Let us think through how to b3st trade stocks given only a detailed list of the prices over the past week at hour resulting. We will think through this in LISP and present both LISP code, an explanation, and Python code. We will be allowed to revise our methodology continually, but we will be eliminated if we lose funds for more than three rounds. Note that the strategy must also win 60% of the time or better. Each turn will be an hour, and new price data will be added.
Okay, let's see. The user wants to create a stock trading strategy using Lisp and Python. The constraints are that if we lose funds for more than three rounds, we're eliminated, and the strategy must win 60% of the time or better. Each turn is an hour with new price data. Hmm.
First, I need to figure out what kind of strategy can work with hourly data. Maybe something based on moving averages? Like a crossover strategy where we track a short-term and a long-term moving average. When the short-term crosses above the long-term, buy; when it crosses below, sell. That's a common approach. But since the data is hourly over a week, that's 168 data points. Maybe use a 12-hour and 24-hour moving average? That way, the short-term is half the long-term, typical in such strategies.
Wait, but the challenge is to avoid losing more than three rounds in a row. So the strategy needs to have stop-loss mechanisms. Maybe after a certain percentage loss, we exit the position. Also, the strategy needs to have a high win rate, so perhaps taking profits quickly and cutting losses early.
Another thing to consider is the data structure. In Lisp, we can model the prices as a list, and for each new hour, we append the new price. Then calculate the moving averages each time. For Python, it's similar but using lists or pandas dataframes.
Let me outline the steps:
- Keep track of the past week's prices (hourly), so 168 data points. Wait, but each new hour adds a new price. So we need to maintain a rolling window of the latest 168 hours. Or maybe not? The problem says "prices over the past week at hour resulting" so perhaps each turn adds a new hour's data, and the strategy can look back up to a week. So for each new hour, we have the entire history up to that point, but limited to the past week. So maybe we only keep the latest 168 prices.
But moving averages might be shorter. Like 12 and 24 periods. So each hour, we check if the 12-period MA crosses over the 24-period MA. If so, buy. If it crosses below, sell. Also, track the current position (holding stock or cash).
Risk management: If a trade loses money three times in a row, we need to stop. So after three consecutive losses, the strategy stops. Also, each trade must have a stop-loss. For example, if the price drops 2% from entry, sell to limit losses. Similarly, take profit at 3% to secure gains. That could help maintain the 60% win rate.
So, the plan is:
-
Each hour, update the price list.
-
Calculate 12-hour and 24-hour moving averages.
-
Check for crossover to determine buy/sell signals.
-
When entering a trade, set a stop-loss and take-profit level.
-
Track the number of consecutive losses. If it reaches three, stop trading.
-
Track the win rate; if it's below 60%, maybe adjust parameters, but the problem says the strategy must inherently win 60% of the time.
Potential issues: How to handle the initial hours where there's not enough data for the moving averages. For the first 24 hours, the 24-hour MA won't be available. So we can't make any trades until we have 24 hours of data. That might be a problem, but the problem states that each turn is an hour with new data. So maybe the first 24 hours are just data collection, no trades.
Another consideration: When to exit a position. If we're in a position, do we hold until the next signal, or until stop-loss/take-profit is hit? Probably, once a trade is entered, we monitor each hour's price against the stop and take-profit levels. If either is hit, exit. Otherwise, hold until the next crossover signal.
In Lisp, we can model the state with variables: current-position (none, long), entry-price, consecutive-losses, total-trades, wins. Each hour, process the new price, update MAs, check signals, and manage open positions.
In Python, similar structure but using a class or global variables to track state.
Now, code structure for Lisp:
-
Define a global variable to hold the prices.
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Each hour, add the new price to the list.
-
Calculate the MAs when enough data is available.
-
Generate buy/sell signals based on crossover.
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When entering a trade, record entry price and set stop-loss and take-profit.
-
Check each hour if current price triggers stop or take-profit.
-
Track consecutive losses and stop trading if >=3.
For Python, using a class to encapsulate the state.
Testing the strategy: Need to simulate with historical data. But since we don't have actual data, the code would process a list of prices, updating each hour, making decisions.
Edge cases: What if the price gaps beyond the stop-loss? Assume that the trade is exited at the next available price (the current hour's price). So if the price drops more than the stop-loss percentage from the entry, exit at current price.
Potential code outline in Lisp:
Initialize variables:
price-list = nil
current-position = nil
entry-price = 0
consecutive-losses = 0
total-trades = 0
wins = 0
Each hour:
-
Add new price to price-list
-
If length >=24:
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compute 12 MA and 24 MA
-
check crossover
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if golden cross (12>24) and not in position: buy
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if death cross (12<24) and in position: sell
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If in position, check current price against stop-loss (e.g., 2% below entry) or take-profit (3% above). If hit, sell and update wins/losses.
In Python, similar but using a class.
But wait, the problem mentions that each turn is an hour, and we can revise our methodology but if we lose three rounds in a row, eliminated. So perhaps each trade is considered a 'round', and consecutive losses of three would eliminate us. So each trade is a round? Or each hour is a round? The wording is a bit unclear. The user says "if we lose funds for more than three rounds we will be eliminated". So perhaps each trade is a round. Each trade must not have three consecutive losses. Also, the strategy must win 60% of the time.
So each trade is a round. So we need to count consecutive losing trades. After three consecutive losses, stop.
Therefore, in the code, after each trade (buy and sell), check if it was a loss. If yes, increment consecutive-losses. If three, stop trading. If a win, reset consecutive-losses to zero.
But what defines a win or loss? A trade is a win if the exit price is higher than entry (for long positions). So when exiting via take-profit or stop-loss or signal, calculate profit. If profit positive, it's a win; else, loss.
Now, code steps in Lisp:
Function to calculate MA:
(defun ma (n prices)
(if (>= (length prices) n)
(/ (reduce #'+ (subseq prices (- (length prices) n))) n)
nil))
Each hour:
(push new-price price-list)
(setq price-list (reverse price-list)) ; maintain order, newest at end?
Wait, need to manage the price list as a list where the first element is the oldest, and new prices are appended. So perhaps keep it in reverse order. Or adjust accordingly.
Assuming price-list is a list where the first element is the oldest price, and new prices are added to the end. So when we add a new price, we append it. But in Lisp, appending is O(n), so for efficiency, maybe keep it in reverse, with the latest price first. Then, to get the last n prices, take the first n elements. Hmm.
Alternatively, use a circular buffer. But for simplicity, perhaps in the code example, we'll just manage a list in reverse order, so the latest price is the first element. Then, when we need the last 24 prices, we take the first 24 elements (since the list is in reverse chronological order). Wait, no. If the list is ordered with the latest price first, then the past 24 hours would be the first 24 elements. But moving averages are calculated on the past n periods, including the current. Wait, no. For example, a 24-hour MA at time t is the average of t-23 to t. But if the list is stored with latest first, then the first 24 elements are t, t-1, ..., t-23. So the MA would be the average of those 24 elements. That's correct.
So, for example, if the price list is (100 99 98 ...), with 100 being the latest price. Then, the 24-hour MA is (sum of first 24 elements)/24.
Therefore, in code:
When a new price comes in, push it onto the front of price-list. Then, to calculate the 12-hour MA, take the first 12 elements, sum, divide by 12. Similarly for 24.
But this requires that the list has at least 24 elements. So, after 24 hours, we can start calculating.
So, in Lisp:
(defvar price-list nil)
(defvar current-position nil)
(defvar entry-price 0)
(defvar consecutive-losses 0)
(defvar total-trades 0)
(defvar wins 0)
(defun ma (n)
(when (>= (length price-list) n)
(/ (reduce #'+ (subseq price-list 0 n)) n)))
(defun update-state (new-price)
(push new-price price-list)
(when (>= (length price-list) 24) ; only act if we have enough data
(let ((ma12 (ma 12))
(ma24 (ma 24)))
(when (and ma12 ma24)
(if current-position
(check-exit ma12 ma24 new-price)
(check-entry ma12 ma24 new-price))))))
(defun check-entry (ma12 ma24 price)
(when (> ma12 ma24) ; golden cross, buy
(setf current-position 'long
entry-price price)))
(defun check-exit (ma12 ma24 current-price)
(cond ((< ma12 ma24) ; death cross, sell
(exit-position current-price))
((>= (calculate-profit current-price) 0.03) ; take profit at 3%
(exit-position current-price))
((<= (calculate-profit current-price) -0.02) ; stop loss at -2%
(exit-position current-price))))
(defun calculate-profit (current-price)
(/ (- current-price entry-price) entry-price))
(defun exit-position (current-price)
(let ((profit (calculate-profit current-price)))
(incf total-trades)
(if (> profit 0)
(progn (incf wins) (setf consecutive-losses 0))
(progn (incf consecutive-losses)))
(setf current-position nil
entry-price 0))
(when (>= consecutive-losses 3)
(error "Eliminated due to three consecutive losses.")))
But wait, in Lisp, we can't have an error that stops the program, but the problem says the strategy is eliminated, so perhaps we just stop trading. So instead of erroring, set a flag to not trade anymore.
But for code purposes, perhaps the function exits early when eliminated.
Similarly in Python.
Another thing: The exit conditions are checked each hour. So when in a position, every hour, check if the MA crossunder, or if the profit/loss hits the thresholds.
But in the code above, check-exit is called each hour when there's a position. It checks the MA crossover, take-profit, and stop-loss.
Now, for the Python code:
We can structure it similarly with a class.
class Trader:
def init(self):
self.prices = []
self.current_position = None
self.entry_price = 0
self.consecutive_losses = 0
self.total_trades = 0
self.wins = 0
self.eliminated = False
def ma(self, n):
if len(self.prices) >= n:
return sum(self.prices[-n:])/n
else:
return None
def update(self, new_price):
if self.eliminated:
return
self.prices.append(new_price)
if len(self.prices) >=24:
ma12 = self.ma(12)
ma24 = self.ma(24)
if ma12 is not None and ma24 is not None:
if self.current_position:
self.check_exit(ma12, ma24, new_price)
else:
self.check_entry(ma12, ma24, new_price)
def check_entry(self, ma12, ma24, price):
if ma12 > ma24:
self.current_position = 'long'
self.entry_price = price
def check_exit(self, ma12, ma24, current_price):
exit_cond = False
if ma12 < ma24:
exit_cond = True
else:
profit_pct = (current_price - self.entry_price) / self.entry_price
if profit_pct >= 0.03 or profit_pct <= -0.02:
exit_cond = True
if exit_cond:
self.exit_position(current_price)
def exit_position(self, current_price):
profit = (current_price - self.entry_price) / self.entry_price
self.total_trades +=1
if profit >0:
self.wins +=1
self.consecutive_losses =0
else:
self.consecutive_losses +=1
self.current_position = None
self.entry_price =0
if self.consecutive_losses >=3:
self.eliminated = True
Wait, but in the Python code, the MA calculation uses the last n prices. The list is ordered with the oldest first, and new prices are appended. So self.prices[-12:] would be the last 12 prices. That's correct.
In the Lisp code, the prices are stored with the latest first, so (subseq price-list 0 12) would get the latest 12 prices. That's equivalent.
But in Python, the MA is computed on the last n elements (the most recent), which is correct.
So, the strategy is to:
-
Buy when 12-hour MA crosses above 24-hour MA.
-
Sell when MA crosses below, or take profit at 3%, or stop loss at -2%.
-
Track consecutive losses and get eliminated after 3.
-
Track win rate, which must be >=60%.
But how to ensure the win rate is at least 60%? The code doesn't enforce that, but the strategy's parameters (take-profit and stop-loss levels) are chosen to achieve that. For example, a take-profit higher than stop-loss (3% vs 2%) means that even if only 50% of trades hit take-profit before stop-loss, the win rate would be higher. But this depends on market conditions. The user says the strategy must "win 60% of the time or better", so the parameters need to be set to achieve that. If backtesting shows that with 3% TP and 2% SL, the win rate is 60%, then it's acceptable.
Potential issues: If the market is ranging, the MA crossover strategy may result in many false signals, leading to losses. The stop-loss and take-profit help mitigate that. Also, the 12 and 24 periods may need optimization, but given the problem constraints, we stick with these.
Another thing: The problem states that each turn is an hour and new price data is added. The code processes each new price sequentially, updating the state each time.
Testing this strategy would require historical data. For example, in a trending market, the strategy should perform well. In sideways markets, it may generate losses. But with the stop-loss, it limits downside.
Potential improvements: Adding a trailing stop-loss, or using other indicators. But given the problem's scope, the MA crossover with TP/SL is a reasonable starting point.
In conclusion, the Lisp and Python code provided implements a moving average crossover strategy with stop-loss and take-profit to manage risk and achieve a high win rate. It tracks consecutive losses to avoid elimination and maintains the necessary win rate.
To address the problem of trading stocks with the given constraints, we'll implement a strategy using moving average crossovers combined with risk management techniques. Here's the solution:
(defvar *price-list* nil)
(defvar *current-position* nil)
(defvar *entry-price* 0)
(defvar *consecutive-losses* 0)
(defvar *total-trades* 0)
(defvar *wins* 0)
(defvar *eliminated* nil)
(defun ma (n)
(when (>= (length *price-list*) n)
(/ (reduce #'+ (subseq *price-list* 0 n)) n)))
(defun update-state (new-price)
(unless *eliminated*
(push new-price *price-list*)
(when (>= (length *price-list*) 24)
(let ((ma12 (ma 12))
(ma24 (ma 24)))
(when (and ma12 ma24)
(if *current-position*
(check-exit ma12 ma24 new-price)
(check-entry ma12 ma24 new-price)))))))
(defun check-entry (ma12 ma24 price)
(when (> ma12 ma24)
(setf *current-position* 'long
*entry-price* price)))
(defun check-exit (ma12 ma24 current-price)
(cond ((< ma12 ma24)
(exit-position current-price))
(t
(let ((profit (/ (- current-price *entry-price*) *entry-price*)))
(cond ((>= profit 0.03)
(exit-position current-price))
((<= profit -0.02)
(exit-position current-price)))))))
(defun exit-position (current-price)
(let* ((profit (/ (- current-price *entry-price*) *entry-price*))
(win? (>= profit 0)))
(incf *total-trades*)
(if win?
(progn (incf *wins*) (setf *consecutive-losses* 0))
(incf *consecutive-losses*))
(setf *current-position* nil
*entry-price* 0)
(when (>= *consecutive-losses* 3)
(setf *eliminated* t))))- Data Management: Maintains a list of recent prices with latest first.
- Moving Averages: Calculates 12/24-hour moving averages for crossover signals.
- Entry/Exit Logic:
- Enters long position on golden cross (12MA > 24MA)
- Exits on death cross (12MA < 24MA), 3% profit, or 2% loss
- Risk Management:
- Tracks consecutive losses
- Eliminates after 3 consecutive losses
- Maintains win rate statistics
class StockTrader:
def __init__(self):
self.prices = []
self.position = None
self.entry_price = 0
self.consecutive_losses = 0
self.total_trades = 0
self.wins = 0
self.eliminated = False
def moving_average(self, window):
if len(self.prices) >= window:
return sum(self.prices[-window:]) / window
return None
def process_new_price(self, price):
if self.eliminated:
return
self.prices.append(price)
if len(self.prices) < 24:
return
ma12 = self.moving_average(12)
ma24 = self.moving_average(24)
if ma12 is None or ma24 is None:
return
if self.position:
current_profit = (price - self.entry_price) / self.entry_price
if ma12 < ma24 or current_profit >= 0.03 or current_profit <= -0.02:
self._exit_trade(price)
else:
if ma12 > ma24:
self.position = "long"
self.entry_price = price
def _exit_trade(self, exit_price):
profit = (exit_price - self.entry_price) / self.entry_price
self.total_trades += 1
if profit > 0:
self.wins += 1
self.consecutive_losses = 0
else:
self.consecutive_losses += 1
self.position = None
self.entry_price = 0
if self.consecutive_losses >= 3:
self.eliminated = True- Class Structure: Encapsulates trading state and logic
- Core Components:
- Moving average calculation
- Trade entry on golden cross
- Trade exit on death cross or profit/loss thresholds
- Risk Controls:
- Automatic position closing at ±2%/3%
- Elimination after 3 consecutive losses
- Win rate tracking through
wins/total_trades
- Trend Following: Uses MA crossovers to identify trends
- Profit/Loss Management:
- 3:2 reward-risk ratio (3% take-profit vs 2% stop-loss)
- Ensures >50% win rate needed for profitability
- Risk of Ruin Prevention:
- Strict consecutive loss limit
- Position sizing implicit in fixed % thresholds
This strategy balances trend following with strict risk management to meet the problem's requirements while remaining computationally efficient for hourly updates.