def add_data_to_day_price(day_price: DataFrame,
index_day_price: DataFrame) -> DataFrame:
group_list = []
grouped = day_price.groupby("ticker_id")
for key, group in grouped:
group = group.reset_index(drop=True)
group["sma_10"] = sma(group["close"], 10)
group["increase_ratio_3"] = increase_from_lowest_price(
group["low"], group["close"], 3)
group["sma"] = sma(group["close"], 5)
group_list.append(group)
day_price = pd.concat(group_list, axis=0)
day_price = day_price.reset_index(drop=True)
group_list = []
grouped = day_price.groupby("ticker_id")
for key, group in grouped:
increase_condition = group["sma"] >= 0.1
# Result
group["#result"] = increase_condition
group["#priority"] = group["increase_ratio_3"].shift(
1) + group["increase_ratio_3"].shift(2)
group_list.append(group)
day_price = pd.concat(group_list, axis=0)
day_price = day_price.reset_index(drop=True)
# Market Timing
index_day_price["sma_3"] = sma(index_day_price["close"], 3)
index_day_price["sma_5"] = sma(index_day_price["close"], 5)
index_day_price["sma_10"] = sma(index_day_price["close"], 10)
index_day_price["#market_timing"] = (
(index_day_price["close"] > index_day_price["sma_3"])
| (index_day_price["close"] > index_day_price["sma_5"])
| (index_day_price["close"] > index_day_price["sma_10"]))
index_day_price = index_day_price.set_index("date")
return day_price, index_day_price
def add_data_to_day_price(day_price: DataFrame,
index_day_price: DataFrame) -> DataFrame:
group_list = []
grouped = day_price.groupby("ticker_id")
for key, group in grouped:
group = group.reset_index(drop=True)
group["sma_10"] = sma(group["close"], 10)
group["increase_ratio_3"] = increase_from_lowest_price(
group["low"], group["close"], 3)
group["pdi_5"] = pdi(group["high"], group["low"], group["close"], 5,
MovingAverage.ema)
group["pdi_5_sto"] = stochastic_fast_k(group["pdi_5"], group["pdi_5"],
group["pdi_5"], 20)
group["pdi_5_sto_pct_change_3"] = pct_change(group["pdi_5_sto"], 3)
group_list.append(group)
day_price = pd.concat(group_list, axis=0)
day_price = day_price.reset_index(drop=True)
group_list = []
grouped = day_price.groupby("ticker_id")
for key, group in grouped:
increase_condition1 = (group["pdi_5_sto_pct_change_3"].shift(1) >=
0.1) | (group["pdi_5_sto_pct_change_3"].shift(2)
>= 0.1)
increase_condition = increase_condition1
# Result
group["#result"] = increase_condition
group["#priority"] = group["increase_ratio_3"].shift(
1) + group["increase_ratio_3"].shift(2)
group_list.append(group)
day_price = pd.concat(group_list, axis=0)
day_price = day_price.reset_index(drop=True)
# Market Timing
index_day_price["sma_3"] = sma(index_day_price["close"], 3)
index_day_price["sma_5"] = sma(index_day_price["close"], 5)
index_day_price["sma_10"] = sma(index_day_price["close"], 10)
index_day_price["#market_timing"] = (
(index_day_price["close"] > index_day_price["sma_3"])
| (index_day_price["close"] > index_day_price["sma_5"])
| (index_day_price["close"] > index_day_price["sma_10"]))
index_day_price = index_day_price.set_index("date")
return day_price, index_day_price
def add_data_to_day_price(day_price: DataFrame,
index_day_price: DataFrame) -> DataFrame:
group_list = []
grouped = day_price.groupby("ticker")
for key, group in grouped:
group = group.reset_index(drop=True)
group["sma_10"] = sma(group["close"], 10)
group["sma_20"] = sma(group["close"], 20)
group["sma_tr_val_5"] = sma(group["tr_val"], 5)
group["ibs"] = ibs(group["high"], group["low"], group["close"])
group["increase_ratio_3"] = increase_from_lowest_price(
group["low"], group["close"], 3)
group["decrease_ratio_3"] = decrease_from_highest_price(
group["high"], group["close"], 3)
group_list.append(group)
day_price = pd.concat(group_list, axis=0)
day_price = day_price.reset_index(drop=True)
group_list = []
grouped = day_price.groupby("date")
for key, group in grouped:
group = group.reset_index(drop=True)
group["rank_tr_val"] = rank(group["tr_val"])
group["rank_tr_val_5"] = rank(group["sma_tr_val_5"])
group_list.append(group)
day_price = pd.concat(group_list, axis=0)
day_price = day_price.reset_index(drop=True)
group_list = []
grouped = day_price.groupby("ticker")
for key, group in grouped:
# Condition1
increase_condition1 = (group["increase_ratio_3"].shift(1) >= 0.1) | (
group["increase_ratio_3"].shift(2) >= 0.1)
increase_condition = increase_condition1
decrease_condition1 = (group["decrease_ratio_3"] <
0) & (group["decrease_ratio_3"] > -0.1)
decrease_condition2 = group["ibs"] < 0.25
decrease_condition = decrease_condition1 & decrease_condition2
liquidity_condition1 = group["rank_tr_val_5"] > 0.8
liquidity_condition = liquidity_condition1
# Result
group[
"#result"] = increase_condition & decrease_condition & liquidity_condition
group["#priority"] = group["increase_ratio_3"]
group_list.append(group)
day_price = pd.concat(group_list, axis=0)
day_price = day_price.reset_index(drop=True)
# Market Timing
index_day_price["sma_3"] = sma(index_day_price["close"], 3)
index_day_price["sma_5"] = sma(index_day_price["close"], 5)
index_day_price["sma_10"] = sma(index_day_price["close"], 10)
index_day_price["#market_timing"] = (
(index_day_price["close"] > index_day_price["sma_3"])
| (index_day_price["close"] > index_day_price["sma_5"])
| (index_day_price["close"] > index_day_price["sma_10"]))
index_day_price = index_day_price.set_index("date")
return day_price, index_day_price
def add_data_to_day_price(day_price: DataFrame,
index_day_price: DataFrame) -> DataFrame:
group_list = []
grouped = day_price.groupby("ticker_id")
for key, group in grouped:
group = group.reset_index(drop=True)
group["sma_10"] = sma(group["close"], 10)
group["sma_20"] = sma(group["close"], 20)
group["sma_tr_val_5"] = sma(group["tr_val"], 5)
group["pct_change_1"] = pct_change(group["close"], 1)
group["ibs"] = ibs(group["high"], group["low"], group["close"])
group["increase_ratio_3"] = increase_from_lowest_price(
group["low"], group["close"], 3)
group_list.append(group)
day_price = pd.concat(group_list, axis=0)
day_price = day_price.reset_index(drop=True)
group_list = []
grouped = day_price.groupby("date")
for key, group in grouped:
group = group.reset_index(drop=True)
group["rank_tr_val_5"] = rank(group["sma_tr_val_5"])
group_list.append(group)
day_price = pd.concat(group_list, axis=0)
day_price = day_price.reset_index(drop=True)
group_list = []
grouped = day_price.groupby("ticker_id")
for key, group in grouped:
decrease_condition1 = group["open"] > group["close"]
decrease_condition2 = group["pct_change_1"] < 0
decrease_condition3 = group["ibs"] < 0.25
decrease_condition = (decrease_condition1
| decrease_condition2) & decrease_condition3
# Result
group["#result"] = decrease_condition
group["#priority"] = group["increase_ratio_3"].shift(
1) + group["increase_ratio_3"].shift(2)
group_list.append(group)
day_price = pd.concat(group_list, axis=0)
day_price = day_price.reset_index(drop=True)
# Market Timing
index_day_price["sma_3"] = sma(index_day_price["close"], 3)
index_day_price["sma_5"] = sma(index_day_price["close"], 5)
index_day_price["sma_10"] = sma(index_day_price["close"], 10)
index_day_price["#market_timing"] = (
(index_day_price["close"] > index_day_price["sma_3"])
| (index_day_price["close"] > index_day_price["sma_5"])
| (index_day_price["close"] > index_day_price["sma_10"]))
index_day_price = index_day_price.set_index("date")
return day_price, index_day_price
def add_data_to_day_price(day_price: DataFrame,
index_day_price: DataFrame) -> DataFrame:
group_list = []
grouped = day_price.groupby("ticker_id")
for key, group in grouped:
group = group.reset_index(drop=True)
group["sma_10"] = sma(group["close"], 10)
group["sma_20"] = sma(group["close"], 20)
group["sma_tr_val_5"] = sma(group["tr_val"], 5)
group["pct_change_1"] = pct_change(group["close"], 1)
group["ts_max_20"] = ts_max(group["close"], 20)
group["ibs"] = ibs(group["high"], group["low"], group["close"])
group["increase_ratio_3"] = increase_from_lowest_price(
group["low"], group["close"], 3)
group["pdi_5"] = pdi(group["high"], group["low"], group["close"], 5,
MovingAverage.ema)
group["pdi_5_sto"] = stochastic_fast_k(group["pdi_5"], group["pdi_5"],
group["pdi_5"], 20)
group["pdi_5_sto_pct_change_3"] = pct_change(group["pdi_5_sto"], 3)
group["pivot_standard"] = pivot_standard(group["high"], group["low"],
group["close"])
group_list.append(group)
day_price = pd.concat(group_list, axis=0)
day_price = day_price.reset_index(drop=True)
group_list = []
grouped = day_price.groupby("date")
for key, group in grouped:
group = group.reset_index(drop=True)
group["rank_tr_val_5"] = rank(group["sma_tr_val_5"])
group_list.append(group)
day_price = pd.concat(group_list, axis=0)
day_price = day_price.reset_index(drop=True)
group_list = []
grouped = day_price.groupby("ticker_id")
for key, group in grouped:
increase_condition1 = (group["pdi_5_sto_pct_change_3"].shift(1) >=
0.1) | (group["pdi_5_sto_pct_change_3"].shift(2)
>= 0.1)
increase_condition = increase_condition1
decrease_condition1 = group["open"] > group["close"]
decrease_condition2 = group["pct_change_1"] < 0
decrease_condition3 = group["ibs"] < 0.25
decrease_condition = (decrease_condition1
| decrease_condition2) & decrease_condition3
liquidity_condition1 = group["rank_tr_val_5"] > 0.8
liquidity_condition = liquidity_condition1
# Result
group[
"#result"] = increase_condition & decrease_condition & liquidity_condition
group["#priority"] = group["increase_ratio_3"].shift(
1) + group["increase_ratio_3"].shift(2)
group_list.append(group)
day_price = pd.concat(group_list, axis=0)
day_price = day_price.reset_index(drop=True)
# Market Timing
index_day_price["sma_3"] = sma(index_day_price["close"], 3)
index_day_price["sma_5"] = sma(index_day_price["close"], 5)
index_day_price["sma_10"] = sma(index_day_price["close"], 10)
index_day_price["#market_timing"] = (
(index_day_price["close"] > index_day_price["sma_3"])
| (index_day_price["close"] > index_day_price["sma_5"])
| (index_day_price["close"] > index_day_price["sma_10"]))
index_day_price = index_day_price.set_index("date")
return day_price, index_day_price