adelorenzo/stable
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

fix ood do_predict issue;

Browse Source
This commit is contained in:
longyu 2022-10-27 23:29:35 +02:00
parent cdec993982
commit a541943edf
1 changed files with 6 additions and 59 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

65
user_data/strategies/FreqaiBinaryClassStrategy_v4.py
View File

@ -359,7 +359,7 @@ class FreqaiBinaryClassStrategy_v4(IStrategy):
def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame: def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
hours_candle_stability = 4 hours_candle_stability = 4
if df["do_predict"].rolling(12 * 4).sum().iloc[-1] == 12 * 4: # enter the market if last `hours_candle_stability` are stable if df["do_predict"].rolling(12 * hours_candle_stability).sum().iloc[-1] == 12 * hours_candle_stability: # enter the market if last `hours_candle_stability` are stable
enter_long_conditions = [df["do_predict"] == 1, df["min"] >= self.minima_threhsold] enter_long_conditions = [df["do_predict"] == 1, df["min"] >= self.minima_threhsold]
if enter_long_conditions: if enter_long_conditions:
@ -375,9 +375,7 @@ class FreqaiBinaryClassStrategy_v4(IStrategy):
reduce(lambda x, y: x & y, enter_short_conditions), ["enter_short", "enter_tag"] reduce(lambda x, y: x & y, enter_short_conditions), ["enter_short", "enter_tag"]
] = (1, "short") ] = (1, "short")
else: else:
df["enter_long", "enter_tag"] = (0, "long") df["enter_long"] = np.zeros(df.shape[0])
if self.can_short:
df["enter_short", "enter_tag"] = (0, "short")
return df return df
def populate_exit_trend(self, df: DataFrame, metadata: dict) -> DataFrame: def populate_exit_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
@ -390,23 +388,6 @@ class FreqaiBinaryClassStrategy_v4(IStrategy):
exit_short_conditions = [df["do_predict"] == 1, df["min"] >= self.minima_threhsold] exit_short_conditions = [df["do_predict"] == 1, df["min"] >= self.minima_threhsold]
if exit_short_conditions: if exit_short_conditions:
df.loc[reduce(lambda x, y: x & y, exit_short_conditions), "exit_short"] = 1 df.loc[reduce(lambda x, y: x & y, exit_short_conditions), "exit_short"] = 1
if self.config['runmode'].value in ('live', 'dry_run'):
trades = Trade.get_trades_proxy(pair=metadata["pair"], is_open=True)
if trades:
if df["do_predict"].iloc[-1] != 1:
avg_entry_price = sum([trade.open_rate * trade.amount for trade in trades]) / sum([trade.amount for trade in trades])
if not trades[0].is_short:
profit = df["close"].iloc[-1] / avg_entry_price - 1
else:
profit = avg_entry_price / df["close"].iloc[-1] - 1
logger.warning(f"Market changed, {metadata['pair']} profit is {profit}")
# if profit < 0: # force sell
last_candle = np.zeros(df.shape[0])
last_candle[-1] = 1
cond = [df["do_predict"] != 1, last_candle]
df.loc[reduce(lambda x, y : x & y, cond),
[f"exit_{'short' if trades[0].is_short else 'long'}", "exit_tag"]] = (1, "OOD Exit")
return df return df
def get_ticker_indicator(self): def get_ticker_indicator(self):
@ -425,45 +406,11 @@ class FreqaiBinaryClassStrategy_v4(IStrategy):
return None return None
trade_candle = trade_candle.squeeze() trade_candle = trade_candle.squeeze()
follow_mode = self.config.get("freqai", {}).get("follow_mode", False) if dataframe["do_predict"].iloc[-1] != 1:
return f"OOD_{trade.enter_tag}_Exit"
if not follow_mode: if (current_time - trade.open_date_utc).seconds > self.max_roi_time_long.value * 60:
pair_dict = self.model.bridge.dd.pair_dict return f"{trade.enter_tag}_Expired"
else:
pair_dict = self.model.bridge.dd.follower_dict
entry_tag = trade.enter_tag
if (
"prediction" + entry_tag not in pair_dict[pair]
or pair_dict[pair]["prediction" + entry_tag] > 0
):
with self.model.bridge.lock:
if entry_tag == "long":
pair_dict[pair]["prediction" + entry_tag] = abs(trade_candle["&s-maxima"])
else:
pair_dict[pair]["prediction" + entry_tag] = abs(trade_candle["&-s_close"])
if not follow_mode:
self.model.bridge.dd.save_drawer_to_disk()
else:
self.model.bridge.dd.save_follower_dict_to_disk()
roi_price = pair_dict[pair]["prediction" + entry_tag]
roi_time = self.max_roi_time_long.value
roi_decay = roi_price * (
1 - ((current_time - trade.open_date_utc).seconds) / (roi_time * 60)
)
if roi_decay < 0:
roi_decay = self.linear_roi_offset.value
else:
roi_decay += self.linear_roi_offset.value
if current_profit > roi_decay:
return "roi_custom_win"
if current_profit < -roi_decay:
return "roi_custom_loss"
def confirm_trade_exit( def confirm_trade_exit(
self, self,