def test_fi2(self):
target = 'FI'
result = ForceIndexIndicator(close=self._df['Close'],
volume=self._df['Volume'],
n=13,
fillna=False).force_index()
pd.testing.assert_series_equal(self._df[target].tail(),
result.tail(),
check_names=False)
def setUpClass(cls):
cls._df = pd.read_csv(cls._filename, sep=",")
cls._params = dict(close=cls._df["Close"],
volume=cls._df["Volume"],
window=13,
fillna=False)
cls._indicator = ForceIndexIndicator(**cls._params)
def setUpClass(cls):
cls._df = pd.read_csv(cls._filename, sep=',')
cls._params = dict(close=cls._df['Close'],
volume=cls._df['Volume'],
n=13,
fillna=False)
cls._indicator = ForceIndexIndicator(**cls._params)
def add_volume_ta(df: pd.DataFrame, high: str, low: str, close: str, volume: str,
fillna: bool = False, colprefix: str = "") -> pd.DataFrame:
"""Add volume technical analysis features to dataframe.
Args:
df (pandas.core.frame.DataFrame): Dataframe base.
high (str): Name of 'high' column.
low (str): Name of 'low' column.
close (str): Name of 'close' column.
volume (str): Name of 'volume' column.
fillna(bool): if True, fill nan values.
colprefix(str): Prefix column names inserted
Returns:
pandas.core.frame.DataFrame: Dataframe with new features.
"""
# Accumulation Distribution Index
df[f'{colprefix}volume_adi'] = AccDistIndexIndicator(
high=df[high], low=df[low], close=df[close], volume=df[volume], fillna=fillna).acc_dist_index()
# On Balance Volume
df[f'{colprefix}volume_obv'] = OnBalanceVolumeIndicator(
close=df[close], volume=df[volume], fillna=fillna).on_balance_volume()
# Chaikin Money Flow
df[f'{colprefix}volume_cmf'] = ChaikinMoneyFlowIndicator(
high=df[high], low=df[low], close=df[close], volume=df[volume], fillna=fillna).chaikin_money_flow()
# Force Index
df[f'{colprefix}volume_fi'] = ForceIndexIndicator(
close=df[close], volume=df[volume], n=13, fillna=fillna).force_index()
# Money Flow Indicator
df[f'{colprefix}volume_mfi'] = MFIIndicator(
high=df[high], low=df[low], close=df[close], volume=df[volume], n=14, fillna=fillna).money_flow_index()
# Ease of Movement
indicator = EaseOfMovementIndicator(high=df[high], low=df[low], volume=df[volume], n=14, fillna=fillna)
df[f'{colprefix}volume_em'] = indicator.ease_of_movement()
df[f'{colprefix}volume_sma_em'] = indicator.sma_ease_of_movement()
# Volume Price Trend
df[f'{colprefix}volume_vpt'] = VolumePriceTrendIndicator(
close=df[close], volume=df[volume], fillna=fillna).volume_price_trend()
# Negative Volume Index
df[f'{colprefix}volume_nvi'] = NegativeVolumeIndexIndicator(
close=df[close], volume=df[volume], fillna=fillna).negative_volume_index()
# Volume Weighted Average Price
df[f'{colprefix}volume_vwap'] = VolumeWeightedAveragePrice(
high=df[high], low=df[low], close=df[close], volume=df[volume], n=14, fillna=fillna
).volume_weighted_average_price()
return df
def handler(event, context):
# DynamoDb
dynamodb = boto3.resource('dynamodb')
tableName = os.environ['DATABASE']
table = dynamodb.Table(tableName)
# Get stock data
stock = yf.Ticker('^GSPC')
stock_data_weekly = stock.history(period="5y", interval="5d")
stock_data_daily = stock.history(period="2y", interval="1d")
# Calculate impulse system weekly
macd_hist = MACD(stock_data_weekly["Close"], fillna=True).macd_diff()
ema = EMAIndicator(
stock_data_weekly["Close"], n=13, fillna=True).ema_indicator()
shouldBeGreen = ema.iloc[-1] > ema.iloc[-2] and macd_hist.iloc[-1] > macd_hist.iloc[-2]
shouldBeRed = ema.iloc[-1] < ema.iloc[-2] and macd_hist.iloc[-1] < macd_hist.iloc[-2]
colorWeekly = 'green' if shouldBeGreen else 'red' if shouldBeRed else 'blue'
# Calculate impulse system daily
macd_hist = MACD(stock_data_daily["Close"], fillna=True).macd_diff()
ema = EMAIndicator(
stock_data_daily["Close"], n=13, fillna=True).ema_indicator()
shouldBeGreen = ema.iloc[-1] > ema.iloc[-2] and macd_hist.iloc[-1] > macd_hist.iloc[-2]
shouldBeRed = ema.iloc[-1] < ema.iloc[-2] and macd_hist.iloc[-1] < macd_hist.iloc[-2]
colorDaily = 'green' if shouldBeGreen else 'red' if shouldBeRed else 'blue'
# Caculate ForceIndex 13 days
indicator_fi = ForceIndexIndicator(
stock_data_daily["Close"], stock_data_daily["Volume"], 13, fillna=True).force_index()
lastForceIndexValue = indicator_fi.iloc[-1]
lastFIDecimal = Decimal(lastForceIndexValue)
lastFI = round(lastFIDecimal, 0)
lastCloseData = stock_data_daily.iloc[-1]["Close"]
lastCloseDecimal = Decimal(lastCloseData)
lastClose = round(lastCloseDecimal, 2)
# daily timestamp
date = datetime.utcnow()
future = date + timedelta(days=14)
expiryDate = round(future.timestamp() * 1000)
dateString = date.strftime("%Y-%m-%d")
id = str(uuid.uuid4())
entry = {
'symbol': '^GSPC',
'id': id,
'date': dateString,
'ttl': expiryDate,
'weeklyImpulse': colorWeekly,
'dailyImpulse': colorDaily,
'forceIndex13': lastFI,
'lastClose': lastClose,
}
table.put_item(Item=entry)
return entry
def handler(event, context):
# Setup dynamodb table
dynamodb = boto3.resource('dynamodb')
tableName = os.environ['DATABASE']
table = dynamodb.Table(tableName)
# Get stock data
stock = yf.Ticker(event['symbol'])
if (event['date']):
endDate = datetime.strptime(event['date'], "%Y-%m-%d")
else:
endDate = datetime.utcnow()
startDate = endDate - timedelta(weeks=156)
stock_data = stock.history(end=endDate, start=startDate, interval="1d")
# Get Force Index result
indicator_fi = ForceIndexIndicator(stock_data["Close"],
stock_data["Volume"],
2,
fillna=True).force_index()
shouldGoLong = indicator_fi.iloc[-1] < 0 and indicator_fi.iloc[-2] > 0
dailyData = stock_data.iloc[-1]
openValue = dailyData["Open"]
openPrice = Decimal(openValue)
roundedOpenPrice = round(openPrice, 2)
highValue = dailyData["High"]
highPrice = Decimal(highValue)
roundedHighPrice = round(highPrice, 2)
lowValue = dailyData["Low"]
lowPrice = Decimal(lowValue)
roundedLowPrice = round(lowPrice, 2)
closeValue = dailyData["Close"]
closePrice = Decimal(closeValue)
roundedClosePrice = round(closePrice, 2)
volumeValue = dailyData["Volume"]
volumePrice = Decimal(volumeValue)
roundedVolumePrice = round(volumePrice, 2)
dailyEntry = {
'type': 'force_index',
'result': 'long' if shouldGoLong else 'short',
'open': roundedOpenPrice,
'high': roundedHighPrice,
'low': roundedLowPrice,
'close': roundedClosePrice,
'volume': roundedVolumePrice
}
# Write to database
table.update_item(Key={
'symbol': event['symbol'],
'date': event['date']
},
UpdateExpression='SET daily = :dailyEntry',
ExpressionAttributeValues={':dailyEntry': dailyEntry})
# Return for next step in step functions
return dailyEntry