def strategy_run(tradingDays,symbols, dataPath, dataReadType, outputpath, signal, lbwindow, lawindow,paraset,Asset,Fee,Name):
stats_ = list()
for tradingDay in [(tradingDays)]:
#print('hapi' + str(list(tradingDays)))
#print(tradingDay)
tradingDay = tradingDay.replace('-','')
print('Processing tradingday = ', tradingDay)
tradingSymbols = list(symbols[list(
map(lambda symbol: CheckStockSuspend(symbol, dataPath, tradingDay,Asset),
symbols.values.tolist()))])
if len(tradingSymbols) == 0:
continue
if Asset == 'Future':
data = Data.Data(dataPath, '', tradingDay, futureSymbols=tradingSymbols, dataReadType=dataReadType, RAWDATA='True')
signalTester = SignalTester.SignalTester(data, dailyData=pd.DataFrame, tradeDate=tradingDay, symbol=tradingSymbols,fee = Fee, dataSavePath=outputpath,type = 'Future')
else:
data = Data.Data(dataPath, tradingSymbols, tradingDay, dataReadType=dataReadType, RAWDATA='True')
# todo signalTester
signalTester = SignalTester.SignalTester(data, dailyData=pd.DataFrame, tradeDate=tradingDay,symbol=tradingSymbols, fee = Fee,dataSavePath=outputpath)
# signalTester.CompareSectorAndStock(symbols[0], orderType='netMainOrderCashFlow')
stsDf = list()
strategyResult = list()
for symbol in tradingSymbols:
temp = signalTester.CheckSignal(symbol,signal,lbwindow,lawindow,paraset)
if temp is not None:
stsDf.append(temp)
if Asset =='Future':
quoteData = data.futureData[symbol]
else:
quoteData = data.quoteData[symbol]
#print(quoteData)
##todo future strategy
if Asset == 'Future':
strategy = Strategy.Strategy(symbol, round(1000000/quoteData['midp'].iloc[-1],-2), quoteData, signal, tradingDay,lbwindow, lawindow,6, 'lawindow',fee = Fee,outputpath = './strategy/' + tradingDay, stockType = 'low',asset = 'Future')
else:
strategy = Strategy.Strategy(symbol, round(1000000/quoteData['midp'].iloc[-1],-2), quoteData, signal,tradingDay,lbwindow, lawindow,6, 'lawindow',fee = Fee,outputpath = './strategy/' + tradingDay, stockType = 'low')
strategy.SummaryStrategy()
strategy.Plot()
#print(strategy.sts)
strategyResult.append(strategy.sts)
#print(len(stsDf))
if len(stsDf) > 0:
##保证至少有1
#print(tradingDay)
pd.concat(stsDf,0).to_csv(outputpath+'./' + tradingDay +Name+ '.csv')
pd.concat(strategyResult,0).to_csv('./strategy/' + tradingDay +Name+ '.csv')
return 0
def start_round(sock, team_id):
game = Game(sock, team_id)
game.register()
with open("log.txt", "a+") as f:
f.write("success register")
while True:
game.receive_msg()
msg_name = game.get_msg_name()
if msg_name == "round":
game.round_start()
control = Control(team_id, game.our_tank_id, game.maps, game.data)
strategy = Strategy(game)
# 操作函数
game.set_tank_msg()
try:
# AI_zx.start_zx(game,control)
new_start.start_5_28(game, control, strategy)
except Exception as e:
log.log("start:")
log.log(e)
control.send_msg(sock)
game.round_clear()
elif msg_name == "leg_start":
game.leg_start()
elif msg_name == MSG_NAME_LEG_END:
print(game.data["msg_data"])
game.leg_clear()
elif msg_name == MSG_NAME_GAME_OVER:
print("Game Over ...")
break
def learnOnSet(set_name):
sets = db.readSet(set_name)
strategy = Strategy.Strategy()
for i in sets:
var = i
self.learnVariant(strategy)
db.save(strategy, "result.txt")
def update_caches_with_latest():
try:
strategy_name = sys.argv[1]
except:
strategy_name = "AM_PM_Change_Average_strat"
# print("enter a valid strategy name")
# exit(0)
local_dir = os.getcwd()
cp = CalendarParser("white_list.txt", "dates")
cp.pullandStoreEarningsDates(append_new_data=True)
cp.loadCached()
strategy = Strategy.Strategy(
strategy_name, None, None, None,
difference_instructions_by_strategy[strategy_name], "am")
strategy.pull_cache_data()
strategy.gather_data(cp.earnings_map,
None,
yahoo_daily=True,
write=True,
append_cache=True)
earnings_return_data_map = strategy.pull_cache_data()
if strategy_name == "AM_PM_Change_Average_strat":
strategy.generate_daily_csv_library(earnings_return_data_map,
"earnings_call_difference_data",
local_dir)
def __init__(self):
self.tb = TradeBook.TradeBook()
self.all_strategy = Strategy.Strategy()
self.cere = ''
self.file = open("Result.json", "w")
self.hisfile = open("History.txt", "w")
self.json_obj = {
"team": "RuntimeException",
"destination": "*****@*****.**",
"transactions": []
}
def run_strategy():
#moving average strategy
strategy = Strategy()
# get buy sell dataframe
bs_df = strategy.handle_data()
bs_df[['return_cum',
'strategy_return_cum']].dropna().plot(title='moving average',
style=['--', '-'])
plt.xticks(rotation=90)
plt.legend()
plt.show()
def start(self, strat, capital, commission):
self.initDatabase()
self.initPipelineTables()
self.initStatisticsTables()
self.initTradingTable()
self.initBookTables()
entryConditions, exitConditions = stratFuncs
strat = stra.Strategy(stratName, entryConditions, exitConditions)
self.databaseManager = dbm.DatabaseManager(self.dbRef, self.conn,
strat, auth)
self.databaseManager.setTradingParameters(self.exchange, self.ticker)
def __init__(self):
self.pf = PriceFeed.PriceFeed()
self.tb = TradeBook.TradeBook()
self.ip = '127.0.0.1'
self.port1 = 8211
self.port2 = 8212
self.all_strategy = Strategy.Strategy()
self.file = open("Result.json", "w")
self.json_obj = {
"team": "RuntimeException",
"destination": "*****@*****.**",
"transactions": []
}
def mousePressed(app, event):
if app.gamePage == True:
if ((app.pvpX <= event.x <= app.pvpX+app.buttonWidth) and
(app.buttonY <= event.y <= app.buttonY + app.buttonHeight)):
app.pvpMode = True
createMatrix(app)
app.timerDelay = 0
app.gamePage = False
elif ((app.spectateX <= event.x <= app.spectateX+app.buttonWidth) and
(app.buttonY <= event.y <= app.buttonY + app.buttonHeight)):
app.spectateMode = True
app.timerDelay = 0
createSpectatorMatrix(app)
app.gamePage = False
elif ((app.aiOneX <= event.x <= app.aiOneX+app.buttonWidth) and
(app.buttonY <= event.y <= app.buttonY + app.buttonHeight)):
app.aiOneMode = True
createMatrix(app)
app.gamePage = False
elif ((app.aiTwoX <= event.x <= app.aiTwoX+app.buttonWidth) and
(app.buttonY <= event.y <= app.buttonY + app.buttonHeight)):
app.aiTwoMode = True
createMatrix(app)
app.gamePage = False
elif ((app.aiThreeX <= event.x <= app.aiThreeX+app.buttonWidth) and
(app.buttonY <= event.y <= app.buttonY + app.buttonHeight)):
app.aiThreeMode = True
createMatrix(app)
app.gamePage = False
elif app.aiOneMode or app.aiThreeMode or app.aiTwoMode or app.pvpMode:
row, col = event.y // app.cellSize, event.x // app.cellSize
app.click = int(row), int(col)
if not app.pvpMode:
if not app.gameOver:
if app.width-2*app.cellSize+4 <= event.x <= app.width-app.cellSize-2 and app.height-60 <= event.y <= app.height-10:
s = Strategy.Strategy()
helperMove = s.best_strategy(app.board, app.playerSymbol)
helperBoard = move(app.board, app.playerSymbol, helperMove)
for i in range(len(app.board)):
if helperBoard[i] != app.board[i]:
app.moveAssist = (i//10, i%10)
if app.moveAssist in app.moves:
break
elif app.spectateMode:
resetApp(app)
app.spectateMode = True
app.timerDelay = 0
createSpectatorMatrix(app)
app.gamePage = False
def __init__(self, parent, tile_index, root):
self.width = 0
self.height = 0
self.parent = parent
self.tile_index = tile_index
self.root = root
self.strategy = Strategy()
self.img = None
self.draw(init=True)
return
def main():
agents, env, motivation = init_maze_task()
#env,motivation = init_simple_task()
#env,motivation = init_alter_task()
strat = Strategy(motivation)
''' First envs'''
#strat = OldStrategy()
agent = DullAgent(strat, ["▲", "■", "▶", "◀"])
#agent = TotalRecall(strat, ["▲", "■", "▶", "◀"])
#agent = CartesianAgent(strat, ["▲", "■", "▶", "◀"])
#Exécuter cet agent pour les premiers environnements (nombre d'actions différent)
''' Firsts Env'''
#agent = SmartAgent(strat,100, ["►", "◄"])
i = 0
while i < FLAGS.steps:
if i > FLAGS.steps - 20:
time.sleep(0.3)
action = agent.chooseExperience(i, FLAGS.steps)
#result = env.getResult(str(action)) # To use if the task is not a Maze
result = env.step(agents[0], action) # To use if the task is a Maze
reward = agent.get_reward(result)
if agent._name != "cartesian":
agent.memory(
) # TODO : ------- > comment this line to see memory usage efficiency
#agent.tracer(reward, i)
if FLAGS.debug:
print("--------------------------")
print("J'ai choisis : " + agent.symb[action])
print("J'ai eu : r" + str(result))
print("Pour : " + str(reward) + " pts")
i += 1
describe(agent)
def __init__(self):
self.time = 0
self.cillioned = [] # log the collision agents
self.deadTargets = []
self.threshold = 0
# add goods
self.goods = [Models.good() for i in xrange(10 * 8)]
# put goods in warehouse
indexupleft = 0 # index the goods position to mark letter
indexupright = 0
indexdownleft = 0
indexdownright = 0
for group in range(10):
offset = (0, 0)
if group % 2 == 0: # upper row
offset = ((3 * (group / 2) + 1) * L + margin_left,
margin_top + L)
elif group % 2 == 1: # lower row
offset = ((3 * (group / 2) + 1) * L + margin_left,
margin_top + 6 * L)
# print "for good in group ", group, " Offset: ", offset
for i in range(8):
if (i % 2 == 0): # on left
self.goods[i + group * 8].status = 0
self.goods[i + group * 8].x = offset[0]
self.goods[i + group * 8].y = offset[1] + int(i / 2) * L
self.goods[i + group *
8].agentx = self.goods[i + group * 8].x - 25
self.goods[i + group *
8].agenty = self.goods[i + group * 8].y + 25
if group % 2 == 0:
self.goods[
i + group *
8].goodfindletter = GOOD_FIND_LETTER_UP_LEFT[
indexupleft]
else:
self.goods[
i + group *
8].goodfindletter = GOOD_FIND_LETTER_DOWN_LEFT[
indexdownleft]
elif (i % 2 == 1): # on right
self.goods[i + group * 8].status = 0
self.goods[i + group * 8].x = offset[0] + L
self.goods[i + group * 8].y = offset[1] + int(i / 2) * L
self.goods[i + group *
8].agentx = self.goods[i + group * 8].x + 75
self.goods[i + group *
8].agenty = self.goods[i + group * 8].y + 25
if group % 2 == 0:
self.goods[
i + group *
8].goodfindletter = GOOD_FIND_LETTER_UP_RIGHT[
indexupright]
else:
self.goods[
i + group *
8].goodfindletter = GOOD_FIND_LETTER_DOWN_RIGHT[
indexdownright]
if group % 2 == 0:
indexupleft += 1
indexupright += 1
else:
indexdownleft += 1
indexdownright += 1
# change the good status ocuppied red colour
for i in xrange(len(GOOD_NUM)):
self.goods[GOOD_NUM[i]].status = 1
# add boxes
self.boxes = [Models.box() for i in xrange(50)]
offset = (75, 675)
for i in range(50):
self.boxes[i].status = 0
self.boxes[i].x = offset[0] + i * int(L / 4)
self.boxes[i].y = offset[1]
agents_theta = 270
battery_theta = agents_theta - 135
# add agents
self.agents = [Models.agent() for i in xrange(num_agents)]
for r in range(num_agents):
if r < num_agents / 2:
self.agents[r].x = world_width / 2 - r * L
else:
self.agents[r].x = world_width / 2 + r * L
self.agents[r].y = L
self.agents[r].r = L / 4
self.agents[r].status = 3 # all dead
self.agents[r].color = 3
self.agents[r].angle = Models.angle(agents_theta)
self.agents[r].lastangle = agents_theta
self.agents[r].agent_id = r
self.agents[r].batteryangle1 = Models.angle(battery_theta)
self.agents[r].batteryangle2 = Models.angle(battery_theta - 90)
self.agents[r].batterystatus = 1
# genetate graph
self.graph_nodes = Graph.generateGraphNodes()
self.graph = Graph.generateGraph()
self.startPoints = [Models.point() for i in xrange(num_areas)]
R1 = 'ABCDEF'
for r in range(num_areas):
self.startPoints[r].x = Graph.graph_nodes[R1[r]].x
self.startPoints[r].y = Graph.graph_nodes[R1[r]].y
self.startPoints[r].r = L / 3
self.endPoints = [Models.point() for i in xrange(num_areas)]
R1 = 'MNOPQR'
for r in range(num_areas):
self.endPoints[r].x = Graph.graph_nodes[R1[r]].x
self.endPoints[r].y = Graph.graph_nodes[R1[r]].y
self.endPoints[r].r = L / 3
self.chargePoints = [Models.point() for i in xrange(num_areas)]
R1 = 'STUVWY'
for r in range(num_areas):
self.chargePoints[r].x = Graph.graph_nodes[R1[r]].x
self.chargePoints[r].y = Graph.graph_nodes[R1[r]].y - 5
self.chargePoints[r].r = L / 3
# initial agents start and goal position letter
self.initstartgoal = InitStartGoal(self.agents)
# set the plan strategy
self.strategy = Strategy.Strategy(self.boxes, self.goods, self.agents,
self.graph_nodes, self.graph)
# set agent charge
self.agentcharge = ChargeThreshold.AgentCharge(self.agents,
self.graph,
self.goods,
self.graph_nodes,
thresholdenergy=3.5,
consumingrate=0.005,
chargingrate=0.05)
def __init__():
self.strategy = Strategy()
self.uav = UAV()
import json
import time
import calendar
dataFinal = {}
import matplotlib.pyplot as plt
from Strategy import *
stuff = []
stamps = []
with open("filed.json", "r") as tsla:
strat = Strategy()
for i in enumerate(tsla):
data = json.loads(i[1]) #figure out later
timeStamp = data["query"]["created"]
timeStamp = calendar.timegm(
time.strptime(timeStamp, "%Y-%m-%dT%H:%M:%SZ"))
print strat.mean_strategy(data)
stamps.append(timeStamp)
stuff.append(data["query"]["results"]["quote"][0]["Ask"])
dataFinal[timeStamp] = data["query"]["results"]["quote"][0]["Ask"]
plt.plot(stamps, stuff)
plt.show()
def addStrategy(self, stratName, entryCond, exitCond):
self.strategy = strat.Strategy(stratName, entryCond, exitCond)
""" # These comments can be removed if you want to download the data
nyse = se.Securities('ticker_universe.csv',mkt_cap_cutoff)
total_df = nyse.get_stock_data(start_date,end_date,data_source)
total_df.to_csv('all_stocks_mktcap.csv')
market_data = nyse.get_market_data(start_date,end_date,data_source)
market_data.to_csv('Market_Index.csv')
market_data['Date'] = market_data.index; market_data = market_data.reset_index(drop=True)
"""
# These two statements are used to load already downloaded stock and market index data
market_data = pd.read_csv('Market_Index.csv',parse_dates=['Date']);
total_df_main = pd.read_csv('all_stocks_mktcap.csv',parse_dates = ['Date']); del total_df_main['Unnamed: 0']
#-----------------------------------------------------------------------
strategy = st.Strategy(train_date_start,train_date_end,test_date_start,test_date_end)
pnl_train = 0; adr_train = 0; sr_train = 0; mdd_train = 0
count = 0
for n in nvalues:
for m in mvalues:
for L in Lvalues:
for U in Uvalues:
total_df = strategy.compute_factors(n,m,L,total_df_main)
weights = strategy.compute_factor_weights(total_df)
total_df['MScore'] = np.matmul(total_df.iloc[:,4:-1],weights)
portfolio = pf.Portfolio(nstocks,init_portfolio_value,avg_trading_vol,tcost)
train_df = total_df[(total_df['Date']>=pd.to_datetime(train_date_start)) & \
(total_df['Date']<=pd.to_datetime(train_date_end))].reset_index(drop=True)
portfolio_df_train = portfolio.compute_portfolio_normal(train_date_start,train_date_end,train_df,U)
from Strategy import *
class k():
def __init__(self):
self.value = -1
m = k()
b = Strategy()
b.best_strategy('.' * 27 + "ox......xo" + '.' * 27, '@', m)
print(m.value)
def __init__(self):
# self.goods_timestep_cnt = 0 # record the times
self.collision_total_cnt = 0 # record the collisions
self.collision_agents = [] # log the collision agents
self.start_time_mark = 0
self.stop_mark = 0
self.start_time = 0
# add shelves
self.shelves = [Models.shelves() for i in xrange(21 * 8)]
# put shelves in warehouse
row1left = 0 # index the goods position to mark letter
row2left = 0
row3left = 0
row1right = 0
row2right = 0
row3right = 0
for group in xrange(21):
offset = (0, 0)
if group % 3 == 0: # row1
offset = ((3 * (group / 3) + 1) * L + margin_left,
margin_top + L)
elif group % 3 == 1: # row2
offset = ((3 * (group / 3) + 1) * L + margin_left,
margin_top + 6 * L)
elif group % 3 == 2: # row3
offset = ((3 * (group / 3) + 1) * L + margin_left,
margin_top + 11 * L)
# print "for good in group ", group, " Offset: ", offset
for i in range(8):
if (i % 2 == 0): # on left
self.shelves[i + group * 8].id = i + group * 8
self.shelves[i + group * 8].status = 1
# every shelf has 10 class goods goods_class_index
for goods_type in xrange(
self.shelves[i + group * 8].id * 10,
self.shelves[i + group * 8].id * 10 + 10):
self.shelves[i +
group * 8].goods_class.append(goods_type)
self.shelves[i + group * 8].shelf_color = 'red'
self.shelves[i + group * 8].x = offset[0]
self.shelves[i + group * 8].y = offset[1] + int(i / 2) * L
self.shelves[i + group *
8].agentx = self.shelves[i + group * 8].x - 25
self.shelves[i + group *
8].agenty = self.shelves[i + group * 8].y + 25
if group % 3 == 0: # row1
self.shelves[i + group *
8].shelf_find_letter = ROW1_LEFT[row1left]
elif group % 3 == 1: # row2
self.shelves[i + group *
8].shelf_find_letter = ROW2_LEFT[row2left]
elif group % 3 == 2: # row3
self.shelves[i + group *
8].shelf_find_letter = ROW3_LEFT[row3left]
elif (i % 2 == 1): # on right
self.shelves[i + group * 8].id = i + group * 8
self.shelves[i + group * 8].status = 1
for goods_type in xrange(
self.shelves[i + group * 8].id * 10,
self.shelves[i + group * 8].id * 10 + 10):
self.shelves[i +
group * 8].goods_class.append(goods_type)
self.shelves[i + group * 8].shelf_color = 'red'
self.shelves[i + group * 8].x = offset[0] + L
self.shelves[i + group * 8].y = offset[1] + int(i / 2) * L
self.shelves[i + group *
8].agentx = self.shelves[i + group * 8].x + 75
self.shelves[i + group *
8].agenty = self.shelves[i + group * 8].y + 25
if group % 3 == 0: # row1
self.shelves[
i + group *
8].shelf_find_letter = ROW1_RIGHT[row1right]
elif group % 3 == 1: # row2
self.shelves[
i + group *
8].shelf_find_letter = ROW2_RIGHT[row2right]
elif group % 3 == 2: # row3
self.shelves[
i + group *
8].shelf_find_letter = ROW3_RIGHT[row3right]
if group % 3 == 0:
row1left += 1
row1right += 1
elif group % 3 == 1:
row2left += 1
row2right += 1
elif group % 3 == 2:
row3left += 1
row3right += 1
# generate graph
self.graph_nodes = Graph.generateGraphNodes()
self.graph = Graph.generateGraph()
# the agent charging position and the box position
self.chargePoints = [Models.point() for i in xrange(NUM_AGENTS)]
agents_theta = -90
battery_theta = agents_theta - 135
# add agents
self.agents = [Models.agent() for i in xrange(NUM_AGENTS)]
for r in xrange(NUM_AGENTS):
if r < NUM_AGENTS / 2:
self.agents[r].x = world_width / 2 - r * L
else:
self.agents[r].x = world_width / 2 + r * L
self.agents[r].y = L
self.agents[r].r = L / 4
self.agents[r].status = 3 # all dead
self.agents[r].color = 3
self.agents[r].angle = Models.angle(agents_theta)
self.agents[r].last_angle = agents_theta
self.agents[r].agent_id = r
self.agents[r].battery_status = 1
self.agents[r].battery_angle1 = Models.angle(battery_theta)
self.agents[r].battery_angle2 = Models.angle(battery_theta - 90)
self.chargePoints[r].x = Graph.graph_nodes[CHARGING_PARK[r]].x
self.chargePoints[r].y = Graph.graph_nodes[CHARGING_PARK[r]].y
self.chargePoints[r].r = L / 3
# draw unloading mark
self.orderPoints = [Models.point() for i in xrange(ORDER_NUM)]
for r in xrange(ORDER_NUM):
self.orderPoints[r].x = Graph.graph_nodes[UNLOADING_POSITION[r]].x
self.orderPoints[r].y = Graph.graph_nodes[UNLOADING_POSITION[r]].y
self.orderPoints[r].r = L / 3
# draw the boxes
self.orderBoxes = [Models.boxes() for i in xrange(ORDER_NUM)]
for r in xrange(ORDER_NUM):
offset = (self.orderPoints[r].x, self.orderPoints[r].y)
self.orderBoxes[r].box_x = offset[0]
self.orderBoxes[r].box_y = offset[1] + L
# initial order
self.orderGoods = [Models.orderGoods() for i in xrange(ORDER_NUM)]
for r in xrange(ORDER_NUM):
self.orderGoods[r].order_id = r
self.orderGoods[r].order_class = GOODS_ORDER[r]
self.orderGoods[r].unloading_position = UNLOADING_POSITION[r]
# initial agents start and goal position letter task allocation
self.initStartGoal = GA_allocate.GA_task_allocate(
self.agents, self.shelves, self.orderGoods, self.graph_nodes,
self.graph)
# set the plan strategy
self.strategy = Strategy.Strategy(self.shelves, self.agents,
self.orderGoods, self.orderBoxes,
self.graph_nodes, self.graph)
# set agent charge
self.agentCharge = ChargeThreshold.AgentCharge(self.agents,
self.graph,
self.shelves,
self.graph_nodes,
threshold_energy=3,
consuming_rate=0.0001,
charging_rate=0.05)
def timerFired(app):
if app.isPaused == False and app.gameOver == False and app.gamePage == False:
if app.spectateMode == True:
s = Strategy.Strategy()
s2 = StrategyMed.Strategy()
if "." in app.board:
if len(possibleMoves(app.board, app.playerSymbol)) == 0:
createSpectatorMatrix(app)
else:
if app.playerSymbol == "@":
cpu_move = s.best_strategy(app.board, app.playerSymbol)
app.board = move(app.board, app.playerSymbol, cpu_move)
score(app)
createSpectatorMatrix(app)
else:
app.board, index = randommove(app.board, app.playerSymbol)
score(app)
createSpectatorMatrix(app)
app.playerSymbol = "o" if app.playerSymbol == "@" else "@"
elif "." not in app.board:
score(app)
app.gameOver = True
elif app.aiOneMode == True:
if "." in app.board:
if len(possibleMoves(app.board, app.playerSymbol)) == 0:
app.turnSwap = True
app.moveAssist = (-1, -1)
createMatrix(app)
else:
if app.playerSymbol == "@":
#player's turn
moves = possibleMoves(app.board, app.playerSymbol)
player_move = app.click
if player_move in app.moves:
app.turnSwap = True
app.moveAssist = (-1, -1)
app.board = move(app.board, app.playerSymbol, (player_move[0]*10) + player_move[1])
app.moves.clear()
score(app)
createMatrix(app)
elif app.playerSymbol == "o":
#ai's turn
app.board, index = randommove(app.board, app.playerSymbol)
app.moves.clear()
moves = possibleMoves(app.board, "@")
for i in range(len(moves)):
app.moves.append((moves[i]//10, moves[i]%10))
createMatrix(app)
app.turnSwap = True
app.moveAssist = (-1, -1)
score(app)
if app.turnSwap:
app.playerSymbol = "o" if app.playerSymbol == "@" else "@"
app.turnSwap = False
elif "." not in app.board:
score(app)
app.gameOver = True
elif app.aiTwoMode:
s = StrategyMed.Strategy()
if "." in app.board:
if len(possibleMoves(app.board, app.playerSymbol)) == 0:
app.turnSwap = True
app.moveAssist = (-1, -1)
createMatrix(app)
else:
if app.playerSymbol == "@":
moves = possibleMoves(app.board, app.playerSymbol)
player_move = app.click
if player_move in app.moves:
app.turnSwap = True
app.board = move(app.board, app.playerSymbol, (player_move[0]*10) + player_move[1])
app.moveAssist = (-1, -1)
app.moves.clear()
score(app)
createMatrix(app)
if app.playerSymbol == "o":
cpu_move = s.best_strategy(app.board, app.playerSymbol)
app.board = move(app.board, app.playerSymbol, cpu_move)
app.moves.clear()
moves = possibleMoves(app.board, "@")
for i in range(len(moves)):
app.moves.append((moves[i]//10, moves[i]%10))
createMatrix(app)
app.turnSwap = True
app.moveAssist = (-1, -1)
score(app)
if app.turnSwap:
app.playerSymbol = "o" if app.playerSymbol == "@" else "@"
app.turnSwap = False
elif "." not in app.board:
score(app)
app.gameOver = True
elif app.aiThreeMode:
s = Strategy.Strategy()
if "." in app.board:
if len(possibleMoves(app.board, app.playerSymbol)) == 0:
app.turnSwap = True
app.moveAssist = (-1, -1)
createMatrix(app)
else:
if app.playerSymbol == "@":
moves = possibleMoves(app.board, app.playerSymbol)
player_move = app.click
if player_move in app.moves:
app.turnSwap = True
app.board = move(app.board, app.playerSymbol, (player_move[0]*10) + player_move[1])
app.moveAssist = (-1, -1)
app.moves.clear()
score(app)
createMatrix(app)
if app.playerSymbol == "o":
cpu_move = s.best_strategy(app.board, app.playerSymbol)
app.board = move(app.board, app.playerSymbol, cpu_move)
app.moves.clear()
moves = possibleMoves(app.board, "@")
for i in range(len(moves)):
app.moves.append((moves[i]//10, moves[i]%10))
createMatrix(app)
app.turnSwap = True
app.moveAssist = (-1, -1)
score(app)
if app.turnSwap:
app.playerSymbol = "o" if app.playerSymbol == "@" else "@"
app.turnSwap = False
elif "." not in app.board:
score(app)
app.gameOver = True
elif app.pvpMode:
if "." in app.board:
if len(possibleMoves(app.board, app.playerSymbol)) == 0:
#equivalent to passing if no legal moves
app.turnSwap = True
createMatrix(app)
else:
if app.playerSymbol == "@":
#p1's (black) turn
player_move = app.click
if player_move in app.blackMoves:
app.turnSwap = True
app.board = move(app.board, app.playerSymbol, (player_move[0]*10) + player_move[1])
score(app)
app.whiteMoves.clear()
whiteMoves = possibleMoves(app.board, "o")
for i in range(len(whiteMoves)):
#clear previous white legal moves and calculate future white legal moves
app.whiteMoves.append((whiteMoves[i]//10, whiteMoves[i]%10))
elif app.playerSymbol == "o":
#p2's (white) turn
player_move = app.click
if player_move in app.whiteMoves:
app.turnSwap = True
app.board = move(app.board, app.playerSymbol, (player_move[0]*10) + player_move[1])
score(app)
app.blackMoves.clear()
blackMoves = possibleMoves(app.board, "@")
for i in range(len(blackMoves)):
#clear previous black legal moves and calculate future black legal moves
app.blackMoves.append((blackMoves[i]//10, blackMoves[i]%10))
if app.turnSwap:
#swaps turns
if app.playerSymbol == "@":
app.playerSymbol = "o"
elif app.playerSymbol == "o":
app.playerSymbol = "@"
app.turnSwap = False
createPvPMatrix(app)
elif "." not in app.board:
#if no more spots
score(app)
app.gameOver = True
"""
多单swap-12.09与空单swap6.527取自2019年2月某刻数据
"""
from Strategy import *
from Transactions import *
import pandas as pd
result = pd.DataFrame()
s = []
t = []
for i in range(0, 30):
print(round((i + 1) * 0.1, 1))
s.append(
Strategy(
pd.read_csv('D:/graduate/TheSecondElement/History/XAUUSD60.csv'),
30, 100, -12.09, 6.527, 300000))
s[i].setStrategyAugment((i + 1) * 0.1, 12, 26, 24)
transactions = s[i].getTransactions()
t.append(Transactions(transactions))
t[i].caculateDetails()
t[i].caculateAboutDrawDown()
openTradesHistory = s[i].openTradesHistory
days = t[i].days
drawDown = t[i].drawDown
result.at[i, 'R'] = (i + 1) * 0.1
result.at[i, '年收益'] = t[i].netProfitPerYear
result.at[i, '最大衰落金额'] = drawDown.describe().loc['max']['drawDown']
result.at[i, 'MAR'] = result.at[i, '年收益'] / result.at[i, '最大衰落金额']
result.at[i, '期望'] = t[i].expectedPayOff
result.at[i, '最大衰落期'] = drawDown.describe().loc['max']['drawDownPeriod']
result.at[i, '胜率'] = t[i].profitTradesRate
Tools.sqlScale,
codeList,
csMap=None,
fieldNum=Tools.numScale,
label='scale')
mkt.CreateDataSource(connStr,
Tools.sqlBm, [2060002293],
csMap=None,
fieldNum=Tools.numBm,
label='INDEX')
random.shuffle(scaleStockCodeList[0])
random.shuffle(scaleStockCodeList[1])
random.shuffle(scaleStockCodeList[2])
random.shuffle(scaleStockCodeList[3])
allCode = []
allCode.extend(scaleStockCodeList[0])
allCode.extend(scaleStockCodeList[1])
allCode.extend(scaleStockCodeList[2])
random.shuffle(allCode)
# ssc = allCode
ssc = allCode[0:100]
# ssc = scaleStockCodeList[0]
# ssc = scaleStockCodeList[1][0 : 100]
# ssc = scaleStockCodeList[2][0 : 100]
stg = Strategy.Strategy(codeList, csMap, 200, mkt, ssc)
mkt.AddAfterCloseReceiver(stg.NewDayHandler)
mkt.AddAfterCloseReceiver(stg.da.NewDayHandler)
"""
多单swap-12.09与空单swap6.527取自2019年2月某刻数据
"""
from Strategy import *
from Transactions import *
import pandas as pd
result = pd.DataFrame()
s = []
t = []
for i in range(0, 30):
print(round((i + 1) * 0.1, 1))
s.append(
Strategy(pd.read_csv('F:/小宇宙/R/History/GBPUSD60.csv'), 30, 100000,
-7.44, 3.37, 300000))
s[i].setStrategyAugment((i + 1) * 0.1, 12, 26, 24)
transactions = s[i].getTransactions()
t.append(Transactions(transactions))
t[i].caculateDetails()
t[i].caculateAboutDrawDown()
openTradesHistory = s[i].openTradesHistory
days = t[i].days
drawDown = t[i].drawDown
result.at[i, 'R'] = (i + 1) * 0.1
result.at[i, '年收益'] = t[i].netProfitPerYear
result.at[i, '最大衰落金额'] = drawDown.describe().loc['max']['drawDown']
result.at[i, 'MAR'] = result.at[i, '年收益'] / result.at[i, '最大衰落金额']
result.at[i, '期望'] = t[i].expectedPayOff
result.at[i, '最大衰落期'] = drawDown.describe().loc['max']['drawDownPeriod']
result.at[i, '胜率'] = t[i].profitTradesRate
result.at[i, '盈利月占比'] = len(
import gongcq.Market as Market import gongcq.CodeSymbol as CodeSymbol import Strategy import datetime as dt root = 'E:\\TEMP\\l2_sample_day' connStr = 'reader/[email protected]:1521/dbcenter' sqlPrc = "SELECT TRADE_DATE, STK_UNI_CODE, CLOSE_PRICE, CLOSE_PRICE_RE, RISE_DROP_RANGE_RE / 100, OPEN_PRICE, OPEN_PRICE_RE, STK_TOT_VALUE, TRADE_AMUT, STK_CIR_VALUE, TURNOVER_RATE " \ "FROM UPCENTER.STK_BASIC_PRICE_MID " \ "WHERE ISVALID = 1 AND TRADE_VOL > 0 AND TRADE_DATE = TO_DATE('{TRADE_DATE}', 'YYYY-MM-DD') " sqlCld = "SELECT MIN(C.END_DATE) " \ "FROM UPCENTER.PUB_EXCH_CALE C " \ "WHERE C.IS_TRADE_DATE = 1 AND C.SEC_MAR_PAR = 1 AND " \ " C.END_DATE > TO_DATE('{LAST_DATE}', 'YYYY-MM-DD') " \ "ORDER BY END_DATE" csMap = CodeSymbol.CodeSymbol(connStr) codeList, symbolList, nameList, mktList = CodeSymbol.GetAllCode(connStr) mkt = Market.Market(connStr, sqlCld, dt.datetime(2017, 4, 4)) mkt.CreateDataSource(connStr, sqlPrc, codeList, csMap, 11) mkt.CreateDataSource(root, None, codeList, csMap, 198) mkt.CreateAccount(0, 1000000) stg = Strategy.Strategy(codeList, csMap, 50) mkt.AddAfterCloseReceiver(stg.da.NewDayHandler) mkt.AddAfterCloseReceiver(stg.NewDayHandler)
#!/bin/env python2.6
import json
import time
import calendar
dataFinal = {}
import matplotlib.pyplot as plt
from Strategy import *
stuff = []
stamps = []
with open("filed.json","r") as tsla:
amount = 1000
strat = Strategy(amount)
print "Start with $"+str(amount)
final_val = 0
for i in enumerate(tsla):
try:
data = json.loads(i[1]) #figure out later
except ValueError:
pass
strat.mean_strategy(data)
timeStamp = data["query"]["created"]
timeStamp = calendar.timegm(time.strptime(timeStamp,"%Y-%m-%dT%H:%M:%SZ"))
# print strat.mean_strategy(data)
final_val = data
strat.sellAll(data)
print "Ended with $"+str(strat.printDatMoney())
def __init__(self, strategy, rules):
self.strategy = Strategy(strategy, rules)
name = strategy + " Strategy Player"
Player.__init__(self, name)
def __init__(self):
arr = [random.randint(0, 1) for _ in range(70)]
self.genes = Strategy(arr)
self.fitness = 0
