def on_5min_bar(self, bar: BarData): self.std_range5.update(self.am5.range[-1]) self.am5.update_bar(bar) if not self.am.inited or not self.trading: return diff_time = bar.datetime - self.am.time_array[-1] if diff_time.total_seconds() > 3600 or self.count > self.interval: self.count = 0 self.score.base = calc_regress_deg(self.am.close[-self.interval:], False) * 1000 print("score:", self.score.base, self.min, self.max) else: self.count += 1 # pattern_list = [KlinePattern.CDLEVENINGSTAR, KlinePattern.CDL2CROWS, KlinePattern.CDLCONCEALBABYSWALL, KlinePattern.CDLEVENINGDOJISTAR] pattern = self.am5.pattern(list(KlinePattern)) if len(pattern) > 0: print( list( map(lambda x: (KLINE_PATTERN_CHINESE[x[0]], x[1]), pattern))) self.pattern_record.add_pattern(pattern) deg_full = calc_regress_deg(self.am.close[-40:], False) print("deg:", deg_full) self.pattern_record.update()
def close2(self, bar: BarData, calc_data): if self.level > 0: self.order_data = np.append(self.order_data, bar.close_price) order_id = None offset = -40 offset_m = int(offset / 2) deg_full = calc_regress_deg(self.strategy.am.close[-10:], False) if len(self.order_data) > abs(offset * 1.5): y_fit = reg_util.regress_y_polynomial(self.order_data, zoom=True) deg_order_short = calc_regress_deg(y_fit[:abs(offset)], False) if self.volumn > 0: if deg_full < -0.01: # if abs(deg_order_short) < abs(deg_full): return self.strategy.sell( bar.close_price, 1, type=OrderType.MARKET, extra={"reason": "平仓:趋势趋弱,deg={}".format(deg_full)}) elif self.volumn < 0: if deg_full > 0.01: # if abs(deg_order_short) < abs(deg_full): return self.strategy.cover( bar.close_price, 1, type=OrderType.MARKET, extra={"reason": "平仓:趋势趋弱,deg={}".format(deg_full)})
def reverse_shape_strategy(self, am: ArrayManager, bar: BarData, calc_data, setting={ "atr": 40, "atr_valve": 0.8, "deg1": (40, 20), "deg2": (20, 0), }): deg1 = calc_data["deg40_20"] deg2 = calc_data["deg20_0"] kdj = calc_data["kdj"] atr = self.am.atr(40) if atr < 0.08: return if deg1 > 0 and deg2 > 0 or \ deg1 < 0 and deg2 < 0: return if not (abs(deg1) > 0.15 and abs(deg2) > 0.1 and (abs(deg1) + abs(deg2)) > 0.3): return close = am.close[-40:] min_val = np.min(close) max_val = np.max(close) mid_val = max_val if deg1 > 0 else min_val mid_pos = np.where(close == mid_val)[0][0] if mid_pos < 10 or mid_pos > 30: return start_val = np.min(close[:mid_pos]) if deg1 > 0 else np.max( close[:mid_pos]) start_pos = np.where(close == start_val)[0][0] l = mid_pos - start_pos # pos2 = np.where(close == min_val)[0][0] x_fit = reg_util.regress_y_polynomial(close[:mid_pos], zoom=True) deg1_remake = calc_regress_deg(x_fit[:abs(mid_pos)], False) y_fit = reg_util.regress_y_polynomial(close[mid_pos:], zoom=True) deg2_remake = calc_regress_deg(y_fit[:abs(mid_pos)], False) print(start_pos, mid_pos, deg1, deg2, deg1_remake, deg2_remake, l, start_val, mid_val) if deg2 < 0: if kdj[0] < 20 and kdj[1] < 10 and kdj[2] < 10: # if kdj[2] < 10: return self.short(bar.close_price, 1, type=OrderType.MARKET) else: if kdj[0] > 80 and kdj[1] > 90 and kdj[2] > 90: # if kdj[2] > 90: return self.buy(bar.close_price, 1, type=OrderType.MARKET)
def generate_data(self, bar: BarData): offset = -self.offset offset_m = int(offset / 2) calc_nums = np.array(self.ma_tag[-offset:-1]) # var_val = np.var(calc_nums) std_val = np.std(calc_nums) std_val2 = np.std(np.array(self.ma_tag[-10:-1])) std_val3 = np.std(np.array(self.am.range[-30:-10])) ma = self.ma_tag[-1] mean_val = np.mean(calc_nums) mean_val2 = np.mean(np.array(self.ma_tag[-5:-1])) mean_val3 = np.mean(np.array(self.ma_tag[-20:-1])) mean_val4 = np.mean(np.array(self.ma_tag[-30:-5])) kdj_val = self.am.kdj() deg1 = calc_regress_deg(self.am.close[offset:offset_m], False) deg2 = calc_regress_deg(self.am.close[offset_m:], False) deg3 = calc_regress_deg(self.am.close[-10:], False) deg_full = calc_regress_deg(self.am.close[offset:], False) calc_data = (dict(kdj=[ round(kdj_val["k"][-1], 2), round(kdj_val["d"][-1], 2), round(kdj_val["j"][-1], 2) ], deg40_20=round(deg1, 2), deg20=round(deg2, 2), deg10=round(deg3, 2), deg_f=round(deg_full, 2), time=bar.datetime, price=bar.close_price, ma=round(ma, 2), std_40=round(std_val, 2), mean40=round(mean_val, 2), std_10=round(std_val2, 2), mean30_10=round(mean_val4, 2), mean10=round(mean_val2, 2), vol=self.am.volume[-1], std_range=self.std_range.data[-1:-5:-1], range=self.am.range[-1:-5:-1].tolist(), range_sum=np.sum(self.am.range[-5:]), atr=self.am.atr(10), tr=self.am.atr(1, length=2), pattern=list( map(lambda x: KLINE_PATTERN_CHINESE[x], self.pattern_record.keys())))) return calc_data
def open_kline1(self, am: ArrayManager, bar: BarData, calc_data): if KlinePattern.CDLEVENINGSTAR not in self.pattern_record: return # if std_val2 < 0.2: deg = calc_regress_deg(self.am.close[-5:], False) print("kline_strategy", deg) if deg < -0.1: return self.short(bar.close_price, 1, type=OrderType.MARKET)
def trend_reverse_close(self, am: ArrayManager, bar: BarData, calc_data): if self.trade_mgr.get_status() != PositionStatus.Empty: data = self.ma_info.info[-60:] length = len(data) close = (data[10] + data[20] + data[30]) / 3 if length > 10: y_fit = reg_util.regress_y_polynomial(close[-10:], zoom=True) deg = calc_regress_deg(y_fit[:10], False) else: deg = calc_regress_deg(close[-10:], False) calc_data["trade_close_deg"] = deg if self.trade_mgr.get_status() == PositionStatus.Short: if deg > 0.01: calc_data["trade_close"] = "平仓:趋势反转{}".format(deg) return self.trade_mgr.cover(bar.close_price) elif self.trade_mgr.get_status() == PositionStatus.Long: if deg < -0.01: calc_data["trade_close"] = "平仓:趋势反转{}".format(deg) return self.trade_mgr.sell(bar.close_price)
def generate_3mindata(self, am:ArrayManager, bar:BarData): offset = -self.offset offset_m = int(offset / 2) calc_nums = np.array(self.ma_tag[-offset:-1]) # var_val = np.var(calc_nums) std_val = np.std(calc_nums) std_val2 = np.std(np.array(self.ma_tag[-10:-1])) std_val3 = np.std(np.array(am.range[-30:-10])) ma = self.ma_tag[-1] mean_val = np.mean(calc_nums) mean_val2 = np.mean(np.array(self.ma_tag[-5:-1])) mean_val3 = np.mean(np.array(self.ma_tag[-20:-1])) mean_val4 = np.mean(np.array(self.ma_tag[-30:-5])) kdj_val = am.kdj() deg1 = calc_regress_deg(am.close[offset : offset_m], False) deg2 = calc_regress_deg(am.close[offset_m :], False) deg3 = calc_regress_deg(am.close[-10 :], False) deg_full = calc_regress_deg(am.close[offset :], False) wave = self.wave(am.close[-30:]) wave_r_sum = np.sum(wave["range"]) macd=am.macd(20,40, 16) calc_data = (dict( kdj=[round(kdj_val["k"][-1],2),round(kdj_val["d"][-1],2),round(kdj_val["j"][-1],2)], cci_20=am.cci(20),rsi=am.rsi(20),adx=am.adx(20),boll=am.boll(20, 3.4), macd=[round(macd[0],2),round(macd[1],2),round(macd[2],2)], deg40_20=round(deg1,2), deg20_0=round(deg2,2), deg20_10=round(calc_regress_deg(am.close[-20:-10], False),2), deg10_0=round(deg3,2), deg30_15=round(calc_regress_deg(am.close[-30:-15], False),2), deg15_0=round(calc_regress_deg(am.close[-15:], False),2),deg_f=round(deg_full,2), atr=round(am.atr(10, length=15), 3), tr=round(am.atr(1, length=2), 3),atr_40=round(am.atr(40, length=42), 3), time=bar.datetime, price=bar.close_price, ma=round(ma, 2), std_40=round(std_val, 2),mean40=round(mean_val,2), mean_std=np.mean(self.std_range.data[-5:]), std_10=round(std_val2,2), mean30_10=round(mean_val4,2), mean10=round(mean_val2,2), vol=am.volume[-1], std_range=self.std_range.data[-1:-5:-1], range=am.range[-1:-5:-1].tolist(), range_sum=np.sum(am.range[-5:]), pattern=list(map(lambda x: KLINE_PATTERN_CHINESE[x], self.pattern_record.keys())), ma120t=self.ma120_track, ma120t_list=self.ma120_track_list[-1:-10:-1], ma120t_sort=sorted(self.ma120_track_list[-20:-1], key=abs), ma120t_sum=np.sum(self.ma120_track_list[-20:-1] + [self.ma120_track]), ma120t_mean=np.mean(self.ma120_track_list[-20:-1] + [self.ma120_track]), ma120t_std=np.std(self.ma120_track_list[-20:-1] + [self.ma120_track]), ma_info=list(map(lambda x:x["std"], self.ma_info[-1:])), wave_cnt=len(wave), wave_r_sum=wave_r_sum, atr_mean=np.mean(am.atr(20, array=True,length=240)[-200:]) )) return calc_data
def ma_trend_strategy(self, am: ArrayManager, bar: BarData, calc_data): context = self.trend_context if context == None: return deg = context["deg"] data = context["data"] sign = context["sign"] if self.pos == 0: ma120 = self.ma_info.info[120] deg120 = calc_regress_deg(ma120[-5:], False) if deg < 0: calc_data["trade_open"] = "开空,deg={}".format(deg) sign() ratio = 0.2 if deg120 < 0 else 0.1 return self.trade_mgr.short(bar.close_price, ratio, {}) elif deg > 0: calc_data["trade_open"] = "开多,deg={}".format(deg) sign() ratio = 0.2 if deg120 > 0 else 0.1 return self.trade_mgr.buy(bar.close_price, ratio, {})
def ma_trend_strategy(self, am:ArrayManager, bar:BarData, calc_data, setting={"len":40, "atr":40, "atr_valve":0.09, "mid_sign":(10,30)}): # 60个bar后取消 if self.trend_record.std_val is not None: if len(self.trend_record.close) >= 60: self.trend_record.close = [] self.trend_record.std_val = None else: self.trend_record.close.append(bar.close_price) # 如果有新的bar,则覆盖 ma5_std = self.ma_info[-1]["ma5"] if ma5_std <= 0.16: self.trend_record.close = [] self.trend_record.std_val = ma5_std return if self.trend_record.std_val is not None and \ ma5_std > 0.8: y_fit = reg_util.regress_y_polynomial(self.trend_record.close, zoom=True) deg = calc_regress_deg(y_fit[:10], False) if deg < 0: # if k < 20 and d < 10 and j < 10: # if kdj[2] < 10: if self.pos == 0: calc_data["trade_open"] = "开空,deg={}".format(deg) return self.short(bar.close_price, 1, type=OrderType.MARKET) else: # if k > 80 and d > 90 and j > 90: # if kdj[2] > 90: if self.pos == 0: calc_data["trade_open"] = "开多,deg={}".format(deg) return self.buy(bar.close_price, 1, type=OrderType.MARKET)
def generate_data(self, am: ArrayManager, bar: BarData): offset = -self.offset offset_m = int(offset / 2) std_val3 = np.std(np.array(am.range[-30:-10])) kdj_val = am.kdj() has_kdj_recore = False k = kdj_val["k"] d = kdj_val["d"] j = kdj_val["j"] if (k[-1] > 75 and d[-1] > 75 and j[-1] > 75) or \ (k[-1] < 25 and d[-1] < 25 and j[-1] < 75): if (j[-2] < k[-2] or j[-2] < d[-2]) and (j[-1] > k[-1] and j[-1] > d[-1]) \ or \ (j[-2] > k[-2] or j[-2] > d[-2]) and (j[-1] < k[-1] and j[-1] < d[-1]): has_kdj_recore = True t = bar.datetime self.kdj_record.append( (t.strftime("%H:%M:%S"), round(k[-1], 3), round(d[-1], 3), round(j[-1], 3))) deg1 = calc_regress_deg(am.close[offset:offset_m], False) deg2 = calc_regress_deg(am.close[offset_m:], False) deg3 = calc_regress_deg(am.close[-10:], False) deg_full = calc_regress_deg(am.close[offset:], False) macd = am.macd(20, 40, 16) calc_data = (dict( kdj=[ round(kdj_val["k"][-1], 2), round(kdj_val["d"][-1], 2), round(kdj_val["j"][-1], 2) ], cci_20=am.cci(20), rsi=am.rsi(20), adx=am.adx(20), boll=am.boll(20, 3.4), macd=[round(macd[0], 2), round(macd[1], 2), round(macd[2], 2)], deg40_20=round(deg1, 2), deg20_0=round(deg2, 2), deg20_10=round(calc_regress_deg(am.close[-20:-10], False), 2), deg30_15=round(calc_regress_deg(am.close[-30:-15], False), 2), deg15_0=round(calc_regress_deg(am.close[-15:], False), 2), deg_f=round(deg_full, 2), deg30_10=round(calc_regress_deg(am.close[-30:-10], False), 2), deg10_0=round(deg3, 2), atr=round(am.atr(10, length=15), 3), tr=round(am.atr(1, length=2), 3), atr_40=round(am.atr(40, length=42), 3), time=bar.datetime, price=bar.close_price, mean_std=np.mean(self.std_range.data[-5:]), vol=am.volume[-1], std_range=self.std_range.data[-1:-5:-1], range=am.range[-1:-5:-1].tolist(), range_sum=np.sum(am.range[-5:]), pattern=list( map(lambda x: KLINE_PATTERN_CHINESE[x], self.pattern_record.keys())), atr_mean=np.mean(am.atr(20, array=True, length=240)[-200:]), )) if self.ma_info.info.index.size >= 31: ma5 = self.ma_info.info[5][-31:] x = AnalyseWave(ma5) calc_data["ma5_info"] = x.optimize ma10 = self.ma_info.info[10][-31:] x = AnalyseWave(ma10) calc_data["ma10_info"] = x.optimize return calc_data
def generate_data(self, am:ArrayManager, bar:BarData): offset = -self.offset offset_m = int(offset / 2) calc_nums = np.array(self.ma_tag[-offset:-1]) # var_val = np.var(calc_nums) std_val = np.std(calc_nums) std_val2 = np.std(np.array(self.ma_tag[-10:-1])) std_val3 = np.std(np.array(am.range[-30:-10])) ma = self.ma_tag[-1] mean_val = np.mean(calc_nums) mean_val2 = np.mean(np.array(self.ma_tag[-5:-1])) mean_val3 = np.mean(np.array(self.ma_tag[-20:-1])) mean_val4 = np.mean(np.array(self.ma_tag[-30:-5])) kdj_val = am.kdj() has_kdj_recore = False k = kdj_val["k"] d = kdj_val["d"] j = kdj_val["j"] if (k[-1] > 75 and d[-1] > 75 and j[-1] > 75) or \ (k[-1] < 25 and d[-1] < 25 and j[-1] < 75): if (j[-2] < k[-2] or j[-2] < d[-2]) and (j[-1] > k[-1] and j[-1] > d[-1]) \ or \ (j[-2] > k[-2] or j[-2] > d[-2]) and (j[-1] < k[-1] and j[-1] < d[-1]): has_kdj_recore = True t = local_to_eastern(bar.datetime.timestamp()) self.kdj_record.append((t.strftime("%H:%M:%S"), round(k[-1], 3), round(d[-1], 3), round(j[-1], 3))) deg1 = calc_regress_deg(am.close[offset : offset_m], False) deg2 = calc_regress_deg(am.close[offset_m :], False) deg3 = calc_regress_deg(am.close[-10 :], False) deg_full = calc_regress_deg(am.close[offset :], False) wave = self.wave(am.close[-30:]) wave_r_sum = np.sum(wave["range"]) macd=am.macd(20,40, 16) calc_data = (dict( ma_info=self.ma_info[-1:], kdj=[round(kdj_val["k"][-1],2),round(kdj_val["d"][-1],2),round(kdj_val["j"][-1],2)], cci_20=am.cci(20),rsi=am.rsi(20),adx=am.adx(20),boll=am.boll(20, 3.4), macd=[round(macd[0],2),round(macd[1],2),round(macd[2],2)], deg40_20=round(deg1,2), deg20_0=round(deg2,2), deg20_10=round(calc_regress_deg(am.close[-20:-10], False),2), deg30_10=round(calc_regress_deg(am.close[-30:-10], False),2),deg10_0=round(deg3,2), deg30_15=round(calc_regress_deg(am.close[-30:-15], False),2), deg15_0=round(calc_regress_deg(am.close[-15:], False),2),deg_f=round(deg_full,2), atr=round(am.atr(10, length=15), 3), tr=round(am.atr(1, length=2), 3),atr_40=round(am.atr(40, length=42), 3), time=bar.datetime, price=bar.close_price, ma=round(ma, 2), std_40=round(std_val, 2),mean40=round(mean_val,2), mean_std=np.mean(self.std_range.data[-5:]), std_10=round(std_val2,2), mean30_10=round(mean_val4,2), mean10=round(mean_val2,2), vol=am.volume[-1], std_range=self.std_range.data[-1:-5:-1], range=am.range[-1:-5:-1].tolist(), range_sum=np.sum(am.range[-5:]), pattern=list(map(lambda x: KLINE_PATTERN_CHINESE[x], self.pattern_record.keys())), ma120t=self.ma120_track, ma120t_list=self.ma120_track_list[-1:-10:-1], ma120t_sort=sorted(self.ma120_track_list[-20:-1], key=abs), ma120t_sum=np.sum(self.ma120_track_list[-20:-1] + [self.ma120_track]), ma120t_mean=np.mean(self.ma120_track_list[-20:-1] + [self.ma120_track]), ma120t_std=np.std(self.ma120_track_list[-20:-1] + [self.ma120_track]), wave_cnt=len(wave), wave_r_sum=wave_r_sum, atr_mean=np.mean(am.atr(20, array=True,length=240)[-200:]), kdj_record=self.kdj_record[-10:], )) if self.ma_info[-1]["ma5"] <= 0.16: calc_data["kdj_key"] = True return calc_data
def reverse2_strategy(self, am:ArrayManager, bar:BarData, calc_data, setting={"len":40, "atr":40, "atr_valve":0.09, "mid_sign":(10,30)}): length = 30 offset1 = -30 offset2 = int(-10) close = am.close deg1 = calc_regress_deg(close[-30:-8], False) deg2 = calc_regress_deg(close[-8:], False) if deg1 > 0 and deg2 > 0 or \ deg1 < 0 and deg2 < 0: return if not (abs(deg1) > 0.15 and abs(deg2) > 0.15 and (abs(deg1) + abs(deg2)) > 0.35) : return close = am.close[-length:] min_val = np.min(close) max_val = np.max(close) mid_val = max_val if deg1 > 0 else min_val mid_pos = np.where(close == mid_val)[0][0] if mid_pos < setting["mid_sign"][0] or mid_pos > setting["mid_sign"][1]: return start_val = np.min(close[:mid_pos]) if deg1 > 0 else np.max(close[:mid_pos]) start_pos = np.where(close == start_val)[0][0] l = mid_pos - start_pos # pos2 = np.where(close == min_val)[0][0] kdj = am.kdj() k = kdj["k"][-1] d = kdj["d"][-1] j = kdj["j"][-1] x_fit = reg_util.regress_y_polynomial(close[:mid_pos], zoom=True) deg1_remake = calc_regress_deg(x_fit[:abs(mid_pos)], False) y_fit = reg_util.regress_y_polynomial(close[mid_pos:], zoom=True) deg2_remake = calc_regress_deg(y_fit[:abs(mid_pos)], False) # print(start_pos, mid_pos, deg1, deg2, deg1_remake, deg2_remake, l, start_val, mid_val) cci = am.cci(20) ma60 = am.sma(60) if deg2 < 0: # if k < 20 and d < 10 and j < 10: # if kdj[2] < 10: if cci < -100 and bar.close_price < ma60: if self.pos == 0: calc_data["trade_open"] = "开空,deg={},cci={}".format(deg2, cci) return self.short(bar.close_price, 1, type=OrderType.MARKET) elif self.pos > 0: calc_data["trade_close"] = "平多后做空仓,deg={},cci={}".format(deg2, cci) order_id_cover = self.sell(bar.close_price, abs(self.volumn), type=OrderType.MARKET) order_id_buy = self.short(bar.close_price, 1, type=OrderType.MARKET) return order_id_cover.extend(order_id_buy) else: # if k > 80 and d > 90 and j > 90: # if kdj[2] > 90: if cci > 100 and bar.close_price > ma60: if self.pos == 0: calc_data["trade_open"] = "开多,deg={},cci={}".format(deg2, cci) return self.buy(bar.close_price, 1, type=OrderType.MARKET) elif self.pos < 0: calc_data["trade_close"] = "平空后多仓,deg={},cci={}".format(deg2, cci) order_id_cover = self.cover(bar.close_price, abs(self.volumn), type=OrderType.MARKET) order_id_buy = self.buy(bar.close_price, 1, type=OrderType.MARKET) return order_id_cover.extend(order_id_buy)