Renko(df)
df2.brick_size = round(ATR(df_, 120)['ATR'][-1], 0)
renko_df = df2.get_ohlc_data()
return renko_df
def __setIndicator(self, a_df: pandas.DataFrame):
"""function to convert ohlc data into renko bricks"""
df: pandas.DataFrame = a_df.copy()
df.reset_index(inplace=True)
df = df.iloc[:, [0, 1, 2, 3, 5, 6]]
df.rename(columns={"Date": "date", "High": "high", "Low": "low", "Open": "open", "Adj Close": "close",
"Volume": "volume"}, inplace=True)
ren = Renko(df)
ren.brick_size = round(self.__getAvgTrueRange(a_df.copy(), 120)["AvgTrueRate"][-1], 0)
# if get_bricks() does not work try using get_ohlc_data() instead
# df2.get_bricks() error => using get_ohlc_data()
# renkoDataFrame = df2.get_bricks()
renDf: pandas.DataFrame = ren.get_ohlc_data()
self.IndicatorDf = renDf
def renko_DF(DF):
"function to convert ohlc data into renko bricks"
df = DF.copy()
df.reset_index(inplace=True)
df = df.iloc[:, [0, 1, 2, 3, 6, 5]]
df.columns = ["date", "open", "high", "low", "close", "volume"]
df2 = Renko(df)
df2.brick_size = round(
ATR(DF, 120)["ATR"][-1], 0
) #We just want the last number of the column. We use ATR as the brick size in here but it can also be fix.
df2.chart_type = Renko.PERIOD_CLOSE #Renko box calcuation based on periodic close
#df2.chart_type = Renko.PRICE_MOVEMENT #Renko box calcuation based on price movement
renko_df = df2.get_ohlc_data(
) #Adds a column to df called "uptrend" with boolean values depending on whether the ranko chart goes up or down
return renko_df
def renko_DF(DF):
"function to convert ohlc data into renko bricks"
df = DF.copy()
df.reset_index(inplace=True)
df = df.iloc[:, [0, 1, 2, 3, 4, 5]]
df.columns = ["date", "open", "high", "low", "close", "volume"]
df2 = Renko(df)
df2.brick_size = max(0.5, round(ATR(DF, 120)["ATR"][-1], 0))
renko_df = df2.get_bricks()
renko_df["bar_num"] = np.where(renko_df["uptrend"] == True, 1, np.where(renko_df["uptrend"] == False, -1, 0))
for i in range(1, len(renko_df["bar_num"])):
if renko_df["bar_num"][i] > 0 and renko_df["bar_num"][i - 1] > 0:
renko_df["bar_num"][i] += renko_df["bar_num"][i - 1]
elif renko_df["bar_num"][i] < 0 and renko_df["bar_num"][i - 1] < 0:
renko_df["bar_num"][i] += renko_df["bar_num"][i - 1]
renko_df.drop_duplicates(subset="date", keep="last", inplace=True)
return renko_df
def renko_bars(self, DF, n, instrument):
df = DF.copy()
df2 = Renko(df)
real = talib.ATR(df["high"], df["low"], df["close"], timeperiod = n)
brick_sizing = round(real[len(real) - 1], 5)
print("BRICK_SIZE: ", brick_sizing)
df2.brick_size = brick_sizing
renko_df = df2.get_bricks()
renko_df["bar_num"] = np\
.where(renko_df["uptrend"]==True,1,np\
.where(renko_df["uptrend"]==False,-1,0))
for i in range(1,len(renko_df["bar_num"])):
if renko_df["bar_num"][i]>0 and renko_df["bar_num"][i-1]>0:
renko_df["bar_num"][i]+=renko_df["bar_num"][i-1]
elif renko_df["bar_num"][i]<0 and renko_df["bar_num"][i-1]<0:
renko_df["bar_num"][i]+=renko_df["bar_num"][i-1]
renko_df.drop_duplicates(subset="date",keep="last",inplace=True)
return renko_df, brick_sizing
def renko_DF(DF):
df = DF.copy()
df2 = Renko(df)
"Using ATR as renko brick size"
real = talib.ATR(df["high"], df["low"], df["close"], timeperiod=14)
brick_sizing = round(real[len(real) - 1], 6)
df2.brick_size = brick_sizing
renko_df = df2.get_bricks()
renko_df["bar_num"] = np\
.where(renko_df["uptrend"]==True,1,np\
.where(renko_df["uptrend"]==False,-1,0))
for i in range(1, len(renko_df["bar_num"])):
if renko_df["bar_num"][i] > 0 and renko_df["bar_num"][i - 1] > 0:
renko_df["bar_num"][i] += renko_df["bar_num"][i - 1]
elif renko_df["bar_num"][i] < 0 and renko_df["bar_num"][i - 1] < 0:
renko_df["bar_num"][i] += renko_df["bar_num"][i - 1]
renko_df.drop_duplicates(subset="date", keep="last", inplace=True)
return renko_df
def renko_DF(DF):
"function to convert ohlc data into renko bricks"
df = DF.copy()
df.reset_index(inplace=True)
df2 = Renko(df)
atr_df = ATR(DF, 120)
#print(atr_df["ATR"])
df2.brick_size = max(0.5, round(atr_df["ATR"].iloc[-1], 0))
renko_df = df2.get_ohlc_data()
renko_df["bar_num"] = np.where(
renko_df["uptrend"] == True, 1,
np.where(renko_df["uptrend"] == False, -1, 0))
for i in range(1, len(renko_df["bar_num"])):
if renko_df["bar_num"][i] > 0 and renko_df["bar_num"][i - 1] > 0:
renko_df["bar_num"][i] += renko_df["bar_num"][i - 1]
elif renko_df["bar_num"][i] < 0 and renko_df["bar_num"][i - 1] < 0:
renko_df["bar_num"][i] += renko_df["bar_num"][i - 1]
renko_df.drop_duplicates(subset="date", keep="last", inplace=True)
return renko_df
def renko_DF(DF):
df = DF.copy()
df.reset_index(inplace=True)
df = df.iloc[:, [0, 1, 2, 3, 4, 5]] #different from 40mycode
df.columns = ['date', 'open', 'high', 'low', 'close', 'volume']
df2 = Renko(df)
df2.brick_size = round(ATR(DF, 120)['ATR'][-1],
4) #different from 40mycode
renko_df = df2.get_ohlc_data()
renko_df['bar_num'] = np.where(
renko_df['uptrend'] == True, 1,
np.where(renko_df['uptrend'] == False, -1, 0))
for i in range(1, len(renko_df['bar_num'])):
if renko_df['bar_num'][i] > 0 and renko_df['bar_num'][i - 1] > 0:
renko_df['bar_num'][i] += renko_df['bar_num'][i - 1]
elif renko_df['bar_num'][i] < 0 and renko_df['bar_num'][i - 1] < 0:
renko_df['bar_num'][i] += renko_df['bar_num'][i - 1]
renko_df.drop_duplicates(subset='date', keep='last', inplace=True)
return renko_df
def renko_dataframe(DF):
df = DF.copy()
df.reset_index(inplace=True)
#make index as a seperate column, convert index to column
#adding this allows the date to be its own column
#For renko, all we need to look at is a couple rows
df = df.iloc[:, [0, 1, 2, 3, 5, 6]]
#only include columns 0,1,2,3,5,6
df.columns = ["date", "open", "high", "low", "close", "volume"]
#simply rename the columns, respectively
renko_df = Renko(df)
renko_df.brick_size = round(ATR(
df, 120)["ATR"][-1]) #use atr to assign bricksize
#-1 takes the latest point/last point
df2 = renko_df.get_ohlc_data()
def renko_DF(DF):
"function to convert ohlc data into renko bricks"
df = DF.copy() # copio el dataframe
df.reset_index(
inplace=True
) # pongo un indice secuencial y la fecha pasa a la primera columna
df = df.iloc[:, [0, 1, 2, 3, 5, 6]] # Quito la colunna 4
df.rename(columns={
"Date": "date",
"High": "high",
"Low": "low",
"Open": "open",
"Adj Close": "close",
"Volume": "volume"
},
inplace=True)
df2 = Renko(df)
df2.brick_size = round(
ATR(DF, 120)["ATR"][-1],
0) #marcamos el incremento basado en ATR para pintar un brick
renko_df = df2.get_ohlc_data(
) #if using older version of the library please use get_bricks() instead
return renko_df
def renko(Df):
'''
Converts ohlc data to Renko bricks
Parameters
==========
Df: pandas.DataFrame(): enter a regular OHLCv dataframe with pandas.Date index
Returns
=======
Renko_df: pandas.DataFrame(): Renko bric indicators
'''
df = Df.copy()
df.reset_index(inplace=True)
df = df.iloc[:, [0, 1, 2, 3, 4, 5]]
df.rename(columns={
'Date': 'date',
'Open': 'open',
'High': 'high',
'Low': 'low',
'Adj Close': 'close',
'Volume': 'volume'
},
inplace=True)
df2 = Renko(df)
df2.brick_size = max(0.5, round(ATR(Df, 120)['ATR'][-1], 0))
renko_df = df2.get_ohlc_data()
renko_df['bar_num'] = np.where(
renko_df['uptrend'] == True, 1,
np.where(renko_df['uptrend'] == False, -1, 0))
for i in range(1, len(renko_df['bar_num'])):
if renko_df['bar_num'][i] > 0 and renko_df['bar_num'][i - 1] > 0:
renko_df['bar_num'][i] += renko_df['bar_num'][i - 1]
elif renko_df['bar_num'][i] < 0 and renko_df['bar_num'][i - 1] < 0:
renko_df['bar_num'][i] += renko_df['bar_num'][i - 1]
renko_df.drop_duplicates(subset='date', keep='last', inplace=True)
return renko_df
def renko(dataframe):
"""atr
A Renko chart is built using price movement without standardized time intervals. A new brick is created when the
price moves a specified price amount. Each block is positioned at a 45-degree angle (up or down) to the prior brick.
An up brick is typically colored white or green, while a down brick is typically colored black or red. Brick size
is determined by ATR.
Args:
dataframe (pd.Dataframe): Dataframe with ohlcv data.
Returns:
pd.Dataframe: Frame with renko col appended.
"""
df = dataframe.copy()
brick_size = max(0.5, round(atr(df, 120)['atr'][-1], 0))
df.reset_index(inplace=True)
df_renko = Renko(col_rename(df))
df_renko.brick_size = brick_size
df_ohlc_renko = df_renko.get_ohlc_data()
df_ohlc_renko["renko_bar_num"] = np.where(df_ohlc_renko["uptrend"], 1,
np.where(df_ohlc_renko["uptrend"] == False, -1, 0))
for i in range(1, len(df_ohlc_renko["renko_bar_num"])):
if df_ohlc_renko["renko_bar_num"][i] > 0 and df_ohlc_renko["renko_bar_num"][i-1] > 0:
df_ohlc_renko["renko_bar_num"][i] += df_ohlc_renko["renko_bar_num"][i-1]
elif df_ohlc_renko["renko_bar_num"][i] < 0 and df_ohlc_renko["renko_bar_num"][i-1] < 0:
df_ohlc_renko["renko_bar_num"][i] += df_ohlc_renko["renko_bar_num"][i-1]
df_ohlc_renko.drop_duplicates(subset="date", keep="last", inplace=True)
df_merged = df.merge(df_ohlc_renko.loc[:, ["date", "renko_bar_num"]], how="outer", on="date")
df_merged["renko_bar_num"].fillna(method='ffill', inplace=True)
df_merged.set_index('date', inplace=True)
return df_merged[['renko_bar_num']]
def test_renko():
renko = Renko(df)
renko.brick_size = 4
cdf = renko.get_ohlc_data()
assert cdf['close'].equals(rdf['close'])
def ATR(DF,n=20):
temp = DF.copy()
temp["H-L"] = abs(temp["High"] - temp["Low"])
temp["H-PC"] = abs(temp["High"] - temp["Adj Close"].shift(1)) # high - previous day close
temp["L-PC"] = abs(temp["Low"] - temp["Adj Close"].shift(1))
temp["TR"] = temp[["H-L", "H-PC", "L-PC"]].max(axis=1, skipna = False)
temp["ATR"] = temp["TR"].rolling(n).mean()
return_val = temp.drop(["H-L","H-PC","L-PC"], axis=1)
return return_val
df.reset_index(inplace = True)
df = df.drop(["Close"], axis=1)
df.columns = ["date", "high", "low", "open", "volume", "close"]
renko_df = Renko(df)
renko_df.brick_size = 10
renko_df.chart_type = Renko.PERIOD_CLOSE
df2 = renko_df.get_ohlc_data()
df2.set_index("date", inplace = True)
def renko_DF(DF):
df = DF.copy()
df.reset_index(inplace=True)
df = df.iloc[:,[0,1,2,3,4,5]]
df.columns = ["date", "low", "high", "open", "close", "volume"]
df2 = Renko(df)
df2.brick_size = max(0.5, round(ATR(DF,120)["ATR"][-1],0))
import pandas_datareader as pdr
def renko():
renko_data = Renko(transform_data(SetUp.data))
renko_data.brick_size = round(atr(SetUp.data, 120)['ATR'][-1], 0)
renko_df = renko_data.get_ohlc_data()
return renko_df
import numpy as np
import pandas as pd
import nsepy
from stocktrends import Renko
if len(sys.argv) > 1:
fname = sys.argv[1]
print('Reading local file {}'.format(fname))
df = pd.read_csv(sys.argv[1])
else:
symbol='SBIN'
print('Downloading {} data from nsepy'.format(symbol))
df = nsepy.get_history(
symbol=symbol,
start=dt.date(2017,1,1),
end=dt.date(2018,1,19)
)
if df.empty:
print('No data is received from nsepy.')
sys.exit()
debug = True
# print(df.tail(20))
renko = Renko(df)
renko.brick_size = 4
r = renko.get_bricks()
print(r.tail(33))
