def build(ohlcv: pd.DataFrame, **kwargs):
W = kwargs.get('W', 10)
ohlc = ohlcv[['open', 'high', 'low', 'close']]
lagged_ohlc = pd.concat(
[ohlc] + [make_lagged(ohlc, i) for i in range(1, W + 1)],
axis='columns',
verify_integrity=True,
sort=True,
join='inner'
)
ta = get_ta_features(ohlcv, TA_CONFIG)
return pd.concat([lagged_ohlc, ta], axis='columns', verify_integrity=True, sort=True, join='inner')
def build(ohlcv: pd.DataFrame, coinmetrics: pd.DataFrame, **kwargs):
W = kwargs.get('W', 10)
# ATSA - OHLC with 10-lag + TA
ohlc = ohlcv[['open', 'high', 'low', 'close']]
lagged_ohlc = pd.concat([ohlc] +
[make_lagged(ohlc, i) for i in range(1, W + 1)],
axis='columns',
verify_integrity=True,
sort=True,
join='inner')
ta = get_ta_features(ohlcv, TA_CONFIG)
# Lagged percent variation of OHLCV
ohlcv_pct = ohlcv[['open', 'high', 'low', 'close', 'volume']].pct_change()
ohlcv_pct.columns = ['{}_pct'.format(c) for c in ohlcv_pct.columns]
lagged_ohlcv_pct = pd.concat(
[ohlcv_pct] + [make_lagged(ohlcv_pct, i) for i in range(1, W + 1)],
axis='columns',
verify_integrity=True,
sort=True,
join='inner')
# TALib Patterns on OHLCV data
_patterns = get_talib_patterns(ohlcv)
ohlc_patterns = pd.DataFrame(index=ohlcv.index)
ohlc_patterns['talib_patterns_mean'] = _patterns.mean(axis=1)
ohlc_patterns['talib_patterns_sum'] = _patterns.sum(axis=1)
# Residual from STL Decomposition of OHLC data
ohlc_residuals = pd.DataFrame()
ohlc_residuals['open_resid'] = get_residual(ohlcv.open)
ohlc_residuals['high_resid'] = get_residual(ohlcv.high)
ohlc_residuals['low_resid'] = get_residual(ohlcv.low)
ohlc_residuals['close_resid'] = get_residual(ohlcv.close)
# SPLINES
# Use SPLINES to extract price information
ohlc_splines = pd.DataFrame(index=ohlcv.index)
# First derivative indicates slope
ohlc_splines['open_spl_d1'] = get_spline(ohlcv.open, 1)
ohlc_splines['high_spl_d1'] = get_spline(ohlcv.high, 1)
ohlc_splines['low_spl_d1'] = get_spline(ohlcv.low, 1)
ohlc_splines['close_spl_d1'] = get_spline(ohlcv.close, 1)
# Second derivative indicates convexity
ohlc_splines['open_spl_d2'] = get_spline(ohlcv.open, 2)
ohlc_splines['high_spl_d2'] = get_spline(ohlcv.high, 2)
ohlc_splines['low_spl_d2'] = get_spline(ohlcv.low, 2)
ohlc_splines['close_spl_d2'] = get_spline(ohlcv.close, 2)
# Relevant stats from OHLC data interpretation
ohlcv_stats = pd.DataFrame(index=ohlcv.index)
ohlcv_stats['close_open_pct'] = (ohlcv.close - ohlcv.open).pct_change(
) # Change in body of the candle (> 0 if candle is green)
ohlcv_stats['high_close_dist_pct'] = (ohlcv.high - ohlcv.close).pct_change(
) # Change in wick size of the candle, shorter wick should be bullish
ohlcv_stats['low_close_dist_pct'] = (ohlcv.close - ohlcv.low).pct_change(
) # Change in shadow size of the candle, this increasing would indicate support (maybe a bounce)
ohlcv_stats['high_low_dist_pct'] = (ohlcv.high - ohlcv.low).pct_change(
) # Change in total candle size, smaller candles stands for low volatility
ohlcv_stats['close_volatility_3d'] = ohlcv.close.pct_change().rolling(
3).std(ddof=0)
ohlcv_stats['close_volatility_7d'] = ohlcv.close.pct_change().rolling(
7).std(ddof=0)
ohlcv_stats['close_volatility_30d'] = ohlcv.close.pct_change().rolling(
30).std(ddof=0)
## Dropped these features because they exclude too much data! Enable if building with MORE DATA!
# if ohlcv.close.shape[0] > 90:
# ohlcv_stats['close_volatility_90d'] = ohlcv.close.pct_change().rolling(90).std(ddof=0)
# if ohlcv.close.shape[0] > 180:
# ohlcv_stats['close_volatility_180d'] = ohlcv.close.pct_change().rolling(180).std(ddof=0)
# if ohlcv.close.shape[0] > 360:
# ohlcv_stats['close_volatility_360d'] = ohlcv.close.pct_change().rolling(360).std(ddof=0)
# Stats from resampled OHLC data
for d in [3, 7, 30]:
ohlcv_d = ohlcv_resample(ohlcv=ohlcv, period=d, interval='D')
ohlcv_stats['close_open_pct_d{}'.format(d)] = (
ohlcv_d.close - ohlcv_d.open).pct_change()
ohlcv_stats['high_close_dist_pct_d{}'.format(d)] = (
ohlcv_d.high - ohlcv_d.close).pct_change()
ohlcv_stats['low_close_dist_pct_d{}'.format(d)] = (
ohlcv_d.close - ohlcv_d.low).pct_change()
ohlcv_stats['high_low_dist_pct_d{}'.format(d)] = (
ohlcv_d.high - ohlcv_d.low).pct_change()
# Cherry-picked and engineered features from technical indicators
ta_picks = pd.DataFrame(index=ta.index)
# REMA / RSMA are already used and well-estabilished in ATSA,
# I'm taking the pct change since i want to encode the relative movement of the ema's not their positions
# Drop other dimensions since they're correlated
ta_picks['rema_8_15_pct'] = ta.rema_8_15.pct_change()
ta_picks['rsma_8_15_pct'] = ta.rsma_8_15.pct_change()
# Stoch is a momentum indicator comparing a particular closing price of a security to a range of its prices
# over a certain period of time.
# The sensitivity of the oscillator to market movements is reducible by adjusting that time period or
# by taking a moving average of the result.
# It is used to generate overbought and oversold trading signals, utilizing a 0-100 bounded range of values.
# IDEA => decrease sensitivity by 3-mean and divide by 100 to get fp values
ta_picks['stoch_14_mean3_div100'] = ta.stoch_14.rolling(3).mean() / 100
# Moving Average Convergence Divergence (MACD) is a trend-following momentum indicator that shows
# the relationship between two moving averages of a security’s price.
# The MACD is calculated by subtracting the 26-period Exponential Moving Average (EMA) from the 12-period EMA.
# A nine-day EMA of the MACD called the "signal line," is then plotted on top of the MACD line,
# which can function as a trigger for buy and sell signals.
# Traders may buy the security when the MACD crosses above its signal line and sell - or short - the security
# when the MACD crosses below the signal line.
# Moving Average Convergence Divergence (MACD) indicators can be interpreted in several ways,
# but the more common methods are crossovers, divergences, and rapid rises/falls.
signal_line = exponential_moving_average(ta.macd_12_26, 9)
ta_picks['macd_12_26_signal'] = signal_line
ta_picks['macd_12_26_diff_signal'] = (ta.macd_12_26 -
signal_line).pct_change()
ta_picks['macd_12_26_pct'] = ta.macd_12_26.pct_change()
# PPO is identical to the moving average convergence divergence (MACD) indicator,
# except the PPO measures percentage difference between two EMAs, while the MACD measures absolute (dollar) difference.
signal_line = exponential_moving_average(ta.ppo_12_26, 9)
ta_picks['ppo_12_26_signal'] = signal_line
ta_picks['ppo_12_26_diff_signal'] = (ta.ppo_12_26 -
signal_line).pct_change()
ta_picks['ppo_12_26_pct'] = ta.ppo_12_26.pct_change()
# ADI Accumulation/distribution is a cumulative indicator that uses volume and price to assess whether
# a stock is being accumulated or distributed.
# The accumulation/distribution measure seeks to identify divergences between the stock price and volume flow.
# This provides insight into how strong a trend is. If the price is rising but the indicator is falling
# this indicates that buying or accumulation volume may not be enough to support
# the price rise and a price decline could be forthcoming.
# ==> IDEA: if we can fit a line to the price y1 = m1X+q1 and a line to ADI y2=m2X+q2 then we can identify
# divergences by simply looking at the sign of M.
# Another insight would be given by the slope (ie pct_change)
ta_picks['adi_pct'] = ta.adi.pct_change()
ta_picks['adi_close_convergence'] = convergence_between_series(
ta.adi, ohlcv.close, 3)
# RSI goes from 0 to 100, values <= 20 mean BUY, while values >= 80 mean SELL.
# Dividing it by 100 to get a floating point feature, makes no sense to pct_change it
ta_picks['rsi_14_div100'] = ta.rsi_14 / 100
# The Money Flow Index (MFI) is a technical indicator that generates overbought or oversold
# signals using both prices and volume data. The oscillator moves between 0 and 100.
# An MFI reading above 80 is considered overbought and an MFI reading below 20 is considered oversold,
# although levels of 90 and 10 are also used as thresholds.
# A divergence between the indicator and price is noteworthy. For example, if the indicator is rising while
# the price is falling or flat, the price could start rising.
ta_picks['mfi_14_div100'] = ta.mfi_14 / 100
# The Chande momentum oscillator is a technical momentum indicator similar to other momentum indicators
# such as Wilder’s Relative Strength Index (Wilder’s RSI) and the Stochastic Oscillator.
# It measures momentum on both up and down days and does not smooth results, triggering more frequent
# oversold and overbought penetrations. The indicator oscillates between +100 and -100.
# Many technical traders add a 10-period moving average to this oscillator to act as a signal line.
# The oscillator generates a bullish signal when it crosses above the moving average and a
# bearish signal when it drops below the moving average.
ta_picks['cmo_14_div100'] = ta.cmo_14 / 100
signal_line = simple_moving_average(ta.cmo_14, 10)
ta_picks['cmo_14_signal'] = signal_line
ta_picks['cmo_14_diff_signal'] = (ta.cmo_14 - signal_line) / 100
# On-balance volume (OBV) is a technical trading momentum indicator that uses volume flow to predict changes in stock price.
# Eventually, volume drives the price upward. At that point, larger investors begin to sell, and smaller investors begin buying.
# Despite being plotted on a price chart and measured numerically,
# the actual individual quantitative value of OBV is not relevant.
# The indicator itself is cumulative, while the time interval remains fixed by a dedicated starting point,
# meaning the real number value of OBV arbitrarily depends on the start date.
# Instead, traders and analysts look to the nature of OBV movements over time;
# the slope of the OBV line carries all of the weight of analysis. => We want percent change
ta_picks['obv_pct'] = ta.obv.pct_change()
ta_picks['obv_mean3_pct'] = ta.obv.rolling(3).mean().pct_change()
# Strong rallies in price should see the force index rise.
# During pullbacks and sideways movements, the force index will often fall because the volume
# and/or the size of the price moves gets smaller.
# => Encoding the percent variation could be a good idea
ta_picks['fi_13_pct'] = ta.fi_13.pct_change()
ta_picks['fi_50_pct'] = ta.fi_50.pct_change()
# The Aroon Oscillator is a trend-following indicator that uses aspects of the
# Aroon Indicator (Aroon Up and Aroon Down) to gauge the strength of a current trend
# and the likelihood that it will continue.
# It moves between -100 and 100. A high oscillator value is an indication of an uptrend
# while a low oscillator value is an indication of a downtrend.
ta_picks['ao_14_div100'] = ta.ao_14 / 100
# The average true range (ATR) is a technical analysis indicator that measures market volatility
# by decomposing the entire range of an asset price for that period.
# ATRP is pct_change of volatility
# ta_picks['atrp_14'] = ta.atrp_14 # We include whole TA
# Percentage Volume Oscillator (PVO) is momentum volume oscillator used in technical analysis
# to evaluate and measure volume surges and to compare trading volume to the average longer-term volume.
# PVO does not analyze price and it is based solely on volume.
# It compares fast and slow volume moving averages by showing how short-term volume differs from
# the average volume over longer-term.
# Since it does not care a trend's factor in its calculation (only volume data are used)
# this technical indicator cannot be used alone to predict changes in a trend.
# ta_picks['pvo_12_26'] = ta.pvo_12_26 # We include whole TA
merge_dataframes = [
lagged_ohlc, lagged_ohlcv_pct, ohlc_patterns, ohlc_residuals,
ohlc_splines, ohlcv_stats, ta, ta_picks
]
# Cherry-picked and engineered features from Blockchain data
# >> Only use blockchain data if we don't lose too many points, at most 60
if coinmetrics is not None and (coinmetrics.shape[0] -
ohlcv.shape[0]) > -60:
cm_picks = pd.DataFrame(index=ohlcv.index)
if 'adractcnt' in coinmetrics.columns:
cm_picks['adractcnt_pct'] = coinmetrics.adractcnt.pct_change()
if 'txtfrvaladjntv' in coinmetrics.columns and 'isstotntv' in coinmetrics.columns and 'feetotntv' in coinmetrics.columns:
# I want to represent miners earnings (fees + issued coins) vs amount transacted in that interval
cm_picks['earned_vs_transacted'] = (
coinmetrics.isstotntv +
coinmetrics.feetotntv) / coinmetrics.txtfrvaladjntv
if 'isstotntv' in coinmetrics.columns:
# isstotntv is total number of coins mined in the time interval
# splycur is total number of coins mined (all time)
total_mined = coinmetrics.isstotntv.rolling(
365, min_periods=7).sum() # total mined in a year
cm_picks['isstot1_isstot365_pct'] = (coinmetrics.isstotntv /
total_mined).pct_change()
if 'splycur' in coinmetrics.columns and 'isstotntv' in coinmetrics.columns:
cm_picks['splycur_isstot1_pct'] = (
coinmetrics.isstotntv / coinmetrics.splycur).pct_change()
if 'hashrate' in coinmetrics.columns:
cm_picks['hashrate_pct'] = coinmetrics.hashrate.pct_change()
# if 'roi30d' in coinmetrics.columns:
# cm_picks['roi30d'] = coinmetrics.roi30d
if 'isstotntv' in coinmetrics.columns:
cm_picks['isstotntv_pct'] = coinmetrics.isstotntv.pct_change()
if 'feetotntv' in coinmetrics.columns:
cm_picks['feetotntv_pct'] = coinmetrics.feetotntv.pct_change()
if 'txtfrcnt' in coinmetrics.columns:
cm_picks['txtfrcnt_pct'] = coinmetrics.txtfrcnt.pct_change()
# if 'vtydayret30d' in coinmetrics.columns:
# cm_picks['vtydayret30d'] = coinmetrics.vtydayret30d
#if 'isscontpctann' in coinmetrics.columns:
# cm_picks['isscontpctann'] = coinmetrics.isscontpctann
# merge_dataframes.append(coinmetrics)
merge_dataframes.append(cm_picks)
# Drop columns whose values are all nan or inf from each facet
with pd.option_context('mode.use_inf_as_na',
True): # Set option temporarily
for _df in merge_dataframes:
_df.dropna(axis='columns', how='all', inplace=True)
return pd.concat(merge_dataframes,
axis='columns',
verify_integrity=True,
sort=True,
join='inner')
def build(ohlcv: pd.DataFrame, coinmetrics: pd.DataFrame, **kwargs):
W = kwargs.get('W', 10)
ta = get_ta_features(ohlcv, TA_CONFIG)
ohlcv_stats = pd.DataFrame(index=ohlcv.index)
# Showld always be > 0, price oscillation range for current day
ohlcv_stats['day_range_pct'] = (ohlcv.high - ohlcv.low).pct_change()
# Price direction for the day green > 0, red < 0. Modulus is range.
ohlcv_stats['direction'] = ohlcv.close - ohlcv.open
cv_pct = ohlcv[['close', 'volume']].pct_change()
lagged_cv_pct = pd.concat(
[cv_pct] + [make_lagged(cv_pct, i) for i in range(1, W + 1)],
axis='columns',
verify_integrity=True,
sort=True,
join='inner'
)
# Cherry-picked and engineered features from Blockchain data
cm_picks = pd.DataFrame(index=ohlcv.index)
if 'adractcnt' in coinmetrics.columns:
cm_picks['adractcnt_pct'] = coinmetrics.adractcnt.pct_change()
if 'txtfrvaladjntv' in coinmetrics.columns and 'isstotntv' in coinmetrics.columns and 'feetotntv' in coinmetrics.columns:
# I want to represent miners earnings (fees + issued coins) vs amount transacted in that interval
cm_picks['earned_vs_transacted'] = (coinmetrics.isstotntv + coinmetrics.feetotntv) / coinmetrics.txtfrvaladjntv
if 'isstotntv' in coinmetrics.columns:
# isstotntv is total number of coins mined in the time interval
# splycur is total number of coins mined (all time)
total_mined = coinmetrics.isstotntv.rolling(365, min_periods=7).sum() # total mined in a year
cm_picks['isstot1_isstot365_pct'] = (coinmetrics.isstotntv / total_mined).pct_change()
if 'splycur' in coinmetrics.columns and 'isstotntv' in coinmetrics.columns:
cm_picks['splycur_isstot1_pct'] = (coinmetrics.isstotntv / coinmetrics.splycur).pct_change()
if 'hashrate' in coinmetrics.columns:
cm_picks['hashrate_pct'] = coinmetrics.hashrate.pct_change()
if 'roi30d' in coinmetrics.columns:
cm_picks['roi30d'] = coinmetrics.roi30d
if 'isstotntv' in coinmetrics.columns:
cm_picks['isstotntv_pct'] = coinmetrics.isstotntv.pct_change()
if 'feetotntv' in coinmetrics.columns:
cm_picks['feetotntv_pct'] = coinmetrics.feetotntv.pct_change()
if 'txtfrcount' in coinmetrics.columns:
cm_picks['txtfrcount_pct'] = coinmetrics.txtfrcount.pct_change()
cm_picks['txtfrcount_volume'] = coinmetrics.txtfrcount.pct_change()
if 'vtydayret30d' in coinmetrics.columns:
cm_picks['vtydayret30d'] = coinmetrics.vtydayret30d
if 'isscontpctann' in coinmetrics.columns:
cm_picks['isscontpctann'] = coinmetrics.isscontpctann
# Cherry-picked and engineered features from technical indicators
ta_picks = pd.DataFrame(index=ta.index)
# REMA / RSMA are already used and well-estabilished in ATSA,
# I'm taking the pct change since i want to encode the relative movement of the ema's not their positions
# Drop other dimensions since they're correlated
ta_picks['rema_8_15_pct'] = ta.rema_8_15.pct_change()
ta_picks['rsma_8_15_pct'] = ta.rema_8_15.pct_change()
# Stoch is a momentum indicator comparing a particular closing price of a security to a range of its prices
# over a certain period of time.
# The sensitivity of the oscillator to market movements is reducible by adjusting that time period or
# by taking a moving average of the result.
# It is used to generate overbought and oversold trading signals, utilizing a 0-100 bounded range of values.
# IDEA => decrease sensitivity by 3-mean and divide by 100 to get fp values
ta_picks['stoch_14_mean3_div100'] = ta.stoch_14.rolling(3).mean() / 100
# Moving Average Convergence Divergence (MACD) is a trend-following momentum indicator that shows
# the relationship between two moving averages of a security’s price.
# The MACD is calculated by subtracting the 26-period Exponential Moving Average (EMA) from the 12-period EMA.
# A nine-day EMA of the MACD called the "signal line," is then plotted on top of the MACD line,
# which can function as a trigger for buy and sell signals.
# Traders may buy the security when the MACD crosses above its signal line and sell - or short - the security
# when the MACD crosses below the signal line.
# Moving Average Convergence Divergence (MACD) indicators can be interpreted in several ways,
# but the more common methods are crossovers, divergences, and rapid rises/falls.
signal_line = exponential_moving_average(ta.macd_12_26, 9)
ta_picks['macd_12_26_signal'] = (ta.macd_12_26 - signal_line).pct_change() # Relationship with signal line
ta_picks['macd_12_26_pct'] = ta.macd_12_26.pct_change() # Information about slope
# PPO is identical to the moving average convergence divergence (MACD) indicator,
# except the PPO measures percentage difference between two EMAs, while the MACD measures absolute (dollar) difference.
signal_line = exponential_moving_average(ta.ppo_12_26, 9)
ta_picks['ppo_12_26_signal'] = (ta.ppo_12_26 - signal_line).pct_change() # Relationship with signal line
ta_picks['ppo_12_26_pct'] = ta.ppo_12_26.pct_change() # Information about slope
# ADI Accumulation/distribution is a cumulative indicator that uses volume and price to assess whether
# a stock is being accumulated or distributed.
# The accumulation/distribution measure seeks to identify divergences between the stock price and volume flow.
# This provides insight into how strong a trend is. If the price is rising but the indicator is falling
# this indicates that buying or accumulation volume may not be enough to support
# the price rise and a price decline could be forthcoming.
# ==> IDEA: if we can fit a line to the price y1 = m1X+q1 and a line to ADI y2=m2X+q2 then we can identify
# divergences by simply looking at the sign of M.
# Another insight would be given by the slope (ie pct_change)
ta_picks['adi_pct'] = ta.adi.pct_change()
ta_picks['adi_close_convergence'] = convergence_between_series(ta.adi, ohlcv.close, 3)
# RSI goes from 0 to 100, values <= 20 mean BUY, while values >= 80 mean SELL.
# Dividing it by 100 to get a floating point feature, makes no sense to pct_change it
ta_picks['rsi_14_div100'] = ta.rsi_14 / 100
# The Money Flow Index (MFI) is a technical indicator that generates overbought or oversold
# signals using both prices and volume data. The oscillator moves between 0 and 100.
# An MFI reading above 80 is considered overbought and an MFI reading below 20 is considered oversold,
# although levels of 90 and 10 are also used as thresholds.
# A divergence between the indicator and price is noteworthy. For example, if the indicator is rising while
# the price is falling or flat, the price could start rising.
ta_picks['mfi_14_div100'] = ta.mfi_14 / 100
# The Chande momentum oscillator is a technical momentum indicator similar to other momentum indicators
# such as Wilder’s Relative Strength Index (Wilder’s RSI) and the Stochastic Oscillator.
# It measures momentum on both up and down days and does not smooth results, triggering more frequent
# oversold and overbought penetrations. The indicator oscillates between +100 and -100.
# Many technical traders add a 10-period moving average to this oscillator to act as a signal line.
# The oscillator generates a bullish signal when it crosses above the moving average and a
# bearish signal when it drops below the moving average.
ta_picks['cmo_14_div100'] = ta.cmo_14 / 100
signal_line = simple_moving_average(ta.cmo_14, 10)
ta_picks['cmo_14_signal'] = (ta.cmo_14 - signal_line) / 100
# On-balance volume (OBV) is a technical trading momentum indicator that uses volume flow to predict changes in stock price.
# Eventually, volume drives the price upward. At that point, larger investors begin to sell, and smaller investors begin buying.
# Despite being plotted on a price chart and measured numerically,
# the actual individual quantitative value of OBV is not relevant.
# The indicator itself is cumulative, while the time interval remains fixed by a dedicated starting point,
# meaning the real number value of OBV arbitrarily depends on the start date.
# Instead, traders and analysts look to the nature of OBV movements over time;
# the slope of the OBV line carries all of the weight of analysis. => We want percent change
ta_picks['obv_pct'] = ta.obv.pct_change()
ta_picks['obv_mean3_pct'] = ta.obv.rolling(3).mean().pct_change()
# Strong rallies in price should see the force index rise.
# During pullbacks and sideways movements, the force index will often fall because the volume
# and/or the size of the price moves gets smaller.
# => Encoding the percent variation could be a good idea
ta_picks['fi_13_pct'] = ta.fi_13.pct_change()
ta_picks['fi_50_pct'] = ta.fi_50.pct_change()
# The Aroon Oscillator is a trend-following indicator that uses aspects of the
# Aroon Indicator (Aroon Up and Aroon Down) to gauge the strength of a current trend
# and the likelihood that it will continue.
# It moves between -100 and 100. A high oscillator value is an indication of an uptrend
# while a low oscillator value is an indication of a downtrend.
ta_picks['ao_14'] = ta.ao_14 / 100
# The average true range (ATR) is a technical analysis indicator that measures market volatility
# by decomposing the entire range of an asset price for that period.
# ATRP is pct_change of volatility
ta_picks['atrp_14'] = ta.atrp_14
# Percentage Volume Oscillator (PVO) is momentum volume oscillator used in technical analysis
# to evaluate and measure volume surges and to compare trading volume to the average longer-term volume.
# PVO does not analyze price and it is based solely on volume.
# It compares fast and slow volume moving averages by showing how short-term volume differs from
# the average volume over longer-term.
# Since it does not care a trend's factor in its calculation (only volume data are used)
# this technical indicator cannot be used alone to predict changes in a trend.
ta_picks['pvo_12_26'] = ta.pvo_12_26
return pd.concat(
[ohlcv_stats, lagged_cv_pct, cm_picks, ta_picks],
axis='columns',
verify_integrity=True,
sort=True,
join='inner'
)
def build(ohlcv: pd.DataFrame, coinmetrics: pd.DataFrame, **kwargs):
W = kwargs.get('W', 10)
ta = get_ta_features(ohlcv, TA_CONFIG)
residuals = pd.DataFrame()
residuals['open_resid'] = get_residual(ohlcv.open)
residuals['high_resid'] = get_residual(ohlcv.high)
residuals['low_resid'] = get_residual(ohlcv.low)
residuals['close_resid'] = get_residual(ohlcv.close)
history_facet = pd.concat(
[residuals] + [make_lagged(residuals, i) for i in range(1, W + 1)],
axis='columns',
verify_integrity=True,
sort=True,
join='inner')
# Price trend facet (REMA/RSMA, MACD, AO, ADX, WD+ - WD-)
trend_facet = ta[[
"rsma_5_20", "rsma_8_15", "rsma_20_50", "rema_5_20", "rema_8_15",
"rema_20_50", "macd_12_26", "ao_14", "adx_14", "wd_14"
]]
# Volatility facet (CMO, ATRp)
volatility_facet = ta[["cmo_14", "atrp_14"]]
# Volume facet (Volume pct, PVO, ADI, OBV)
volume_pct = ohlcv.volume.pct_change().replace([np.inf, -np.inf], 0)
volume_facet = pd.concat([volume_pct, ta[["pvo_12_26", "adi", "obv"]]],
axis='columns',
verify_integrity=True,
sort=True,
join='inner')
# On-chain facet
cm_1 = coinmetrics.reindex(columns=[
'adractcnt', 'txtfrvaladjntv', 'isstotntv',
'feetotntv', 'splycur', 'hashrate',
'difficulty', 'txtfrcount'])\
.pct_change()
cm_2 = coinmetrics.reindex(columns=['isscontpctann'])
chain_facet = pd.concat([cm_1, cm_2],
axis='columns',
verify_integrity=True,
sort=True,
join='inner')
# Drop columns whose values are all nan or inf from each facet
with pd.option_context('mode.use_inf_as_na',
True): # Set option temporarily
history_facet = history_facet.dropna(axis='columns', how='all')
trend_facet = trend_facet.dropna(axis='columns', how='all')
volatility_facet = volatility_facet.dropna(axis='columns', how='all')
volume_facet = volume_facet.dropna(axis='columns', how='all')
chain_facet = chain_facet.dropna(axis='columns', how='all')
# feature_groups = {
# 'price_history': [c for c in history_facet.columns],
# 'trend': [c for c in trend_facet.columns],
# 'volatility': [c for c in volatility_facet.columns],
# 'volume': [c for c in volume_facet.columns],
# 'chain': [c for c in chain_facet.columns],
# }
return pd.concat([
history_facet, trend_facet, volatility_facet, volume_facet, chain_facet
],
axis='columns',
verify_integrity=True,
sort=True,
join='inner')