def get_moving_average_analysis(data, current_moving_average, time_period):
time_period_unit_moving_average = tulipy.sma(data, time_period)
# equalize array size
min_len_arrays = min(len(time_period_unit_moving_average), len(current_moving_average))
# compute difference between 1 unit values and others ( >0 means currently up the other one)
values_difference = (current_moving_average[-min_len_arrays:] - time_period_unit_moving_average[-min_len_arrays:])
values_difference = DataUtil.drop_nan(values_difference)
if len(values_difference):
# indexes where current_unit_moving_average crosses time_period_unit_moving_average
crossing_indexes = TrendAnalysis.get_threshold_change_indexes(values_difference, 0)
multiplier = 1 if values_difference[-1] > 0 else -1
# check at least some data crossed 0
if crossing_indexes:
normalized_data = DataUtil.normalize_data(values_difference)
current_value = min(abs(normalized_data[-1])*2, 1)
if math.isnan(current_value):
return 0
# check <= values_difference.count()-1if current value is max/min
if current_value == 0 or current_value == 1:
chances_to_be_max = TrendAnalysis.get_estimation_of_move_state_relatively_to_previous_moves_length(
crossing_indexes,
values_difference)
return multiplier*current_value*chances_to_be_max
# other case: maxima already reached => return distance to max
else:
return multiplier*current_value
# just crossed the average => neutral
return 0
def eval_impl(self):
self.eval_note = START_PENDING_EVAL_NOTE
long_period_length = 26
if len(self.data[PriceIndexes.IND_PRICE_CLOSE.value]) >= long_period_length:
macd, macd_signal, macd_hist = tulipy.macd(self.data[PriceIndexes.IND_PRICE_CLOSE.value], 12,
long_period_length, 9)
# on macd hist => M pattern: bearish movement, W pattern: bullish movement
# max on hist: optimal sell or buy
macd_hist = DataUtil.drop_nan(macd_hist)
zero_crossing_indexes = TrendAnalysis.get_threshold_change_indexes(macd_hist, 0)
last_index = len(macd_hist) - 1
pattern, start_index, end_index = PatternAnalyser.find_pattern(macd_hist, zero_crossing_indexes,
last_index)
if pattern != PatternAnalyser.UNKNOWN_PATTERN:
# set sign (-1 buy or 1 sell)
sign_multiplier = -1 if pattern == "W" or pattern == "V" else 1
# set pattern time frame => W and M are on 2 time frames, others 1
pattern_move_time = 2 if (pattern == "W" or pattern == "M") and end_index == last_index else 1
# set weight according to the max value of the pattern and the current value
current_pattern_start = start_index
price_weight = macd_hist[-1] / macd_hist[current_pattern_start:].max() if sign_multiplier == 1 \
else macd_hist[-1] / macd_hist[current_pattern_start:].min()
if not math.isnan(price_weight):
self._analyse_pattern(pattern, macd_hist, zero_crossing_indexes, price_weight,
pattern_move_time, sign_multiplier)
def get_average_strategy_eval(self, exchange, active_only=False):
eval_list = [
s.get_eval_note()
for s in self.get_strategies_eval_list(exchange, active_only)
if isinstance(s.get_eval_note(), (int, float))
]
return DataUtil.mean(eval_list)
async def eval_impl(self):
self.eval_note = False
short_period = 35 # standard with klinger
long_period = 55 # standard with klinger
ema_signal_period = 13 # standard ema signal for klinger
kvo = tulipy.kvo(self.data[PriceIndexes.IND_PRICE_HIGH.value],
self.data[PriceIndexes.IND_PRICE_LOW.value],
self.data[PriceIndexes.IND_PRICE_CLOSE.value],
self.data[PriceIndexes.IND_PRICE_VOL.value],
short_period, long_period)
kvo = DataUtil.drop_nan(kvo)
if len(kvo) >= ema_signal_period:
kvo_ema = tulipy.ema(kvo, ema_signal_period)
ema_difference = kvo - kvo_ema
if len(ema_difference) > 1:
zero_crossing_indexes = TrendAnalysis.get_threshold_change_indexes(
ema_difference, 0)
max_elements = 7
to_consider_kvo = min(
max_elements,
len(ema_difference) - zero_crossing_indexes[-1])
self.eval_note = TrendAnalysis.min_has_just_been_reached(
ema_difference[-to_consider_kvo:],
acceptance_window=0.9,
delay=1)
def eval_impl(self):
self.eval_note = START_PENDING_EVAL_NOTE
period_length = 14
if len(self.data[
PriceIndexes.IND_PRICE_HIGH.value]) > period_length + 10:
min_adx = 7.5
max_adx = 45
neutral_adx = 25
adx = tulipy.adx(self.data[PriceIndexes.IND_PRICE_HIGH.value],
self.data[PriceIndexes.IND_PRICE_LOW.value],
self.data[PriceIndexes.IND_PRICE_CLOSE.value],
period_length)
instant_ema = tulipy.ema(
self.data[PriceIndexes.IND_PRICE_CLOSE.value], 2)
slow_ema = tulipy.ema(
self.data[PriceIndexes.IND_PRICE_CLOSE.value], 20)
adx = DataUtil.drop_nan(adx)
if len(adx):
current_adx = adx[-1]
current_slows_ema = slow_ema[-1]
current_instant_ema = instant_ema[-1]
multiplier = -1 if current_instant_ema < current_slows_ema else 1
# strong adx => strong trend
if current_adx > neutral_adx:
# if max adx already reached => when ADX forms a top and begins to turn down, you should look for a
# retracement that causes the price to move toward its 20-day exponential moving average (EMA).
adx_last_values = adx[-15:]
adx_last_value = adx_last_values[-1]
local_max_adx = adx_last_values.max()
# max already reached => trend will slow down
if adx_last_value < local_max_adx:
self.eval_note = multiplier * (
current_adx - neutral_adx) / (local_max_adx -
neutral_adx)
# max not reached => trend will continue, return chances to be max now
else:
crossing_indexes = TrendAnalysis.get_threshold_change_indexes(
adx, neutral_adx)
chances_to_be_max = \
TrendAnalysis.get_estimation_of_move_state_relatively_to_previous_moves_length(
crossing_indexes, adx) if len(crossing_indexes) > 2 \
else 0.75
proximity_to_max = min(1, current_adx / max_adx)
self.eval_note = multiplier * proximity_to_max * chances_to_be_max
# weak adx => change to come
else:
self.eval_note = multiplier * min(1, (
(neutral_adx - current_adx) / (neutral_adx - min_adx)))
def get_progress(self):
if not self.min_time_frame_to_consider:
return 0
else:
progresses = []
for symbol in self.time_frame_get_times:
if symbol in self.min_time_frame_to_consider:
current = self.time_frame_get_times[symbol][self.min_time_frame_to_consider[symbol]]
nb_max = len(self.data[symbol][self.min_time_frame_to_consider[symbol]])
progresses.append(current / nb_max)
return int(DataUtil.mean(progresses) * 100)
def _get_rsi_averages(self):
# compute the slow and fast RSI average
if len(self.data[
PriceIndexes.IND_PRICE_CLOSE.value]) > self.period_length:
rsi_v = tulipy.rsi(self.data[PriceIndexes.IND_PRICE_CLOSE.value],
period=self.period_length)
rsi_v = DataUtil.drop_nan(rsi_v)
if len(rsi_v):
slow_average = numpy.mean(rsi_v[-self.slow_eval_count:])
fast_average = numpy.mean(rsi_v[-self.fast_eval_count:])
return slow_average, fast_average, rsi_v
return None, None, None
def _find_optimal_configuration_using_results(self):
for time_frame in self.all_time_frames:
time_frame_sorted_results = self.get_sorted_results(self.run_results, time_frame)
self.sorted_results_by_time_frame[time_frame.value] = time_frame_sorted_results
results_through_all_time_frame = {}
for results in self.sorted_results_by_time_frame.values():
for rank, result in enumerate(results):
result_summary = result.get_config_summary()
if result_summary not in results_through_all_time_frame:
results_through_all_time_frame[result_summary] = [[], 0]
results_through_all_time_frame[result_summary][RANK] += rank
results_through_all_time_frame[result_summary][TRADES] += result.trades_counts
result_list = [(result, trades_and_rank[RANK], DataUtil.mean(trades_and_rank[TRADES]))
for result, trades_and_rank in results_through_all_time_frame.items()]
self.sorted_results_through_all_time_frame = sorted(result_list, key=lambda res: res[RANK])
def eval_impl(self):
eval_proposition = START_PENDING_EVAL_NOTE
short_period = 35 # standard with klinger
long_period = 55 # standard with klinger
ema_signal_period = 13 # standard ema signal for klinger
kvo = tulipy.kvo(self.data[PriceIndexes.IND_PRICE_HIGH.value],
self.data[PriceIndexes.IND_PRICE_LOW.value],
self.data[PriceIndexes.IND_PRICE_CLOSE.value],
self.data[PriceIndexes.IND_PRICE_VOL.value],
short_period, long_period)
kvo = DataUtil.drop_nan(kvo)
if len(kvo) >= ema_signal_period:
kvo_ema = tulipy.ema(kvo, ema_signal_period)
ema_difference = kvo - kvo_ema
if len(ema_difference) > 1:
zero_crossing_indexes = TrendAnalysis.get_threshold_change_indexes(
ema_difference, 0)
current_difference = ema_difference[-1]
significant_move_threshold = numpy.std(ema_difference)
factor = 0.2
if TrendAnalysis.peak_has_been_reached_already(
ema_difference[zero_crossing_indexes[-1]:]):
if abs(current_difference) > significant_move_threshold:
factor = 1
else:
factor = 0.5
eval_proposition = current_difference * factor / significant_move_threshold
if abs(eval_proposition) > 1:
eval_proposition = 1 if eval_proposition > 0 else -1
self.eval_note = eval_proposition
def _create_orders(self, lower_bound, upper_bound, side, sorted_orders,
portfolio, current_price, missing_orders, state):
if lower_bound >= upper_bound:
self.logger.warning(f"No {side} orders for {self.symbol} possible: current price beyond boundaries.")
return []
orders = []
selling = side == TradeOrderSide.SELL
currency, market = split_symbol(self.symbol)
order_limiting_currency = currency if selling else market
order_limiting_currency_amount = portfolio[order_limiting_currency][Portfolio.AVAILABLE] \
if order_limiting_currency in portfolio else 0
if state == self.NEW:
# create staggered orders
starting_bound = lower_bound * (1 + self.spread / 2) if selling else upper_bound * (1 - self.spread / 2)
self.flat_spread = AbstractTradingModeCreator.adapt_price(self.symbol_market,
current_price * self.spread)
orders_count, average_order_quantity = \
self._get_order_count_and_average_quantity(current_price, selling, lower_bound,
upper_bound, order_limiting_currency_amount,
currency=order_limiting_currency)
for i in range(orders_count):
price = self._get_price_from_iteration(starting_bound, selling, i)
if price is not None:
quantity = self._get_quantity_from_iteration(average_order_quantity, self.mode,
side, i, orders_count, price)
if quantity is not None:
orders.append(OrderData(side, quantity, price, self.symbol))
if not orders:
self.logger.error(f"Not enough {order_limiting_currency} to create {side.name} orders. "
f"For the strategy to work better, add {order_limiting_currency} funds or "
f"change change the strategy settings to make less but bigger orders.")
else:
orders.reverse()
if state == self.FILL:
# complete missing orders
if missing_orders:
max_quant_per_order = order_limiting_currency_amount / len(missing_orders)
missing_orders_around_spread = []
for missing_order_price, missing_order_side in missing_orders:
if missing_order_side == side:
previous_o = None
following_o = None
for o in sorted_orders:
if previous_o is None:
previous_o = o
elif o.origin_price > missing_order_price:
following_o = o
break
else:
previous_o = o
if previous_o.side == following_o.side:
# missing order between similar orders
quantity = min(DataUtil.mean([previous_o.origin_quantity, following_o.origin_quantity]),
max_quant_per_order / missing_order_price)
orders.append(OrderData(missing_order_side, quantity,
missing_order_price, self.symbol, False))
self.logger.debug(f"Creating missing orders not around spread: {orders[-1]}")
else:
missing_orders_around_spread.append((missing_order_price, missing_order_side))
if missing_orders_around_spread:
# missing order next to spread
starting_bound = upper_bound if selling else lower_bound
increment_window = self.flat_increment/2
order_limiting_currency_available_amount = \
portfolio[order_limiting_currency][Portfolio.AVAILABLE] \
if order_limiting_currency in portfolio else 0
portfolio_total = portfolio[order_limiting_currency][Portfolio.TOTAL] \
if order_limiting_currency in portfolio else 0
order_limiting_currency_amount = portfolio_total
if order_limiting_currency_available_amount:
orders_count, average_order_quantity = \
self._get_order_count_and_average_quantity(current_price, selling, lower_bound,
upper_bound, portfolio_total,
currency=order_limiting_currency)
for missing_order_price, missing_order_side in missing_orders_around_spread:
limiting_amount_from_this_order = order_limiting_currency_amount
price = starting_bound
found_order = False
i = 0
while not found_order and i < orders_count:
quantity = self._get_quantity_from_iteration(average_order_quantity, self.mode,
side, i, orders_count,
price)
limiting_currency_quantity = quantity if selling else quantity / price
if price is not None and limiting_amount_from_this_order > 0 and \
price-increment_window <= missing_order_price <= price+increment_window:
if limiting_currency_quantity > limiting_amount_from_this_order or \
limiting_currency_quantity > order_limiting_currency_available_amount:
limiting_currency_quantity = min(limiting_amount_from_this_order,
order_limiting_currency_available_amount)
found_order = True
if limiting_currency_quantity is not None:
orders.append(OrderData(side, limiting_currency_quantity, price,
self.symbol, False))
self.logger.debug(f"Creating missing order around spread {orders[-1]}")
price = price - self.flat_increment if selling else price + self.flat_increment
limiting_amount_from_this_order -= limiting_currency_quantity
i += 1
elif state == self.ERROR:
self.logger.error("Impossible to create staggered orders when incompatible order are already in place. "
"Cancel these orders of you want to use this trading mode.")
return orders
def get_average_trades_count(self):
return DataUtil.mean(self.trades_counts)
def get_average_score(self):
bot_profitabilities = [
profitability_result[self.BOT_PROFITABILITY] - profitability_result[self.MARKET_PROFITABILITY]
for profitability_result in self.run_profitabilities]
return DataUtil.mean(bot_profitabilities)
